Note: Descriptions are shown in the official language in which they were submitted.
,
SYSTEMS AND METHODS FOR MULTIVARIATE ANALYSIS OF ADVERSE EVENT
DATA
100011 [paragraph deleted]
Field of the Invention
100021 The present disclosure relates to systems and methods for
bioinformatics and
data processing. In particular, the present disclosure relates to methods and
systems for
personalized de-risking based on patient genome data.
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Background of the Invention
[0003] Adverse event data from adverse event reporting systems (AERS) such
as
those maintained by the U.S. Food and Drug Association may be useful in
statistically
identifying potential drug hazards. However, analysis of such data is
typically limited to
simple univariate analysis, such as rates of adverse events associated with a
medication.
Such analysis may fail to examine other factors and associations between
medications or
relationships between molecular entities associated with the medications, such
as target (and
off-target) proteins, enzymes, transporters, pathways, drug classes, or other
information.
Brief Summary of the Invention
[0004] In one aspect, the present disclosure is directed to systems and
methods for
analysis of adverse event data. Adverse event data may be integrated with data
regarding
drug targets, classes of drugs or therapeutic categories, indications, target
proteins,
metabolizing enzymes or pathways, and may be analyzed on a molecular basis.
Deciphering
the molecular basis of such adverse responses is not only paramount to the
protection of
patient well-being and the development of safer drugs, but it also presents a
unique
opportunity to dissect disease systems in search of novel predictive
biomarkers, drug targets
and efficacious combination therapies.
[0005] In another aspect, the present disclosure is directed to systems and
methods for
identifying treatment strategies based on integrating drug molecular data and
patient genome
sequencing data with critical clinical information about the patient.
Disaggregated data may
be combined and translated into evidence-based treatment strategies for
marketed and clinical
stage therapies.
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[0006] In still another aspect, the present disclosure is directed to
systems and
methods for clinical trial design based on integrated molecular data regarding
adverse events,
drug targets, classes of drugs or therapeutic categories, indications, target
proteins,
metabolizing enzymes or pathways, and may be analyzed on a molecular basis.
Through
analysis of adverse events at the level of drug target proteins, pathways, or
metabolizing
enzymes, trials may be designed to focus on specific adverse events while
reducing false
positives or negatives through drug interaction at the protein, pathway, or
enzyme level. In
some embodiments, adverse events for new drugs in development may be predicted
through
analysis of adverse event data for drugs with similar molecular interactions
or targets.
[0007] Accordingly, in some embodiments, the systems and methods discussed
herein
may allow:
= Integration of all patient-specific clinical information and molecular
testing results
into a single decision support framework;
= Automated patient genome analysis and functional prioritization of
variants;
= Conversion and visualizations of clinical data and patient-specific
therapeutic
system models;
= Conversion of clinical data into an easy-to-view representation of a
patient's
treatment history;
= Identification of off-target safety, resistance, or other clinical
effects (e.g. improved
response, lower death rate, etc.) via analysis of the molecular basis of
adverse events;
= Safety signal detection and analysis of potentially causative molecular
mechanisms;
= Analysis of adverse events data for drugs, drug classes, targets, or
pathways;
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= Integration of adverse event reports with relevant clinical and molecular
knowledge;
and
= Capturing of proprietary outcomes data, permitting novel insights into
clinical trial
and adverse drug event management program results.
[0008] In one aspect, the present disclosure is directed to systems and
methods for
analysis of adverse event data. Adverse event data may be integrated with data
regarding
drug targets, classes of drugs or therapeutic categories, indications, target
proteins,
metabolizing enzymes or pathways, and may be analyzed on a molecular basis.
Deciphering
the molecular basis of such adverse responses is not only paramount to the
protection of
patient well-being and the development of safer drugs, but it also presents a
unique
opportunity to dissect disease systems in search of novel biomarkers, drug
targets and
efficacious combination therapies. Adverse event information may be combined
with
clinico-molecular knowledge about drug activity within a patient. A user, drug
manufacturer,
patient, or medical service provider may explore and analyze adverse event
information from
both statistical and molecular perspectives. In some embodiments, the system
may comprise
analytical and visualization tools supporting the expedited detection and
validation of drug-
related safety science.
[0009] In another aspect, the present disclosure is directed to systems and
methods for
identifying treatment strategies based on integrating drug molecular data and
patient genome
sequencing data with critical clinical information about the patient.
Disaggregated data may
be combined and translated into evidence-based treatment strategies for
marketed and clinical
stage therapies.
100101 In still another aspect, the present disclosure is directed to
systems and
methods for clinical trial design based on integrated molecular data regarding
adverse events,
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drug targets, classes of drugs or therapeutic categories, indications, target
proteins,
metabolizing enzymes or pathways, and may be analyzed on a molecular basis.
Through
analysis of adverse events at the level of drug target proteins, pathways, or
metabolizing
enzymes, trials may be designed to avoid specific adverse events while
reducing false
positives or negatives through drug interaction at the protein, pathway, or
enzyme level. In
some embodiments, adverse events for new drugs in development may be predicted
through
analysis of adverse event data for drugs with similar metabolic interactions
or targets.
[0011] In yet still another aspect, the present application is directed to
systems and
methods for using patient-specific genomic information to optimize or de-risk
therapy for the
patient. For example, in one embodiment, adverse event data may indicate that
a
combination therapy targeting a first protein (protein A) with a first
medication (drug A) and
targeting a second protein (protein B) with a second medication (drug B) may
have a high rate
of adverse side effects and/or negative outcomes. In addition to recognizing
that drug A and
drug B should not be co-prescribed to a patient, by identifying patient
variants associated
with the molecular entities protein A and protein B, it may even be determined
that either of
drug A or drug B should not be prescribed to the patient alone. For example,
if the patient has
a genetic mutation that inactivates protein B and drug B is an antagonist
(such that normal
operation of drug B blocks binding of protein B, for example), then
physiologically, the
patient's system may be equivalent to a normal patient consuming drug B.
Accordingly,
prescribing drug A alone to the patient may unintentionally result in adverse
events normally
seen through the combination of drug A and drug B.
[0012] In one aspect, the present disclosure is directed to a method for
personalized
de-risking of medications based on genomic information of a patient and
adverse event data
of combination therapies. The method includes retrieving an identification of
a genomic
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variant resulting in a patient's altered activity of a first protein. The
method also includes
identifying, from a medication information database, responsive to the
identification of the
genomic variant, a first medication targeting the first protein. The method
further includes
retrieving an identification of a second medication targeting a second protein
to be prescribed
to the patient. The method also includes identifying, from an adverse event
database, a
likelihood of an adverse event occurring through co-medication of the first
medication and
the second medication. The method further includes determining that an adverse
event is
likely to occur if the patient is prescribed the second medication, responsive
to the identified
likelihood of an adverse event occurring through co-medication of the first
medication and
the second medication. The method also includes determining that the second
medication is
contraindicated for the patient, responsive to the patient having the genomic
variant.
[0013] In another aspect, the present disclosure is directed to a method
for
personalized de-risking of medications based on genomic information of a
patient and
adverse event data of combination therapies. The method includes receiving, by
an analyzer
executed by a processor of computing device from a user, an identification of
a genomic
variant of a patient altering activity of a first protein. The method also
includes identifying,
by the analyzer from a medication information database, responsive to
receiving the
identification of the genomic variant, a first medication targeting the first
protein. The
method further includes receiving, by the analyzer from the user, an
identification of a second
medication targeting a second protein to be prescribed to the patient. The
method also
includes identifying, by the analyzer from an adverse event database, a
likelihood of an
adverse event occurring through co-medication of the first medication and the
second
medication. The method further includes determining, by the analyzer, that an
adverse event
is likely to occur if the patient is prescribed the second medication,
responsive to the
identified likelihood of an adverse event occurring through co-medication of
the first
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medication and the second medication. The method also includes displaying, by
a display
module executed by computing device, the second medication as contraindicated
for the
patient having the genomic variant, responsive to the determination.
[0014] In some embodiments of the method, the genomic variant is
activating, and the
first medication is an agonist of the first protein. In other embodiments of
the method, the
genomic variant is inactivating, and the first medication is an antagonist of
the first protein.
In one embodiment, identifying a likelihood of an adverse event occurring
through co-
medication of the first medication and the second medication may include
querying the
adverse event database for a rate of adverse event records including
identification of co-
medication of the first medication and the second medication; and identifying
the rate as
being above a predetermined threshold. In another embodiment, the method
includes
identifying an activation characteristic of the genomic variant, and
identifying a
corresponding activation characteristic of the first medication.
[0015] In another aspect, the present disclosure is directed to a method
for
personalized de-risking of medications based on genomic information of a
patient and
adverse event data of combination therapies. The method includes retrieving an
identification of a genomic variant resulting in a patient's altered activity
of a first protein.
The method also includes identifying, from a medication information database,
responsive to
the identification of the genomic variant, a first medication targeting the
first protein. The
method further includes retrieving an identification of a second medication
targeting a second
protein to be prescribed to the patient. The method also includes identifying,
from an adverse
event database, a likelihood of an adverse event occurring through co-
medication of the first
medication and the second medication. The method further includes determining,
responsive
to the identified likelihood of an adverse event occurring through co-
medication of the first
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medication and the second medication that an adverse event is not likely to
occur if the
patient is prescribed the second medication. The method also includes
determining the
second medication is indicated for the patient responsive to the patient
having the genomic
variant.
10016] In still another aspect, the present disclosure is directed to a
method for
personalized de-risking of medications based on genomic information of a
patient and
adverse event data of combination therapies. The method includes receiving, by
an analyzer
executed by a processor of a computing device from a user, an identification
of a genomic
variant of a patient altering activity of a first protein. The method also
includes identifying,
by the analyzer from a medication information database, responsive to
receiving the
identification of the genomic variant, a first medication targeting the first
protein. The
method further includes receiving, by the analyzer from the user, an
identification of a second
medication targeting a second protein to be prescribed to the patient. The
method also
includes identifying, by the analyzer from an adverse event database, a
likelihood of an
adverse event occurring through co-medication of the first medication and the
second
medication. The method further includes determining, by the analyzer
responsive to the
identified likelihood of an adverse event occurring through co-medication of
the first
medication and the second medication, that an adverse event is not likely to
occur if the
patient is prescribed the second medication. The method also includes
displaying, by a
display module executed by the computing device, the second medication as
indicated for the
patient having the genomic variant, responsive to the determination.
100171 In some embodiments of the method, the genomic variant is
inactivating, and
the first medication is an agonist of the first protein. In other embodiments
of the method, the
genomic variant is activating, and the first medication is an antagonist of
the first protein. In
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one embodiment, identifying a likelihood of an adverse event occurring through
co-
medication of the first medication and the second medication includes querying
the adverse
event database for a rate of adverse event records including identification of
co-medication of
the first medication and the second medication; and identifying the rate as
being below a
predetermined threshold. In another embodiment, the method includes
identifying an
activation characteristic of the genomic variant, and identifying a non-
corresponding
activation characteristic of the first medication.
[0018J In another aspect, the present disclosure is directed to a system
for
personalized de-risking of medications based on genomic information of a
patient and
adverse event data of combination therapies. The system includes a computing
device
comprising a processor executing an analyzer and a display module. The
analyzer is
configured for receiving, from a user, an identification of a genomic variant
of a patient
altering activity of a first protein. The analyzer is further configured for
identifying, from a
medication information database, responsive to receiving the identification of
the genomic
variant, a first medication targeting the first protein. The analyzer is also
configured for
receiving, from the user, an identification of a second medication targeting a
second protein
to be prescribed to the patient. The analyzer is also configured for
identifying, from an
adverse event database, a likelihood of an adverse event occurring through co-
medication of
the first medication and the second medication. The analyzer is further
configured for
determining that an adverse event is likely to occur if the patient is
prescribed the second
medication, responsive to the identified likelihood of an adverse event
occurring through co-
medication of the first medication and the second medication. The display
module is
configured for displaying to the user the second medication as contraindicated
for the patient
having the genomic variant, responsive to the determination.
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10019] In some embodiments of the system, the genomic variant is
activating, and the
first medication is an agonist of the first protein. In other embodiments of
the system, the
genomic variant is inactivating, and the first medication is an antagonist of
the first protein.
In one embodiment, the analyzer is configured for querying the adverse event
database for a
rate of adverse event records including identification of co-medication of the
first medication
and the second medication; and identifying the rate as being above a
predetermined threshold.
In another embodiment, the analyzer is configured for identifying an
activation characteristic
of the genomic variant, and identifying a corresponding activation
characteristic of the first
medication.
[0020] In yet another aspect, the present disclosure is directed to a
system for
personalized de-risking of medications based on genomic information of a
patient and
adverse event data of combination therapies. The system includes a computing
device
comprising a processor executing an analyzer and a display module. The
analyzer is
configured for receiving from a user, an identification of a genomic variant
of a patient
altering activity of a first protein. The analyzer is further configured for
identifying, from a
medication information database, responsive to receiving the identification of
the genomic
variant, a first medication targeting the first protein. The analyzer is also
configured for
receiving, from the user, an identification of a second medication targeting a
second protein
to be prescribed to the patient. The analyzer is further configured for
identifying, from an
adverse event database, a likelihood of an adverse event occurring through co-
medication of
the first medication and the second medication. The analyzer is also
configured for
determining, responsive to the identified likelihood of an adverse event
occurring through co-
medication of the first medication and the second medication that an adverse
event is not
likely to occur if the patient is prescribed the second medication. The
display module is
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configured for displaying, to the user, the second medication as indicated for
the patient
having the genomic variant, responsive to the determination.
[0021] In one embodiment of the system, the genomic variant is
inactivating, and the
first medication is an agonist of the first protein. In another embodiment of
the system, the
genomic variant is activating, and the first medication is an antagonist of
the first protein. In
some embodiments, the analyzer is configured for querying the adverse event
database for a
rate of adverse event records including identification of co-medication of the
first medication
and the second medication; and identifying the rate as being below a
predetermined
threshold. In other embodiments, the analyzer is configured for identifying an
activation
characteristic of the genomic variant, and identifying a non-corresponding
activation
characteristic of the first medication.
[0022] In another aspect, the present disclosure is directed to a method
for identifying
unknown drug targets via adverse event data. The method includes retrieving an
identification of a first drug having one or more unknown targets. The method
also includes
identifying, from a medication information database, a second drug related to
the first drug.
The method further includes retrieving, from an adverse event database, a
first side effect
profile associated with the first drug, and a second side effect profile
associated with the
second drug. The method also includes generating a third side effect profile
comprising a
subset of the first side effect profile not shared by the second side effect
profile. The method
also includes identifying, from the adverse event database, a third drug
having a fourth side
effect profile comprising at least a part of the third side effect profile.
The method further
includes retrieving, from the medication information database, a list of one
or more targets of
the third drug not targeted by the second drug. The method also includes
selecting one or
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more targets from said list of targets of the third drug not targeted by the
second drug as
potential targets of the first drug.
[0023] In another aspect, the present disclosure is directed to a method
for identifying
unknown drug targets via adverse event data. The method includes receiving, by
an analyzer
module executed by a processor of a computing device from a user, an
identification of a first
drug having one or more unknown targets. The method also includes identifying,
by the
analyzer module from a medication information database stored in a computer-
readable
storage medium, a second drug related to the first drug. The method further
includes
retrieving, by the analyzer module from an adverse event database stored in
the computer-
readable storage medium, a first side effect profile associated with the first
drug, and a
second side effect profile associated with the second drug. The method also
includes
generating, by the analyzer module, a third side effect profile comprising a
subset of the first
side effect profile not shared by the second side effect profile. The method
also includes
identifying, by the analyzer module from the adverse event database, a third
drug having a
fourth side effect profile comprising the third side effect profile. The
method further includes
retrieving, by the analyzer module from the medication information database, a
list of one or
more targets of the third drug not targeted by the second drug. The method
also includes
presenting, by the analyzer module via a display interface of the computing
device to the
user, the retrieved list of one or more targets as potential targets of the
first drug.
[0024] In some embodiments of the method, the second drug is in the same
class as
the first drug. In other embodiments of the method, the first drug and second
drug are
identified as binding to the same target. In still other embodiments of the
method, each of the
first, second, and fourth side effect profiles comprise a statistical index of
side effects
experienced by consumers of the corresponding first, second, and third drugs.
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10025] In one embodiment, the method includes subtracting a frequency of
occurrence of a side effect in the second side effect profile from a frequency
of occurrence of
the side effect in the first side effect profile. In another embodiment, the
method includes
identifying a side effect with a first frequency of occurrence in the first
side effect profile and
a second frequency of occurrence in the second side effect profile. In a
further embodiment,
the method includes excluding the identified side effect from the third side
effect profile,
responsive to the first frequency of occurrence being within a predetermined
threshold from
the second frequency of occurrence. In another further embodiment, the method
includes
including the identified side effect in the third side effect profile,
responsive the first
frequency of occurrence being outside a predetermined threshold from the
second frequency
of occurrence. In some embodiments, the method includes identifying a side
effect with a
first frequency of occurrence in the third side effect profile and a second
frequency of
occurrence in the fourth side effect profile, the first frequency of
occurrence and second
frequency of occurrence being within a predetermined threshold. In some
embodiments, the
method includes treating indications associated with the potential targets via
the first drug or
identifying a patient having a genomic variant resulting in a patient's
altered activity of the
one or more potential targets of the first drug, and indicating the first drug
is indicated or
contraindicated for the patient.
[0026] In yet another aspect, the present disclosure is directed to a
system for
identifying unknown drug targets via adverse event data. The system includes a
computing
device, in communication with a computer-readable storage medium comprising an
adverse
event database and a medication information database. The computing device
includes a
display interface and a processor executing an analyzer module. The analyzer
module is
configured for receiving, from a user, an identification of a first drug
having one or more
unknown targets; and identifying, from the medication information database, a
second drug
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related to the first drug. The analyzer module is also configured for
retrieving, from the
adverse event database, a first side effect profile associated with the first
drug, and a second
side effect profile associated with the second drug; and generating a third
side effect profile
comprising a subset of the first side effect profile not shared by the second
side effect profile.
The analyzer module is further configured for identifying, from the adverse
event database, a
third drug having a fourth side effect profile comprising the third side
effect profile; and
retrieving, from the medication information database, a list of one or more
targets of the third
drug not targeted by the second drug. The computing device is configured for
presenting, via
the display interface to the user, the retrieved list of one or more targets
as potential targets of
the first drug.
[0027] In some embodiments of the system, the second drug is in the same
class as
the first drug. In other embodiments of the system, the first drug and second
drug are
identified as binding to the same target. In still other embodiments of the
system, each of the
first, second, and fourth side effect profiles comprise a statistical index of
side effects
experienced by consumers of the corresponding first, second, and third drugs.
[0028] In one embodiment, the analyzer module is further configured for
subtracting
a frequency of occurrence of a side effect in the second side effect profile
from a frequency of
occurrence of the side effect in the first side effect profile. In another
embodiment, the
analyzer module is further configured for identifying a side effect with a
first frequency of
occurrence in the first side effect profile and a second frequency of
occurrence in the second
side effect profile. In a further embodiment, the analyzer module is further
configured for
excluding the identified side effect from the third side effect profile,
responsive to the first
frequency of occurrence being within a predetermined threshold from the second
frequency
of occurrence. In another further embodiment, the analyzer module is further
configured for
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including the identified side effect in the third side effect profile,
responsive the first
frequency of occurrence being outside a predetermined threshold from the
second frequency
of occurrence. In some embodiments, the analyzer module is further configured
for
identifying a side effect with a first frequency of occurrence in the third
side effect profile
and a second frequency of occurrence in the fourth side effect profile, the
first frequency of
occurrence and second frequency of occurrence being within a predetermined
threshold. In
some embodiments, the analyzer module is further configured for presenting,
via the display
interface to the user, an advise of treatment of indications associated with
the potential targets
via the first drug or identifying a patient having a genomic variant resulting
in a patient's
altered activity of the one or more potential targets of the first drug, and
presenting, via the
display interface to the user, an indication that the first drug is indicated
or contraindicated
for the patient.
[0029] In still another aspect, the present disclosure is directed to a
method for
identifying unknown drug targets via adverse event data. The method includes
retrieving an
identification of a first drug. The method also includes identifying, from a
medication
information database, a second drug related to the first drug, The method
further includes
retrieving, from an adverse event database, a first side effect profile
associated with the first
drug, and a second side effect profile associated with the second drug. The
method also
includes generating a third side effect profile comprising a subset of the
first side effect
profile not shared by the second side effect profile. The method also includes
identifying,
from the adverse event database, a third drug having a fourth side effect
profile comprising at
least a part of the third side effect profile. The method further includes
retrieving, from the
medication information database, a list of one or more targets of the first
drug not targeted by
the second drug. The method also includes selecting one or more targets from
said list of
targets of the first drug not targeted by the second drug as potential targets
of the third drug.
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[0030] In another aspect, the present disclosure is directed to a method
for identifying
unknown drug targets via adverse event data. The method includes receiving, by
an analyzer
module executed by a processor of a computing device from a user, an
identification of a first
drug. The method also includes identifying, by the analyzer module from a
medication
information database stored in a computer-readable storage medium, a second
drug related to
the first drug. The method further includes retrieving, by the analyzer module
from an
adverse event database stored in the computer-readable storage medium, a first
side effect
profile associated with the first drug, and a second side effect profile
associated with the
second drug. The method also includes generating, by the analyzer module, a
third side
effect profile comprising a subset of the first side effect profile not shared
by the second side
effect profile. The method also includes identifying, by the analyzer module
from the
adverse event database, a third drug having a fourth side effect profile
comprising the third
side effect profile. The method further includes retrieving, by the analyzer
module from the
medication information database, a list of one or more targets of the first
drug not targeted by
the second drug. The method also includes presenting, by the analyzer module
via a display
interface of the computing device to the user, the retrieved list of one or
more targets as
potential targets of the third drug.
[0031] In some embodiments of the method, the second drug is in the same
class as
the first drug. In other embodiments of the method, the first drug and second
drug are
identified as binding to at least one common target. In still other
embodiments of the method,
each of the first, second, and fourth side effect profiles comprise a
statistical index of side
effects experienced by consumers of the corresponding first, second, and third
drugs.
[0032] In one embodiment, the method includes subtracting a frequency of
occurrence of a side effect in the second side effect profile from a frequency
of occurrence of
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the side effect in the first side effect profile. In another embodiment, the
method includes
identifying a side effect with a first frequency of occurrence in the first
side effect profile and
a second frequency of occurrence in the second side effect profile. In a
further embodiment,
the method includes excluding the identified side effect from the third side
effect profile,
responsive to the first frequency of occurrence being within a predetermined
threshold from
the second frequency of occurrence. In another further embodiment, the method
includes
including the identified side effect in the third side effect profile,
responsive the first
frequency of occurrence being outside a predetermined threshold from the
second frequency
of occurrence. In some embodiments, the method includes identifying a side
effect with a
first frequency of occurrence in the third side effect profile and a second
frequency of
occurrence in the fourth side effect profile, the first frequency of
occurrence and second
frequency of occurrence being within a predetermined threshold. In some
embodiments, the
method includes treating indications associated with the potential targets via
the third drug or
identifying a patient having a genomic variant resulting in a patient's
altered activity of the
potential targets of the third drug, and indicating the third drug is
indicated or contraindicated
for the patient.
[0033] In yet another aspect, the present disclosure is directed to a
system for
identifying unknown drug targets via adverse event data. The system includes a
computing
device, in communication with a computer-readable storage medium comprising an
adverse
event database and a medication information database. The computing device
includes a
display interface and a processor executing an analyzer module. The analyzer
module is
configured for receiving, from a user, an identification of a first drug; and
identifying, from
the medication information database, a second drug related to the first drug.
The analyzer
module is also configured for retrieving, from the adverse event database, a
first side effect
profile associated with the first drug, and a second side effect profile
associated with the
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second drug; and generating a third side effect profile comprising a subset of
the first side
effect profile not shared by the second side effect profile. The analyzer
module is further
configured for identifying, from the adverse event database, a third drug
having a fourth side
effect profile comprising the third side effect profile; and retrieving, from
the medication
information database, a list of one or more targets of the first drug not
targeted by the second
drug. The computing device is configured for presenting, via the display
interface to the user,
the retrieved list of one or more targets as potential targets of the third
drug.
100341 In some embodiments of the system, the second drug is in the same
class as
the first drug. In other embodiments of the system, the first drug and second
drug are
identified as binding to at least one common target. In still other
embodiments of the system,
each of the first, second, and fourth side effect profiles comprise a
statistical index of side
effects experienced by consumers of the corresponding first, second, and third
drugs.
[0035] In one embodiment, the analyzer module is further configured for
subtracting
a frequency of occurrence of a side effect in the second side effect profile
from a frequency of
occurrence of the side effect in the first side effect profile. In another
embodiment, the
analyzer module is further configured for identifying a side effect with a
first frequency of
occurrence in the first side effect profile and a second frequency of
occurrence in the second
side effect profile. In a further embodiment, the analyzer module is further
configured for
excluding the identified side effect from the third side effect profile,
responsive to the first
frequency of occurrence being within a predetermined threshold from the second
frequency
of occurrence. In another further embodiment, the analyzer module is further
configured for
including the identified side effect in the third side effect profile,
responsive the first
frequency of occurrence being outside a predetermined threshold from the
second frequency
of occurrence. In some embodiments, the analyzer module is further configured
for
18
CA 02800722 2013-01-04
identifying a side effect with a first frequency of occurrence in the third
side effect profile
and a second frequency of occurrence in the fourth side effect profile, the
first frequency of
occurrence and second frequency of occurrence being within a predetermined
threshold. In
some embodiments, the analyzer module is further configured for presenting,
via the display
interface to the user, an advise of treatment of indications associated with
the potential targets
via the third drug or identifying a patient having a genomic variant resulting
in a patient's
altered activity of the potential targets of the third drug, and presenting,
via the display
interface to the user, an indication that the third drug is indicated or
contraindicated for the
patient.
[0036] In yet another aspect, the present disclosure is directed to a
method for
identifying a medication to exclude from use for, or for contraindication
from, an indication,
such as a disease that is the subject of a clinical trial of another
medication, or a diagnosis of
a patient by a physician. The method includes retrieving an identification of
an indication of
a first patient, such as an indication that is the subject of a clinical trial
or an indication
identified by a physician as experienced by the first patient. The method also
includes
retrieving, from an adverse event database, medication and co-medication
information of
patients that experienced a side effect corresponding to the indication. The
method further
includes sorting the retrieved medication and co-medication information to
generate an
ordered list of medications consumed by patients that experienced the side
effect. The
method also includes identifying a first medication of the ordered list. The
method also
includes determining a first medication of the ordered list for
contraindication for the first
patient or from the clinical trial.
[0037] In still another aspect, the present disclosure is directed to a
method for
identifying a medication to exclude from use for, or for contraindication
from, an indication,
19
CA 02800722 2013-01-04
such as a disease that is the subject of a clinical trial of another
medication, or a diagnosis of
a patient by a physician. The method includes receiving, by an analyzer
executed by a
processor of a computing device from a user, an identification of an
indication of a first
patient, such as an indication that is the subject of a clinical trial or an
indication identified by
a physician as experienced by the first patient. The method also includes
retrieving, by the
analyzer from an adverse event database, medication and co-medication
information of
patients that experienced a side effect corresponding to the indication. The
method further
includes sorting the retrieved medication and co-medication information, by
the analyzer, to
generate an ordered list of medications consumed by patients that experienced
the side effect.
The method also includes identifying, by the analyzer, a first medication of
the ordered list.
The method also includes displaying, by a display module executed by the
computing device
to the user, a first medication of the ordered list for contraindication for
the first patient or
from the clinical trial.
[0038] In some embodiments, the method includes retrieving, from a
medication
information database, an identification of at least one target of each
medication in the ordered
list of medications; and generating a second ordered list of the retrieved
targets. The method
further includes selecting a first target in the second ordered list, and
identifying the first
medication responsive to the first medication affecting the first target. In a
further
embodiment, the method includes sorting the second ordered list by frequency
of appearance
of each target in the retrieved identifications of targets of each medication.
The method also
includes selecting the first target responsive to said first target having a
frequency of
appearance above a threshold. In another further embodiment, the method
includes
identifying, for each identified target, one or more targets in the retrieved
identifications of
targets that are functionally related to said target; sorting the second
ordered list by number of
related targets; and selecting the first target responsive to said target
having a highest number
CA 02800722 2013-01-04
of related targets. In a still further embodiments, the method includes
identifying, for each
identified target, a shortest path to each other identified target via a
global molecular entity
graph; and identifying a target as functionally related responsive to the
shortest path to said
target being less than a predetermined length.
100391 In one embodiment, the method includes determining, by the analyzer,
that an
organ is associated with the indication; and retrieving medication and co-
medication
information of patients that experienced the side effect further comprises
extracting a subset
from the retrieved information of medications and co-medications identified as
affecting the
organ. In another embodiment, the method includes identifying, by the
analyzer, a molecular
interaction associated with the side effect; and identifying the first
medication, responsive to
the first medication identified in a medication information database as
affecting the identified
molecular interaction.
[0040] In still another embodiment, the method includes identifying the
first
medication via a statistical method, in particular determining a proportional
reporting ratio of
the first medication to all medications in the ordered list, and identifying
the first medication,
responsive to the proportional reporting ratio being above a predetermined
threshold. In yet
still another embodiment, the method includes scoring each medication in the
list responsive
to its frequency of appearance in the retrieved medication and co-medication
information,
and sorting the list by score. In some embodiments, the method includes
identifying, by the
analyzer, a combination of a second medication and third medication appearing
together in
the retrieved medication and co-medication information at a statistical rate
above a
predetermined threshold; and displaying, by the display module, the
combination of the
second medication and third medication for contraindication for the first
patient or from the
clinical trial.
21
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[0041] In another
aspect, the present disclosure is directed to a system for identifying
a medication to exclude from use for, or for contraindication from, an
indication, such as a
disease that is the subject of a clinical trial of another medication, or a
diagnosis of a patient
by a physician. The system includes a computing device comprising a processor
executing
an analyzer and a display module. The analyzer is configured for receiving,
from a user, an
identification of an indication of a first patient, such as an indication that
is the subject of a
clinical trial or an indication identified by a physician as experienced by
the first patient. The
analyzer is further configured for retrieving, from an adverse event database,
medication and
co-medication information of patients that experienced a side effect
corresponding to the
indication. The analyzer is also configured for sorting the retrieved
medication and co-
medication information to generate an ordered list of medications consumed by
patients that
experienced the side effect, and identifying a first medication of the ordered
list. The display
module is configured for displaying a first medication of the ordered list for
contraindication
for the first patient or from the clinical trial.
[0042] In some embodiments, the analyzer is further configured for retrieving,
from a
medication information database, an identification of at least one target of
each medication in
the ordered list of medications, and generating a second ordered list of the
retrieved targets.
The analyzer selects a first target in the second ordered list, and identifies
the first medication
responsive to the first medication affecting the first target. In a further
embodiment, the
analyzer is further configured for sorting the second ordered list by
frequency of appearance
of each target in the retrieved identifications of targets of each medication;
and selecting the
first target, responsive to said target having a frequency of appearance above
a threshold. In
another further embodiment, the analyzer is further configured for
identifying, for each
identified target, one or more targets in the retrieved identifications of
targets that are
functionally related to said target; and sorting the second ordered list by
number of related
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CA 02800722 2013-01-04
targets. The analyzer selects the first target responsive to said first target
having a highest
number of related targets. In a still further embodiment, the analyzer is
further configured for
identifying, for each identified target, a shortest path to each other
identified target via a
global molecular entity graph, and identifying a target as functionally
related responsive to
the shortest path to said target being less than a predetermined length.
[0043] In some embodiments, the analyzer is further configured for
determining that
an organ is associated with the indication; and retrieving medication and co-
medication
information of patients that experienced the side effect further comprises
extracting a subset
from the retrieved information of medications and co-medications identified as
affecting the
organ. In other embodiments, the analyzer is further configured for
identifying a molecular
interaction associated with the side effect; and identifying the first
medication, responsive to
the first medication identified in a medication information database as
affecting the identified
molecular interaction.
[0044] In one embodiment, the analyzer is further configured for
identifying the first
medication via a statistical method, in particular for determining a
proportional reporting
ratio of the first medication to all medications in the ordered list, and
identifying the first
medication, responsive to the proportional reporting ratio being above a
predetermined
threshold. In another embodiment, the analyzer is further configured for
scoring each
medication in the list responsive to its frequency of appearance in the
retrieved medication
and co-medication information, and sorting the list by score. In still another
embodiment, the
analyzer is further configured for identifying a combination of a second
medication and third
medication appearing together in the retrieved medication and co-medication
information at a
statistical rate above a predetermined threshold; and the display module is
further configured
for displaying the combination of the second medication and third medication
for
contraindication for the first patient or from the clinical trial.
23
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[0045] In still another aspect, the present disclosure is directed to a
computer readable
storage device comprising computer-readable instructions for identifying a
medication to
exclude from use for, or for contraindication from, an indication, such as a
disease that is the
subject of a clinical trial of another medication, or a diagnosis of a patient
by a physician.
The storage device includes instructions for receiving, by an analyzer
executed by a processor
of a computing device from a user, an identification of an indication of a
first patient, such as
an indication that is the subject of a clinical trial or an indication
identified by a physician as
experienced by the first patient. The storage device also includes
instructions for retrieving,
by the analyzer from an adverse event database, medication and co-medication
information of
patients that experienced a side effect corresponding to the indication. The
storage device
further includes instructions for sorting the retrieved medication and co-
medication
information, by the analyzer, to generate an ordered list of medications
consumed by patients
that experienced the side effect. The storage device also includes
instructions for identifying,
by the analyzer, a first medication of the ordered list. The storage device
also includes
instructions for displaying, by a display module executed by the computing
device to the
user, a first medication of the ordered list for contraindication from the
first patient or from
the clinical trial.
[0046] In some embodiments, the storage device includes instructions for
retrieving,
by the analyzer from a medication information database, an identification of
at least one
target of each medication in the ordered list of medications; and generating,
by the analyzer, a
second ordered list of the retrieved targets. The storage device further
includes instructions
for selecting a first target in the second ordered list, and identifying the
first medication
responsive to the first medication affecting the first target. In a further
embodiment, the
storage device includes instructions for sorting the second ordered list by
frequency of
appearance of each target in the retrieved identifications of targets of each
medication; and
24
CA 02800722 2013-01-04
for selecting the first target, responsive to said target having a frequency
of appearance above
a threshold. In another further embodiment, the storage device includes
instructions for
identifying, for each identified target, one or more targets in the retrieved
identifications of
targets that are functionally related to said target; sorting the second
ordered list by number of
related targets; and selecting the first target responsive to said target
having a highest number
of related targets. In a still further embodiment, the storage device includes
instructions for
identifying, for each identified target, a shortest path to each other
identified target via a
global molecular entity graph, and identifying a target as functionally
related responsive to
the shortest path to said target being less than a predetermined length.
[0047] In some embodiments, the storage device includes instructions for
determining, by the analyzer, that an organ is associated with the indication;
and extracting a
subset from the retrieved information of medications and co-medications
identified as
affecting the organ. In other embodiments, the storage device includes
instructions for
identifying, by the analyzer, a molecular interaction associated with the side
effect; and
identifying the first medication, responsive to the first medication
identified in a medication
information database as affecting the identified molecular interaction.
[0048] In one embodiment, the storage device includes instructions for
identifying the
first medication via a statistical method, in particular for determining a
proportional reporting
ratio of the first medication to all medications in the ordered list, and
identifying the first
medication, responsive to the proportional reporting ratio being above a
predetermined
threshold. In another embodiment, the storage device includes instructions for
scoring each
medication in the list responsive to its frequency of appearance in the
retrieved medication
and co-medication information, and sorting the list by score. In still another
embodiment, the
storage device includes instructions for identifying, by the analyzer, a
combination of a
CA 02800722 2013-01-04
second medication and third medication appearing together in the retrieved
medication and
co-medication information at a statistical rate above a predetermined
threshold; and
displaying, by the display module, the combination of the second medication
and third
medication for contraindication for the first patient or from the clinical
trial.
[0049] In still another aspect, the present disclosure is directed to a
method for
generating a predicted side effect profile for a medication targeting a novel
target. The
method includes retrieving an identification of a novel drug target. The
method also includes
identifying, from a global molecular entity graph, a second target
functionally connected to
the novel drug target. The method further includes retrieving, from a
medication information
database, an identification of a first medication targeting the second target.
The method also
includes retrieving, from an adverse event database, a first side effect
profile associated with
the first medication. The method also includes generating a predicted side
effect profile for
the novel drug target based on the retrieved first side effect profile
associated with the first
medication.
[0050] In yet another aspect, the present disclosure is directed to a
method for
generating a predicted side effect profile for a medication targeting a novel
target. The
method includes receiving, by an analyzer executed by a processor of a
computing device
from a user, an identification of a novel drug target. The method also
includes identifying, by
the analyzer from a global molecular entity graph, a second target
functionally connected to
the novel drug target. The method further includes retrieving, by the analyzer
from a
medication information database, an identification of a first medication
targeting the second
target. The method also includes retrieving, by the analyzer from an adverse
event database,
a first side effect profile associated with the first medication. The method
also includes
generating, by the analyzer, a predicted side effect profile for the novel
drug target based on
26
CA 02800722 2013-01-04
the retrieved first side effect profile associated with the first medication.
The method further
includes presenting, by a display module executed by the computing device to
the user, the
predicted side effect profile for the novel drug target.
[0051] In some embodiments, the method includes identifying a third target
functionally connected to the novel drug target. The method also includes
retrieving, from
the medication information database, an identification of a second medication
targeting the
third target. The method further includes retrieving, from the adverse event
database, a
second side effect profile associated with the second medication. The method
also includes
generating the predicted side effect profile, based on an intersection of the
first side effect
profile and the second side effect profile.
[0052] In one embodiment, the method includes selecting, via a shortest
path
algorithm, the second target from a plurality of targets functionally
connected to the novel
drug target in the graph. In another embodiment, the method includes selecting
the second
target from a plurality of targets functionally connected to the novel drug
target in the graph,
responsive to the second target having a highest number of nodal
interconnections with the
novel drug target of the plurality of targets. In yet another embodiment, the
method includes
selecting the second target from a plurality of targets functionally connected
to the novel drug
target in the graph, responsive to the second target having a fewest number of
nodal
interconnections to nodes not shared with the novel target of the plurality of
targets. In yet
still another embodiment, the method includes selecting the second target
responsive to a
relationship between an organ associated with the second target and the novel
drug target. In
still yet another embodiment, the method includes selecting the second target,
responsive to
the second target and novel drug target being included in a common pathway.
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CA 02800722 2013-01-04
[0053] In some embodiments, the method includes generating a score for each
of a
plurality of targets responsive to interconnections with the novel drug
target, and selecting the
second target responsive to the second target having a highest score of the
plurality of targets,
e. g. the highest scoring target in the list. In a further embodiment, the
method includes
identifying a third target functionally connected to the novel drug target
having a second
highest score of the plurality of targets. The method also includes
retrieving, from the
medication information database, an identification of a second medication
targeting the third
target. The method further includes retrieving, from the adverse event
database, a second
side effect profile associated with the second medication. The method also
includes
generating the predicted side effect profile, based on an intersection of the
first side effect
profile and the second side effect profile.
[0054] In another embodiment, the method includes retrieving an
identification of a
potential participant in a clinical trial of a second medication targeting the
novel drug target.
The method also includes determining that at least one indication of the
potential participant
corresponds to a side effect in the predicted side effect profile for the
novel drug target. The
method further includes excluding the potential participant from the clinical
trial, responsive
to the determination. In other embodiments, the method includes retrieving an
identification
of a patient having an indication to be treated with a second medication
targeting the novel
drug target. The method also includes determining that at least one indication
of the patient
corresponds to a side effect in the predicted side effect profile for the
novel drug target. The
method further includes indicating that the second medication is
contraindicated for the
patient.
[0055] In another aspect, the present disclosure is directed to a system
for generating
a predicted side effect profile for a medication targeting a novel target. The
system includes
28
CA 02800722 2013-01-04
a computing device comprising a processor executing an analyzer and a display
module. The
analyzer is configured for receiving, from a user, an identification of a
novel drug target. The
analyzer is also configured for identifying, from a global molecular entity
graph, a second
target functionally connected to the novel drug target. The analyzer is
further configured for
retrieving, from a medication information database, an identification of a
first medication
targeting the second target. The analyzer is also configured for retrieving,
from an adverse
event database, a first side effect profile associated with the first
medication. The analyzer is
also configured for generating a predicted side effect profile for the novel
drug target based
on the retrieved first side effect profile associated with the first
medication. The display
module is configured for presenting, to the user, the predicted side effect
profile for the novel
drug target.
[0056] In one embodiment, the analyzer is further configured for
identifying a third
target functionally connected to the novel drug target. The analyzer is also
configured for
retrieving, from the medication information database, an identification of a
second
medication targeting the third target. The analyzer is further configured for
retrieving, from
the adverse event database, a second side effect profile associated with the
second
medication. The analyzer is also configured for generating the predicted side
effect profile,
based on an intersection of the first side effect profile and the second side
effect profile.
[0057] In some embodiments, the analyzer is further configured for
selecting, via a
shortest path algorithm, the second target from a plurality of targets
functionally connected to
the novel drug target in the graph. In other embodiments, the analyzer is
further configured
for selecting the second target from a plurality of targets functionally
connected to the novel
drug target in the graph, responsive to the second target having a highest
number of nodal
interconnections with the novel drug target of the plurality of targets. In
yet other
29
CA 02800722 2013-01-04
embodiments, the analyzer is further configured for selecting the second
target protein from a
plurality of targets functionally connected to the novel drug target in the
graph, responsive to
the second target having a fewest number of nodal interconnections to nodes
not shared with
the novel target of the plurality of targets. In still yet other embodiments,
the analyzer is
further configured for selecting the second target responsive to a
relationship between an
organ associated with the second target and the novel drug target. In yet
still other
embodiments, the analyzer is further configured for selecting the second
target, responsive to
the second target and novel drug target being included in a common pathway.
[0058] In some embodiments, the analyzer is further configured for
generating a score
for each of a plurality of targets responsive to interconnections with the
novel drug target, and
selecting the second target responsive to the second target having a highest
score of the
plurality of targets. In a further embodiment, the analyzer is further
configured for
identifying a third target functionally connected to the novel drug target
having a second
highest score of the plurality of targets. The analyzer is also configured for
retrieving, from
the medication information database, an identification of a second medication
targeting the
third target. The analyzer is further configured for retrieving, from the
adverse event
database, a second side effect profile associated with the second medication.
The analyzer is
also configured for generating the predicted side effect profile, based on an
intersection of the
first side effect profile and the second side effect profile.
[0059] In other embodiments, the analyzer is further configured for
retrieving an
identification of a potential participant in a clinical trial of a second
medication targeting the
novel drug target. The analyzer determines that at least one indication of the
potential
participant corresponds to a side effect in the predicted side effect profile
for the novel drug
CA 02800722 2013-01-04
target. The analyzer excludes the potential participant from the clinical
trial, responsive to
the determination.
[0060] In other embodiments, the analyzer is further configured for
retrieving an
identification of a patient having an indication to be treated with a second
medication
targeting the novel drug target. The analyzer determines that at least one
indication of the
patient corresponds to a side effect in the predicted side effect profile for
the novel drug
target. The analyzer indicates that the second medication is contraindicated
for the patient.
[0061] In still another aspect, the present disclosure is directed to a
method for
identifying a medication for contraindication from a clinical trial of another
medication. The
method includes retrieving an identification of an indication that is the
subject of a clinical
trial. The method also includes retrieving, from an adverse event database,
medication and
co-medication information of patients that experienced a side effect
corresponding to the
indication. The method further includes sorting the retrieved medication and
co-medication
information to generate an ordered list of medications consumed by patients
that experienced
the side effect. The method also includes identifying a first medication of
the ordered list.
The method includes determining the first medication of the ordered list for
contraindication
from the clinical trial.
[0062] In still another aspect, the present disclosure is directed to a
method for
identifying a medication for contraindication from a clinical trial of another
medication. The
method includes receiving, by a computing device from a user, an
identification of an
indication that is the subject of a clinical trial. The method also includes
retrieving, by the
computing device from an adverse event database, medication and co-medication
information
of patients that experienced a side effect corresponding to the indication.
The method further
includes sorting the retrieved medication and co-medication information, by
the computing
31
CA 02800722 2013-01-04
device, to generate an ordered list of medications consumed by patients that
experienced the
side effect. The method also includes identifying, by the computing device, a
first
medication of the ordered list. The method further includes displaying, by the
computing
device for the user, a first medication of the ordered list for
contraindication from the clinical
trial.
[0063] In some embodiments, the method includes retrieving, by the
computing
device from a medication information database, an identification of at least
one target of each
medication in the ordered list of medications. The method further includes
generating, by the
computing device, a second ordered list of the retrieved targets. The method
also includes
selecting a first target in the second ordered list, and identifying the first
medication
responsive to the first medication affecting the first target. In a further
embodiment, the
method includes sorting the second ordered list by frequency of appearance of
each target in
the retrieved identifications of targets of each medication; and selecting the
first target,
responsive to said target having a frequency of appearance above a threshold.
In another
further embodiment, the method includes identifying, for each identified
target, one or more
targets in the retrieved identifications of targets that are functionally
related to said target;
sorting the second ordered list by number of related targets; and selecting
the first target
responsive to said target having a highest number of related targets. In a
still further
embodiment, the method includes identifying, for each identified target, a
shortest path to
each other identified target via a global molecular entity graph, and
identifying a target as
functionally related responsive to the shortest path to said target being less
than a
predetermined length.
[0064] In one embodiment, the method includes determining, by the computing
device, that an organ is associated with the indication; and retrieving
medication and co-
32
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medication information of patients that experienced the side effect further
comprises
extracting a subset from the retrieved information of medications and co-
medications
identified as inducing side effects that affect the organ. In another
embodiment, the method
includes identifying, by the computing device, a molecular interaction
associated with the
side effect; and identifying the first medication, responsive to the first
medication identified
in a medication information database as affecting the identified molecular
interaction. In still
another embodiment, the method includes identifying the first medication via a
statistical
method, in particular determining a proportional reporting ratio of the first
medication to all
medications in the ordered list, and identifying the first medication,
responsive to the
proportional reporting ratio being above a predetermined threshold. In yet
still another
embodiment, the method includes scoring each medication in the list responsive
to its
frequency of appearance in the retrieved medication and co-medication
information, and
sorting the list by score. In some embodiments, the method includes
identifying, by the
analyzer, a combination of a second medication and third medication appearing
together in
the retrieved medication and co-medication information at a statistical rate
above a
predetermined threshold; and displaying, by the display module, the
combination of the
second medication and third medication for contraindication from the clinical
trial.
[0065] In another
aspect, the present disclosure is directed to a system for identifying
a medication for contraindication from a clinical trial of another medication.
The system
includes a computing device comprising a processor executing an analyzer. The
analyzer is
configured for receiving, from a user, an identification of an indication that
is the subject of a
clinical trial; retrieving, from an adverse event database, medication and co-
medication
information of patients that experienced a side effect corresponding to the
indication; sorting
the retrieved medication and co-medication information to generate an ordered
list of
medications consumed by patients that experienced the side effect; and
identifying, by the
33
CA 02800722 2013-01-04
analyzer, a first medication of the ordered list. The system further includes
a display module
configured for displaying the first medication of the ordered list for
contraindication from the
clinical trial of the other medication.
10066] In some embodiments of the system, the analyzer is further
configured for
retrieving, from a medication information database, an identification of at
least one target of
each medication in the ordered list of medications, and generating a second
ordered list of the
retrieved targets. The analyzer is also configured for selecting a first
target in the second
ordered list, and identifying the first medication responsive to the first
medication affecting
the first target. In a further embodiment of the system, the analyzer is
further configured for
sorting the second ordered list by frequency of appearance of each target in
the retrieved
identifications of targets of each medication; and selecting the first target,
responsive to said
target having a frequency of appearance above a threshold. In another further
embodiment,
the analyzer is further configured for identifying, for each identified
target, one or more
targets in the retrieved identifications of targets that are functionally
related to said target, and
sorting the second ordered list by number of related targets. The analyzer is
also configured
for selecting the first target responsive to said target having a highest
number of related
targets. In a still further embodiment, the analyzer is further configured for
identifying, for
each identified target, a shortest path to each other identified target via a
global molecular
entity graph, and identifying a target as functionally related responsive to
the shortest path to
said target being less than a predetermined length.
100671 In one embodiment of the system, the analyzer is further configured
for
determining that an organ is associated with the indication; and extracting a
subset from the
retrieved information of medications and co-medications identified as inducing
side effects
that affect the organ. In another embodiment of the system, the analyzer is
further configured
34
CA 02800722 2013-01-04
for identifying a molecular interaction associated with the side effect; and
identifying the first
medication, responsive to the first medication identified in a medication
information database
as affecting the identified molecular interaction.
[0068] In some embodiments of the system, the analyzer is further
configured for
identifying the first medication via a statistical method, in particular for
determining a
proportional reporting ratio of the first medication to all medications in the
ordered list, and
identifying the first medication, responsive to the proportional reporting
ratio being above a
predetermined threshold. In other embodiments of the system, the analyzer is
further
configured for scoring each medication in the list responsive to its frequency
of appearance in
the retrieved medication and co-medication information, and sorting the list
by score. In still
other embodiments of the system, the analyzer is further configured for
identifying a
combination of a second medication and third medication appearing together in
the retrieved
medication and co-medication information at a statistical rate above a
predetermined
threshold; and the display module is further configured for displaying the
combination of the
second medication and third medication for contraindication from the clinical
trial.
[0069] In yet another aspect, the present disclosure is directed to a
computer readable
storage device comprising computer-readable instructions for identifying a
medication for
contraindication from a clinical trial of another medication. The storage
device includes
instructions for receiving, by an analyzer executed by a processor of a
computing device from
a user, an identification of an indication that is the subject of a clinical
trial; instructions for
retrieving, by the analyzer from an adverse event database, medication and co-
medication
information of patients that experienced a side effect corresponding to the
indication;
instructions for sorting the retrieved medication and co-medication
information, by the
analyzer, to generate an ordered list of medications consumed by patients that
experienced
CA 02800722 2013-01-04
the side effect; instructions for identifying, by the analyzer, a first
medication of the ordered
list; and instructions for displaying, by a display module executed by the
computing device to
the user, the first medication of the ordered list for contraindication from
the clinical trial of
the other medication.
100701 In some embodiments, the storage device includes instructions for
retrieving,
by the analyzer from a medication information database, an identification of
at least one
target of each medication in the ordered list of medications; instructions for
generating, by
the analyzer, a second ordered list of the retrieved targets; and instructions
for selecting a first
target in the second ordered list, and identifying the first medication
responsive to the first
medication affecting the first target. In a further embodiment, the storage
device includes
instructions for: sorting the second ordered list by frequency of appearance
of each target in
the retrieved identifications of targets of each medication; and selecting the
first target,
responsive to said target having a frequency of appearance above a threshold.
In another
further embodiment, the storage device includes instructions for identifying,
for each
identified target, one or more targets in the retrieved identifications of
targets that are
functionally related to said target; sorting the second ordered list by number
of related targets;
and wherein selecting the first target comprises selecting the first target
responsive to said
target having a highest number of related targets. In a still further
embodiment, the
computing device includes instructions for identifying, for each identified
target, a shortest
path to each other identified target via a global molecular entity graph, and
identifying a
target as functionally related responsive to the shortest path to said target
being less than a
predetermined length.
[0071] In another embodiment, the storage device includes instructions for
determining, by the analyzer, that an organ is associated with the indication;
and extracting a
36
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subset from the retrieved information of medications and co-medications
identified as
affecting the organ. In still another embodiment, the storage device includes
instructions for
identifying, by the analyzer, a molecular interaction associated with the side
effect; and
identifying the first medication, responsive to the first medication
identified in a medication
information database as affecting the identified molecular interaction. In
still yet another
embodiment, the storage device includes instructions for identifying the first
medication via a
statistical method, in particular for determining a proportional reporting
ratio of the first
medication to all medications in the ordered list, and identifying the first
medication,
responsive to the proportional reporting ratio being above a predetermined
threshold. In yet
another embodiment, the storage device includes instructions for scoring each
medication in
the list responsive to its frequency of appearance in the retrieved medication
and co-
medication information, and sorting the list by score. In some embodiments,
the storage
device includes instructions for identifying, by the analyzer, a combination
of a second
medication and third medication appearing together in the retrieved medication
and co-
medication information at a statistical rate above a predetermined threshold;
and displaying,
by the display module, the combination of the second medication and third
medication for
contraindication from the clinical trial.
[0072] In another
aspect, the present disclosure is directed to a method for identifying
combination therapies via adverse event data. The method includes retrieving
an
identification of an indication. The method further includes retrieving an
identification of a
first patient cohort treated via a first biomolecular entity. The method also
includes extracting
from an adverse event database a first subset of statistical data of adverse
events or outcomes
experienced by the identified first patient cohort. The method further
includes retrieving an
identification of a second patient cohort treated via the first biomolecular
entity and co-
medicated via a second biomolecular entity. The method also includes
extracting from the
37
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adverse event database a second subset of statistical data of adverse events
or outcomes
experienced by the identified second patient cohort. The method further
includes comparing
for each adverse event in the statistical data of adverse events, the first
subset and second
subset to generate a difference value. The method also includes collating the
generated
difference values into a list of statistical differences of adverse events or
outcomes between
the first patient cohort and the second patient cohort.
[0073] In another
aspect, the present disclosure is directed to a method for identifying
combination therapies for research via adverse event data. The method includes
receiving, by
a computing device from a user, an identification of an indication. The method
also includes
retrieving, by the computing device from an adverse event database,
statistical data of
adverse events experienced by patients with the identified indication. The
method further
includes receiving, by the computing device from the user, an identification
of a first patient
cohort treated via a first biomolecular entity. The method also includes
extracting, by the
computing device from the retrieved statistical data, a first subset of
statistical data of adverse
events or outcomes experienced by the identified first patient cohort. The
method includes
receiving, by the computing device from the user, an identification of a
second patient cohort
treated via the first biomolecular entity and co-medicated via a second
biomolecular entity.
The method also includes extracting, by the computing device from the
retrieved statistical
data, a second subset of statistical data of adverse events or outcomes
experienced by the
identified second patient cohort. The method further includes comparing, by
the computing
device for each adverse event in the statistical data of adverse events, the
first subset and
second subset to generate one or more difference values, the generated
difference values
collated into a list of statistical differences of adverse events or outcomes
between the first
patient cohort and the second patient cohort. The method also includes
displaying, by the
computing device, the collated list of statistical differences.
38
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[0074] In one embodiment of the method, the first biomolecular entity
comprises a
medication, drug class, target, or pathway. In another embodiment, the second
biomolecular
entity comprises a medication, drug class, target, or pathway. In yet another
embodiment,
the method includes identifying a difference between a molecular interaction
of the first
biomolecular entity and a molecular interaction of the second biomolecular
entity, and
displaying the identified different molecular interaction of the second
medication to the user.
[0075] In some embodiments, the method includes identifying a reduced
incidence of
adverse events; and responsive to the identification, using the second
biomolecular entity and
first biomolecular entity as a combination therapy for a patient having the
indication. In other
embodiments, the method includes identifying an increased incidence of
positive outcomes of
the second patient cohort; and responsive to the identification, using the
second biomolecular
entity and first biomolecular entity as a combination therapy for a patient
having the
indication. In still other embodiments, the method includes identifying a
difference value
beyond a predetermined threshold; and indicating the identified difference
value in the
collated list.
[0076] In still another aspect, the present disclosure is directed to a
system for
identifying combination therapies for research via adverse event data. The
system includes a
computing device comprising a processor executing an analyzer, and a display
module. The
analyzer is configured for: receiving, from a user, an identification of an
indication;
retrieving, an adverse event database, statistical data of adverse events
experienced by
patients with the identified indication; and receiving, from the user, an
identification of a first
patient cohort treated via a first biomolecular entity. The analyzer is also
configured for
extracting, from the retrieved statistical data, a first subset of statistical
data of adverse events
or outcomes experienced by the identified first patient cohort; receiving,
from the user, an
39
CA 02800722 2013-01-04
identification of a second patient cohort treated via the first biomolecular
entity and co-
medicated via a second biomolecular entity; extracting, from the retrieved
statistical data, a
second subset of statistical data of adverse events or outcomes experienced by
the identified
second patient cohort; and comparing, for each adverse event in the
statistical data of adverse
events, the first subset and second subset to generate one or more difference
values, the
generated difference values collated into a list of statistical differences of
adverse events or
outcomes between the first patient cohort and the second patient cohort. The
display module
is configured for displaying the collated list of statistical differences.
[0077] In some embodiments of the system, the first biomolecular entity
comprises a
medication, drug class, target, or pathway. In other embodiments of the
system, the second
biomolecular entity comprises a medication, drug class, target, or pathway. In
one
embodiment, the analyzer is further configured for identifying a difference
between a
molecular interaction of the first biomolecular entity and a molecular
interaction of the
second biomolecular entity, and displaying the identified different molecular
interaction of
the second medication to the user. In another embodiment, the analyzer is
further configured
for identifying a reduced incidence of adverse events; and responsive to the
identification,
using the second biomolecular entity and first biomolecular entity as a
combination therapy
for a patient having the indication.
[0078] In some embodiments, the analyzer is further configured for
identifying an
increased incidence of positive outcomes of the second patient cohort; and
responsive to the
identification, using the second biomolecular entity and first biomolecular
entity as a
combination therapy for a patient having the indication. In other embodiments,
the analyzer
is further configured for identifying a difference value beyond a
predetermined threshold; and
indicating the identified difference value in the collated list.
CA 02800722 2013-01-04
[0079] In yet another aspect, the present disclosure is directed to a
system for
displaying molecular interactions between a plurality of molecular entities in
an intuitive
format. The system includes a computing device, comprising an input module for
receiving,
from a user, an identification of one or more medications. The computing
device also
includes a database comprising entries corresponding to the one or more
medications, each
entry associated with a target, metabolizing enzyme, transporter, or protein
pathway. The
computing device further includes a display module for generating for display
for the user a
radial graph, the radial graph comprising a plurality of radial entries
grouped into sub-groups
of medications, targets, metabolizing enzymes, transporters, and pathways,
each radial entry
in the radial graph visually connected to associated radial entries according
to the database. In
some embodiments, the display module is further configured to highlight one or
more visual
connections to a radial entry, responsive to user interaction with the radial
entry. In many
embodiments, associated radial entries are connected via Bezier curves or
splines.
[0080] In still another aspect, the present disclosure is directed to a
method for
identifying molecular entities responsible for adverse event differences
between similar
indications. The method includes retrieving an identification of a first
indication and a second
indication similar to the first indication, and an identification of an
adverse event. The
method further includes retrieving from an adverse event database an ordered
first list of
medications prescribed to patients with the first indication who experienced
the identified
adverse event, and an ordered second list of medications associated with the
second
indication who experienced the identified adverse event, each of the first
list and second list
ordered by percentage of adverse event-experiencing patients prescribed each
medication.
The method also includes identifying, by the computing device, a medication
included in the
ordered first list of medications and the ordered second list of medications,
the medication
having a different percentage value and/or order position in each ordered
list. The method
41
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includes retrieving, by the computing device from a medication information
database, a third
list of molecular entities associated with the identified medication. The
method also includes
determining elements from the third list as molecular entities potentially
affected by only one
of the first indication and second indication.
[0081] In still another aspect, the present disclosure is directed to a
method for
identifying molecular entities responsible for adverse event differences
between similar
indications. The method includes receiving, by a computing device from a user,
an
identification of a first indication and a second indication similar to the
first indication, and
an identification of an adverse event. The method also includes retrieving, by
the computing
device from an adverse event database, an ordered first list of medications
prescribed to
patients with the first indication who experienced the identified adverse
event, and an ordered
second list of medications associated with the second indication who
experienced the
identified adverse event, each of the first list and second list ordered by
percentage of adverse
event-experiencing patients prescribed each medication. The method further
includes
identifying, by the computing device, a medication included in the ordered
first list of
medications and the ordered second list of medications, the medication having
a different
percentage value and/or order position in each ordered list. The method also
includes
retrieving, by the computing device from a medication information database, a
third list of
molecular entities associated with the identified medication. The method
includes presenting,
by the computing device to the user, the third list as a list of molecular
entities potentially
affected by only one of the first indication and second indication.
100821 In some embodiments of the method, the first indication and second
indication
share at least one symptom. In other embodiments of the method, the first
indication and
second indication affect the same organ, pathway, protein, or molecular
entity. In still other
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CA 02800722 2013-01-04
embodiments of the method, the first indication and second indication are
subsets of the same
category of indication. In yet still other embodiments of the method, the
adverse event is
distinct from the first indication and second indication.
[0083] In one embodiment, the method includes retrieving, from the
medication
information database, an identification of a medication affecting a molecular
entity of the
third list; and prescribing the identified medication to a patient having the
first indication or
second indication. In another embodiment, the method includes identifying a
second
medication included in the ordered list of medications and the ordered second
list of
medications, the second medication having a different percentage value and/or
order position
in each ordered list; retrieving, from the medication information database, a
fourth list of
molecular entities associated with the identified second medication;
generating a subset list of
molecular entities from an intersection of the third list and fourth list; and
presenting, to the
user, the subset list of molecular entities potentially affected by only one
of the first
indication and second indication.
[0084] In yet another aspect, the present disclosure is directed to a
system for
identifying molecular entities responsible for adverse event differences
between similar
indications. The system includes a computing device comprising a processor and
a display
module. The processor executes an analyzer, configured for receiving, from a
user, an
identification of a first indication and a second indication similar to the
first indication, and
an identification of an adverse event; and retrieving, from an adverse event
database, an
ordered first list of medications prescribed to patients with the first
indication who
experienced the identified adverse event, and an ordered second list of
medications associated
with the second indication who experienced the identified adverse event, each
of the first list
and second list ordered by percentage of adverse event-experiencing patients
prescribed each
43
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medication. The analyzer is also configured for identifying a medication
included in the
ordered first list of medications and the ordered second list of medications,
the medication
having a different percentage value and/or order position in each ordered
list, and retrieving,
from a medication information database, a third list of molecular entities
associated with the
identified medication. The display module is configured for presenting, to the
user, the third
list as a list of molecular entities potentially affected by only one of the
first indication and
second indication.
[0085] In some embodiments of the system, the first indication and second
indication
share at least one symptom. In other embodiments of the system, the first
indication and
second indication affect the same organ, pathway, protein, or molecular
entity. In still other
embodiments of the system, the first indication and second indication are
subsets of the same
category of indication. In yet still other embodiments of the system, the
adverse event is
distinct from the first indication and second indication.
[0086] In one embodiment, the analyzer is further configured for
retrieving, from the
medication information database, an identification of a medication affecting a
molecular
entity of the third list; and prescribing the identified medication to a
patient having the first
indication or second indication. In another embodiment, the analyzer is
further configured
for identifying a second medication included in the ordered list of
medications and the
ordered second list of medications, the second medication having a different
percentage value
and/or order position in each ordered list; retrieving, from the medication
information
database, a fourth list of molecular entities associated with the identified
second medication;
and generating a subset list of molecular entities from an intersection of the
third list and
fourth list. The display module is further configured for presenting, to the
user, the subset list
44
CA 02800722 2013-01-04
of molecular entities potentially affected by only one of the first indication
and second
indication.
[0087] In still yet another aspect, the present disclosure is directed to a
method for
identifying genetic variants associated with adverse events in a clinical
trial. The method
includes retrieving an identification of an adverse event experienced by a
participant in a
clinical trial of a first medication. The method also includes retrieving from
an adverse event
database an ordered list of targets associated with the adverse event. The
method further
includes retrieving an identification of a plurality of genomic variants of
the participant. The
method also includes modifying an order of the list of targets responsive to a
target in the list
corresponding to an identified genomic variant of the participant to create a
prioritized list of
potential targets inducing the adverse event.
[0088] In still yet another aspect, the present disclosure is directed to a
method for
identifying genetic variants associated with adverse events in a clinical
trial. The method
includes receiving, by a computing device from a user, an identification of an
adverse event
experienced by a participant in a clinical trial of a first medication. The
method also includes
retrieving, by the computing device from an adverse event database, an ordered
list of protein
targets associated with the adverse event. The method further includes
receiving, by the
computing device, an identification of a plurality of genomic variants of the
participant. The
method also includes modifying, by the computing device, an order of the list
of protein
targets responsive to a protein target in the list corresponding to an
identified genomic variant
of the participant to create a prioritized list of potential targets inducing
the adverse event.
The method also includes displaying, by the computing device, the modified
list to the user.
[0089] In some embodiments, the method includes selecting a first target of
the
modified list; identifying a second participant in the clinical trial having a
genomic variant
CA 02800722 2013-01-04
corresponding to the selected first target; and excluding the second
participant from the
clinical trial, responsive to the identification. In other embodiments of the
method, the
ordered list of targets is ordered by frequency of appearance of each target
in adverse event
reports.
[0090] In still other embodiments, the method includes retrieving, from the
adverse
event database, an identification of one or more medications consumed by
patients
experiencing the adverse event; and identifying, from a medication information
database, one
or more targets associated with the identified one or more medications. In a
further
embodiment, the ordered list of targets is ordered by frequency of appearance
of each of the
identified one or more medications in adverse event reports. In another
further embodiment,
the ordered list of targets is ordered by a proportional reporting ratio value
associated with
each of the identified one or more medications in adverse event reports.
[0091] In some embodiments, the method includes increasing a score
associated with
each target responsive to the first participant having a variant associated
with said target, the
list sorted by score. In other embodiments, the method includes increasing a
score associated
with each target responsive to said target being associated with an organ
affected by the
adverse event.
[0092] In another aspect, the present disclosure is directed to a system
for identifying
genetic variants associated with adverse events in a clinical trial,
comprising a computing
device comprising a display module and a processor executing an analyzer. The
analyzer is
configured for retrieving an identification of an adverse event experienced by
a participant in
a clinical trial of a first medication. The analyzer is also configured for
retrieving from an
adverse event database an ordered list of targets associated with the adverse
event. The
analyzer is also configured for retrieving an identification of a plurality of
genomic variants
46
CA 02800722 2013-01-04
of the participant. The analyzer is further configured for modifying an order
of the list of
targets responsive to a target in the list corresponding to an identified
genomic variant of the
participant to create a prioritized list of potential targets inducing the
adverse event. The
display module is configured for displaying the modified list to the user.
[0093] In some embodiments, the analyzer is further configured for
selecting a first
target of the modified list; identifying a second participant in the clinical
trial having a
genomic variant corresponding to the selected first target; and excluding the
second
participant from the clinical trial, responsive to the identification. In
other embodiments, the
ordered list of targets is ordered by frequency of appearance of each target
in adverse event
reports.
[0094] In one embodiment, the analyzer is further configured for
retrieving, from the
adverse event database, an identification of one or more medications consumed
by patients
experiencing the adverse event; and identifying, from a medication information
database, one
or more targets associated with the identified one or more medications. In a
further
embodiment, the the ordered list of targets is ordered by frequency of
appearance of each of
the identified one or more medications in adverse event reports. In another
further
embodiment, the ordered list of targets is ordered by a proportional reporting
ratio value
associated with each of the identified one or more medications in adverse
event reports. In
some embodiments, the analyzer is further configured for increasing a score
associated with
each target responsive to the first participant having a variant associated
with said target, the
list sorted by score. In other embodiments, the analyzer is further configured
for increasing a
score associated with each target responsive to said target being associated
with an organ
affected by the adverse event.
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[0095] The details of various embodiments of the invention are set forth in
the
accompanying drawings and the description below.
Brief Description of the Figures
[0096] The foregoing and other objects, aspects, features, and advantages
of the
disclosure will become more apparent and better understood by referring to the
following
description taken in conjunction with the accompanying drawings, in which:
[0097] FIG. IA is a block diagram depicting relationships between data
provided by
embodiments of an adverse event reporting system;
[0098] FIG. 1B is a block diagram depicting relationships between molecular
entities
in an embodiment of a multivariate analysis system;
[0099] FIG. 2A is a block diagram depicting an embodiment of a network
environment comprising local machines in communication with remote machines;
[0100] FIGs. 2B-2E are block diagrams depicting embodiments of computers
useful
in connection with the methods and systems described herein;
[0101] FIG. 3A is a block diagram of an embodiment of a system for
multivariate
analysis of adverse event data;
[0102] FIG. 3B is a diagram of an example embodiment of a global molecular
entity
graph;
[0103] FIG. 3C is a diagram of an example embodiment of extracted
subgraphs;
[0104] FIG. 4A is a diagram of an embodiment of method for identifying
molecular
entities responsible for adverse event differences between similar
indications;
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[0105] FIG. 4B is a flow chart of an embodiment of method for identifying
molecular
entities responsible for adverse event differences between similar
indications;
[0106] FIG. 4C is a flow chart of an embodiment of a method for retrieving
an
ordered list of medications for an indication and adverse event;
[0107] FIG. 5A is a diagram of another embodiment of a global molecular
entity
graph;
[0108] FIG. 5B is a flow diagram of an embodiment of a method for
extracting an
indication-specific model from a global molecular entity graph;
[0109] FIG. 5C is another diagram of another embodiment of a global
molecular
entity graph;
[0110] FIG. 5D is a flow diagram of an embodiment of a method for examining
side
effects associated with activating a pathway vs. inactivating the pathway;
[0111] FIG. 6A is a diagram of a method of utilizing side effect profile
dissimilarities
to identify likely unknown targets of a medication;
[0112] FIG. 6B is a flow chart of an embodiment of a method for identifying
unknown likely targets of a first medication via comparison of adverse event
data;
[0113] FIG. 7A-7C are screenshots of an example of embodiments of a
molecular
entity dependency graph that provides intuitive identification of redundancies
and molecular
interactions between medications in a patient's prescription load;
49
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[0114] FIG. 8 is a flow chart of an embodiment of a method for personalized
de-
risking of medications based on genomic information of a patient and adverse
event data of
combination therapies;
[0115] FIG. 9 is a flow chart of an embodiment of a method for identifying
a
medication for contraindication from a clinical trial of another medication;
[0116] FIG. 10A is a Venn diagram of an example of an embodiment of
defining
cohorts within adverse event data and extracting difference profiles for a
cohort;
[0117] FIG. 1013 is a flow chart of an embodiment of a method for
identifying
potential combination therapies for research via adverse event data;
[0118] FIG. 11A is a graph of an example of a region of an example
embodiment of a
global molecular entity graph or molecular entity network comprising a
plurality of molecular
entities 1106 connected via functional links;
[0119] FIG. 11B is a flow chart of an embodiment of a method for generating
a
predicted side effect profile for a medication targeting a novel target;
[0120] FIG. 12A is a block diagram of an embodiment of a process for using
genomic
information to identify protein targets responsible for adverse events;
[0121] FIG. 12B is a flow chart of an embodiment of a method of identifying
genetic
variants associated with adverse events;
[0122] FIGs. 13A-13Y are screenshots of an example embodiment of an
interface for
analyzing adverse event data; and
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[0123] FIGs. 14A-14C are screenshots of an example embodiment of comparison
of
side effect profiles for molecular entities.
[0124] The features and advantages of the present invention will become
more
apparent from the detailed description set forth below when taken in
conjunction with the
drawings, in which like reference characters identify corresponding elements
throughout. In
the drawings, like reference numbers generally indicate identical,
functionally similar, and/or
structurally similar elements.
Detailed Description of the Invention
[0125] Adverse events are a common and, for the most part, unavoidable
consequence of therapeutic intervention. The identification of novel adverse
events is critical
to the protection of patient well-being and the healthcare system that
supports them. From the
induction of avoidable and sometimes fatal side effects to the billions of
dollars in associated
medical costs, adverse events (AE's) remain a critical issue for all
stakeholders in the
healthcare system.
[0126] Data about adverse events are provided by clinicians, researchers,
and
manufacturers to spontaneous reporting systems, such as the U.S. Food and Drug
Administration's Adverse Event Reporting System (AERS). After a manual review
of each
submission the data are made publically available on quarterly basis via the
online AERS
data files. All reports contain information surrounding the treatment, side
effects, and patient
characteristics/demographics. Drug information is further qualified as to
whether the drug is
suspected as the primary or secondary cause of the adverse event or whether it
was
concomitant. However, there are a number of considerations that limit the
usefulness of the
AERS data for pharmacovigilance purposes. Traditional methods of Adverse Drug
Reaction
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(ADR) detection have often relied on the manual review of drug-specific cases
by clinical
pharmacologists. However, the increasing size and complexity of SRS databases,
and
limitations in human resources have led to demands for more efficient methods
of ADR
detection. Additionally, AERS data is frequently difficult to use, with
misspellings,
abbreviations, and inconsistent synonyms used. Furthermore, as adverse event
reporting
systems focus on adverse events and drugs, detailed molecular information is
absent. For
example, referring briefly to FIG. 1A, adverse event data typically includes
identifications of
drugs prescribed to a patient 102; indications 104, or diseases or symptoms
for which the
drug or drugs was prescribed; reactions or side effects 106; and outcomes 108.
For example,
an outcome 108 may comprise prolonged hospitalization, short term
hospitalization, or death.
Accordingly, while the data may be useful for identifying drug-drug
interactions, or
performing univariate analysis, such as the statistical percentage of patients
taking a drug that
had a particular outcome when experiencing an adverse event, the data may be
limited in
utility on its own.
101271 The systems
and methods discussed herein provide for multivariate analysis of
molecular entities involved with adverse events. Referring briefly to FIG. 1B
and in
contradistinction from FIG. IA, a multivariate analyzer 120 may utilize links
between not
just drugs 102, indications 104, reactions 106, and outcomes 108, but
molecular entities such
as pathways 110, protein targets 112, metabolizing enzymes or transporters
114. Drugs 102
may also be associated with a drug class 116. This enables investigation of
the relationship
between, say, a particular side effect or reaction 106 and a protein target
112, or other entity
types such as protein domains, gene ontology terms for biological processes,
and other
biological, chemical, or clinical descriptors. Deciphering the molecular basis
of such adverse
responses is not only paramount to the protection of patient well-being and
the development
52
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of safer drugs, but it also presents a unique opportunity to dissect disease
systems in search of
novel predictive biomarkers, drug targets and efficacious combination
therapies.
[0128] Prior to discussing specifics of methods and systems utilizing
multivariate
analysis of adverse event data, it may be helpful to briefly define a few
terms as used herein.
The following definitions are not intended to be limiting, but may comprise
alternate
definitions commonly utilized by those of ordinary skill in the art.
Accordingly, context may
clarify whether, for example, the term indication refers to a symptom or
disease, a flag in a
database, or a selection by a user. Additionally, the following list of
definitions is not
intended to be exhaustive, but rather discuss a few key terms that may be
helpful to those of
skill in the art.
[0129] Adverse event: In pharmacology, an adverse event may refer to any
unexpected or dangerous reaction to a drug. An unwanted effect caused by the
administration
of a drug. The onset of the adverse reaction may be sudden or develop over
time. Also
interchangeably called: adverse drug event (ADE), adverse drug reaction (ADR),
adverse
effect or adverse reaction.
[0130] Absorption, Distribution, Metabolism, Excretion (ADME): Refers to
the
standard pharmacokinetic mechanism of a drug (see Pharmacokinetics).
[0131] AERS - Adverse Event Reporting System: The Adverse Event Reporting
System (AERS) is a computerized information database designed to support the
FDA's post-
marketing safety surveillance program for all approved drug and therapeutic
biologic
products. The FDA uses AERS to monitor for new adverse events and medication
errors that
might occur with these marketed products.
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[0132] Bioavailability: Also referred to as availability, this is the
amount of a drug
that is absorbed into circulation after administration of a specific dosage.
[0133] Challenge-dechallenge-rechallenge (CDR): This is a medical testing
protocol
in which a medicine (or drug) is administered (challenge), withdrawn
(dechallenge), then re-
administered (rechallenge), while being monitored for adverse effects
(reactions) at each
stage.
101341 Contingency table (or matrix): Also referred to as cross tabulation
or cross tab.
A contingency table is often used to record and analyze the relation between
two or more
categorical variables. It displays the (multivariate) frequency distribution
of the variables in a
matrix format.
[0135] Drug interaction: A drug interaction is a situation in which a
substance affects
the activity of a drug, i.e. the effects are increased or decreased, or they
produce a new effect
that neither produces on its own. However, interactions may also exist between
drugs &
foods (drug-food interactions), as well as drugs & herbs (drug-herb
interactions). These may
occur out of accidental misuse or due to lack of knowledge about the active
ingredients
involved in the relevant substances or the underlying molecular mechanisms.
[0136] Entity Coverage/Co-Entity Coverage: The Entity Coverage is an
estimate that
refers to the significance with which a first entity (El) is related with a
second entity (E2) in a
data set. it is the calculated from the number of data entries containing El
and E2 divided by
the overall number of data entries containing El. The Co-Entity Coverage is
the calculated
from the number of data entries containing El and E2 divided by the overall
number of data
entries containing E2. This method gives thus an indication for the
significance of entity
relations in subsets of data.
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[0137] Gamma Poisson Shrinker: Advanced method for Pharmacovigilance Signal
Detection. In contrast to simple methods that focus on a specific AE-drug-
combination at a
time (encoded in 2*2 contingency tables), it can directly use contingency
tables that range
over all drugs and AEs.
[0138] Idiosyncratic response: An abnormal response from a drug that is
specific to
the person having the response.
[0139] Indication (or 'drug use'): In medicine, an indication is a valid
reason to use a
certain test, medication, procedure, or surgery. An indication may thus refer
to a disease, a
symptom, or diagnosis. The opposite of indication is contraindication.
[0140] Metabolizing enzyme: A protein that metabolizes a medication; the
enzyme
may help transforming a pro-drug to its pharmacologically active chemical
compound form
or it may play a role in its degradation.
[0141] Molecular mechanism: The flow of events that take place in the
molecular
level when a drug is administered. The molecular mechanisms can be highly
complex due to
the variety of participating components (e.g., drugs, organs, cells, proteins,
etc.), systems
(e.g., pathways, disease networks, etc.), entity interrelations (e.g., drug-
target, drug-
metabolizing enzyme, carriers, transporters, overlapping systems and pathways,
etc.), and
molecular aberrations (e.g., mutations, radiation damage, etc.). Components of
the molecular
mechanism, such as protein targets, pathways, transporters, drugs, or drug
classes may be
referred to variously as molecular entities or biomolecular entities. Protein
targets may be
generally referred to as targets.
[0142] Side effect: Any unintended effect of a pharmaceutical product
occurring at a
dose normally used in man, which is related to the pharmacological properties
of the drug. A
CA 02800722 2013-01-04
side effect may frequently correspond to an indication. For example, nausea
may be a side
effect of a first drug, but may be an indication to be treated by a second
drug. A negative side
effect may also be referred to as an adverse event.
[0143] Prior to discussing specifics of methods and systems for
multivariate analysis
of adverse event data, it may be helpful to briefly discuss embodiments of
networks and
computing devices that may be utilized in various embodiments of these methods
and
systems. It is to be noted that all methods for multivariate analysis of
adverse event data as
described herein are preferably computer implemented methods. Referring now to
FIG. 2A,
an embodiment of a network environment is depicted. In brief overview, the
network
environment comprises one or more local machines 202a-202n (also generally
referred to as
local machine(s) 202, client(s) 202, client node(s) 202, client machine(s)
202, client
computer(s) 202, client device(s) 202, endpoint(s) 202, or endpoint node(s)
202) in
communication with one or more remote machines 206a-206n (also generally
referred to as
server(s) 206 or remote machine(s) 206) via one or more networks 204. In some
embodiments, a local machine 202 has the capacity to function as both a client
node seeking
access to resources provided by a server and as a server providing access to
hosted resources
for other clients 202a-202n.
[0144] Although FIG. 2A shows a network 204 between the local machines 202
and
the remote machines 206, the local machines 202 and the remote machines 206
may be on the
same network 204. The network 204 can be a local-area network (LAN), such as a
company
Intranet, a metropolitan area network (MAN), or a wide area network (WAN),
such as the
Internet or the World Wide Web. In some embodiments, there are multiple
networks 204
between the local machines 202 and the remote machines 206. In one of these
embodiments,
a network 204' (not shown) may be a private network and a network 204 may be a
public
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network. In another of these embodiments, a network 204 may be a private
network and a
network 204' a public network. In still another embodiment, networks 204 and
204' may
both be private networks. In yet another embodiment, networks 204 and 204' may
both be
public networks.
[01451 The network 204 may be any type and/or form of network and may
include
any of the following: a point to point network, a broadcast network, a wide
area network, a
local area network, a telecommunications network, a data communication
network, a
computer network, an ATM (Asynchronous Transfer Mode) network, a SONET
(Synchronous Optical Network) network, a SDH (Synchronous Digital Hierarchy)
network, a
wireless network and a wireline network. In some embodiments, the network 204
may
comprise a wireless link, such as an infrared channel or satellite band. The
topology of the
network 204 may be a bus, star, or ring network topology. The network 204 may
be of any
such network topology as known to those ordinarily skilled in the art capable
of supporting
the operations described herein. The network may comprise mobile telephone
networks
utilizing any protocol or protocols used to communicate among mobile devices,
including
AMPS, TDMA, CDMA, GSM, GPRS or UMTS. In some embodiments, different types of
data may be transmitted via different protocols. In other embodiments, the
same types of
data may be transmitted via different protocols.
[0146] In some embodiments, the system may include multiple, logically-
grouped
remote machines 206. In one of these embodiments, the logical group of remote
machines
may be referred to as a server farm 38. In another of these embodiments, the
remote
machines 206 may be geographically dispersed. In other embodiments, a server
farm 38 may
be administered as a single entity. In still other embodiments, the server
farm 38 comprises a
plurality of server farms 38. The remote machines 206 within each server farm
38 can be
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heterogeneous ¨ one or more of the remote machines 206 can operate according
to one type
of operating system platform (e.g., WINDOWS NT, WINDOWS 2003, WINDOWS 2008,
WINDOWS 7 and WINDOWS Server 2008 R2, all of which are manufactured by
Microsoft
Corp. of Redmond, Washington), while one or more of the other remote machines
206 can
operate on according to another type of operating system platform (e.g., Unix
or Linux).
[01471 The remote machines 206 of each server farm 38 do not need to be
physically
proximate to another remote machine 206 in the same server farm 38. Thus, the
group of
remote machines 206 logically grouped as a server farm 38 may be
interconnected using a
wide-area network (WAN) connection or a metropolitan-area network (MAN)
connection.
For example, a server farm 38 may include remote machines 206 physically
located in
different continents or different regions of a continent, country, state,
city, campus, or room.
Data transmission speeds between remote machines 206 in the server farm 38 can
be
increased if the remote machines 206 are connected using a local-area network
(LAN)
connection or some form of direct connection.
[0148] A remote machine 206 may be a file server, application server, web
server,
proxy server, appliance, network appliance, gateway, application gateway,
gateway server,
virtualization server, deployment server, SSL VPN server, or firewall. In some
embodiments, a remote machine 206 provides a remote authentication dial-in
user service,
and is referred to as a RADIUS server. In other embodiments, a remote machine
206 may
have the capacity to function as either an application server or as a master
application server.
In still other embodiments, a remote machine 206 is a blade server. In yet
other
embodiments, a remote machine 206 executes a virtual machine providing, to a
user or client
computer 202, access to a computing environment.
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[0149] In one embodiment, a remote machine 206 may include an Active
Directory.
The remote machine 206 may be an application acceleration appliance. For
embodiments in
which the remote machine 206 is an application acceleration appliance, the
remote machine
206 may provide functionality including firewall functionality, application
firewall
functionality, or load balancing functionality. In some embodiments, the
remote machine
206 comprises an appliance such as one of the line of appliances manufactured
by the Citrix
Application Networking Group, of San Jose, CA, or Silver Peak Systems, Inc.,
of Mountain
View, CA, or of Riverbed Technology, Inc., of San Francisco, CA, or of F5
Networks, Inc.,
of Seattle, WA, or of Juniper Networks, Inc., of Sunnyvale, CA.
[0150] In some embodiments, a remote machine 206 executes an application on
behalf of a user of a local machine 202. In other embodiments, a remote
machine 206
executes a virtual machine, which provides an execution session within which
applications
execute on behalf of a user of a local machine 202. In one of these
embodiments, the
execution session is a hosted desktop session. In another of these
embodiments, the
execution session provides access to a computing environment, which may
comprise one or
more of: an application, a plurality of applications, a desktop application,
and a desktop
session in which one or more applications may execute.
[01511 In some embodiments, a local machine 202 communicates with a remote
machine 206. In one embodiment, the local machine 202 communicates directly
with one of
the remote machines 206 in a server farm 38. In another embodiment, the local
machine 202
executes a program neighborhood application to communicate with a remote
machine 206 in
a server farm 38. In still another embodiment, the remote machine 206 provides
the
functionality of a master node. In some embodiments, the local machine 202
communicates
with the remote machine 206 in the server farm 38 through a network 204. Over
the network
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204, the local machine 202 can, for example, request execution of various
applications hosted
by the remote machines 206a-206n in the server farm 38 and receive output of
the results of
the application execution for display. In some embodiments, only a master node
provides the
functionality required to identify and provide address information associated
with a remote
machine 206b hosting a requested application.
[0152] In one embodiment, the remote machine 206 provides the functionality
of a
web server. In another embodiment, the remote machine 206a receives requests
from the
local machine 202, forwards the requests to a second remote machine 206b and
responds to
the request by the local machine 202 with a response to the request from the
remote machine
206b. In still another embodiment, the remote machine 206a acquires an
enumeration of
applications available to the local machine 202 and address information
associated with a
remote machine 206b hosting an application identified by the enumeration of
applications. In
yet another embodiment, the remote machine 206 presents the response to the
request to the
local machine 202 using a web interface. In one embodiment, the local machine
202
communicates directly with the remote machine 206 to access the identified
application. In
another embodiment, the local machine 202 receives output data, such as
display data,
generated by an execution of the identified application on the remote machine
206.
101531 In some embodiments, the remote machine 206 or a server farm 38 may
be
running one or more applications, such as an application providing a thin-
client computing or
remote display presentation application. In one embodiment, the remote machine
206 or
server farm 38 executes as an application any portion of the CITRIX ACCESS
SUITE by
Citrix Systems, Inc., such as the METAFRAME or CITRIX PRESENTATION SERVER
products, any of the following products manufactured by Citrix Systems, Inc.:
CITRIX
XENAPP, CITRIX XENDESKTOP, CITRIX ACCESS GATEWAY, and/or any of the
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MICROSOFT WINDOWS Terminal Services manufactured by the Microsoft Corporation.
In another embodiment, the application is an ICA client, developed by Citrix
Systems, Inc. of
Fort Lauderdale, Florida. In still another embodiment, the remote machine 206
may run an
application, which, for example, may be an application server providing email
services such
as MICROSOFT EXCHANGE manufactured by the Microsoft Corporation of Redmond,
Washington, a web or Internet server, or a desktop sharing server, or a
collaboration server.
In yet another embodiment, any of the applications may comprise any type of
hosted service
or products, such as GOTOMEETING provided by Citrix Online Division, Inc. of
Santa
Barbara, California, WEBEX provided by WebEx, Inc. of Santa Clara, California,
or
Microsoft Office LIVE MEETING provided by Microsoft Corporation of Redmond,
Washington.
[0154] A local machine 202 may execute, operate or otherwise provide an
application, which can be any type and/or form of software, program, or
executable
instructions such as any type and/or form of web browser, web-based client,
client-server
application, a thin-client computing client, an ActiveX control, or a Java
applet, or any other
type and/or form of executable instructions capable of executing on local
machine 202. In
some embodiments, the application may be a server-based or a remote-based
application
executed on behalf of the local machine 202 on a remote machine 206. In other
embodiments, the remote machine 206 may display output to the local machine
202 using
any thin-client protocol, presentation layer protocol, or remote-display
protocol, such as the
Independent Computing Architecture (ICA) protocol manufactured by Citrix
Systems, Inc. of
Ft. Lauderdale, Florida; the Remote Desktop Protocol (RDP) manufactured by the
Microsoft
Corporation of Redmond, Washington; the X11 protocol; the Virtual Network
Computing
(VNC) protocol, manufactured by AT&T Bell Labs; the SPICE protocol,
manufactured by
Qumranet, Inc., of Sunnyvale, CA, USA, and of Raanana, Israel; the Net2Display
protocol,
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manufactured by VESA, of Milpitas, CA; the PC-over-IP protocol, manufactured
by Teradici
Corporation, of Burnaby, B.C.; the TCX protocol, manufactured by Wyse
Technology, Inc.,
of San Jose, CA; the THINC protocol developed by Columbia University in the
City of New
York, of New York, NY; or the Virtual-D protocols manufactured by Desktone,
Inc., of
Chelmsford, MA. The application can use any type of protocol and it can be,
for example, an
HTTP client, an FTP client, an Oscar client, or a Telnet client. In still
other embodiments,
the application comprises any type of software related to voice over Internet
protocol (VoIP)
communications, such as a soft IP telephone. In further embodiments, the
application
comprises any application related to real-time data communications, such as
applications for
streaming video and/or audio.
101551 The local machine 202 and remote machine 206 may be deployed as
and/or
executed on any type and form of computing device, such as a computer, network
device or
appliance capable of communicating on any type and form of network and
performing the
operations described herein. FIGs. 2B and 2C depict block diagrams of a
computing device
200 useful for practicing an embodiment of the local machine 202 or a remote
machine 206.
As shown in FIGs. 2B and 2C, each computing device 200 includes a central
processing unit
221, and a main memory unit 222. As shown in FIG. 2B, a computing device 200
may
include a storage device 228, an installation device 216, a network interface
218, an I/O
controller 223, display devices 224a-n, a keyboard 226 and a pointing device
227, such as a
mouse. The storage device 228 may include, without limitation, an operating
system,
software, and a client agent 220. As shown in FIG. 2C, each computing device
200 may also
include additional optional elements, such as a memory port 203, a bridge 270,
one or more
input/output devices 230a-230n (generally referred to using reference numeral
230), and a
cache memory 240 in communication with the central processing unit 221.
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[0156] The central processing unit 221 is any logic circuitry that responds
to and
processes instructions fetched from the main memory unit 222. In many
embodiments, the
central processing unit 221 is provided by a microprocessor unit, such as:
those manufactured
by Intel Corporation of Mountain View, California; those manufactured by
Motorola
Corporation of Schaumburg, Illinois; those manufactured by Transmeta
Corporation of Santa
Clara, California; the RS/6000 processor, those manufactured by International
Business
Machines of White Plains, New York; or those manufactured by Advanced Micro
Devices of
Sunnyvale, California. The computing device 200 may be based on any of these
processors,
or any other processor capable of operating as described herein.
[0157] Main memory unit 222 may be one or more memory chips capable of
storing
data and allowing any storage location to be directly accessed by the
microprocessor 221,
such as Static random access memory (SRAM), Burst SRAM or SynchBurst SRAM
(BSRAM), Dynamic random access memory (DRAM), Fast Page Mode DRAM (FPM
DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended
Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM),
Enhanced DRAM (EDRAM), synchronous DRAM (SDRAM), JEDEC SRAM, PC100
SDRAM, Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM),
SyncLink DRAM (SLDRAM), Direct Rambus DRAM (DRDRAM), or Ferroelectric RAM
(FRAM). The main memory 222 may be based on any of the above described memory
chips,
or any other available memory chips capable of operating as described herein.
In the
embodiment shown in FIG. 2B, the processor 221 communicates with main memory
222 via
a system bus 250 (described in more detail below). FIG. 2C depicts an
embodiment of a
computing device 200 in which the processor communicates directly with main
memory 222
via a memory port 203. For example, in FIG. 2C the main memory 222 may be
DRDRAM.
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[0158] FIG. 2C depicts an embodiment in which the main processor 221
communicates directly with cache memory 240 via a secondary bus, sometimes
referred to as
a backside bus. In other embodiments, the main processor 221 communicates with
cache
memory 240 using the system bus 250. Cache memory 240 typically has a faster
response
time than main memory 222 and is typically provided by SRAM, BSRAM, or EDRAM.
In
the embodiment shown in FIG. 2B, the processor 221 communicates with various
I/O devices
230 via a local system bus 250. Various buses may be used to connect the
central processing
unit 221 to any of the I/O devices 230, including a VESA VL bus, an ISA bus,
an EISA bus,
a MicroChannel Architecture (MCA) bus, a PCI bus, a PCI-X bus, a PCI-Express
bus, or a
NuBus. For embodiments in which the I/0 device is a video display 224, the
processor 221
may use an Advanced Graphics Port (AGP) to communicate with the display 224.
FIG. 2C
depicts an embodiment of a computer 200 in which the main processor 221
communicates
directly with I/O device 230b via HYPERTRANSPORT, RAPIDIO, or INFINIBAND
communications technology. FIG. 2C also depicts an embodiment in which local
busses and
direct communication are mixed: the processor 221 communicates with I/O device
230a
using a local interconnect bus while communicating with I/O device 230b
directly.
[0159] A wide variety of I/O devices 230a-230n may be present in the
computing
device 200. Input devices include keyboards, mice, trackpads, trackballs,
microphones, and
drawing tablets. Output devices include video displays, speakers, inkjet
printers, laser
printers, and dye-sublimation printers. An I/O controller 223, as shown in
FIG. 28, may
control the I/O devices. The I/O controller may control one or more I/O
devices such as a
keyboard 226 and a pointing device 227, e.g., a mouse or optical pen.
Furthermore, an I/O
device may also provide storage and/or an installation medium 216 for the
computing device
200. In still other embodiments, the computing device 200 may provide USB
connections
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(not shown) to receive handheld USB storage devices such as the USB Flash
Drive line of
devices manufactured by Twintech Industry, Inc. of Los Alamitos, California.
101601 Referring again to FIG. 2B, the computing device 200 may support any
suitable installation device 216, such as a floppy disk drive for receiving
floppy disks such as
3.5-inch, 5.25-inch disks or ZIP disks, a CD-ROM drive, a CD-R/RW drive, a DVD-
ROM
drive, tape drives of various formats, USB device, hard-drive or any other
device suitable for
installing software and programs. The computing device 200 may further
comprise a storage
device, such as one or more hard disk drives or redundant arrays of
independent disks, for
storing an operating system and other related software, and for storing
application software
programs such as any program related to the client agent 220. Optionally, any
of the
installation devices 216 could also be used as the storage device.
Additionally, the operating
system and the software can be run from a bootable medium, for example, a
bootable CD,
such as KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux
distribution from knoppix.net.
[01611 Furthermore, the computing device 200 may include a network
interface 218
to interface to the network 204 through a variety of connections including,
but not limited to,
standard telephone lines, LAN or WAN links (e.g., 802.11, TI, T3, 56kb, X.25,
SNA,
DECNET), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit
Ethernet,
Ethernet-over-SONET), wireless connections, or some combination of any or all
of the
above. Connections can be established using a variety of communication
protocols (e.g.,
TCP/IP, IPX, SPX, NetBIOS, Ethernet, ARCNET, SONET, SDH, Fiber Distributed
Data
Interface (FDDI), RS232, IEEE 802.11, IEEE 802.11a, IEEE 802.11b, IEEE
802.11g,
CDMA, GSM, WiMax and direct asynchronous connections). In one embodiment, the
computing device 200 communicates with other computing devices 200' via any
type and/or
CA 02800722 2013-01-04
form of gateway or tunneling protocol such as Secure Socket Layer (SSL) or
Transport Layer
Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems,
Inc. of Ft.
Lauderdale, Florida. The network interface 218 may comprise a built-in network
adapter,
network interface card, PCMCIA network card, card bus network adapter,
wireless network
adapter, USB network adapter, modem or any other device suitable for
interfacing the
computing device 200 to any type of network capable of communication and
performing the
operations described herein.
[0162] In some embodiments, the computing device 200 may comprise or be
connected to multiple display devices 224a-224n, which each may be of the same
or different
type and/or form. As such, any of the I/O devices 230a-230n and/or the I/O
controller 223
may comprise any type and/or form of suitable hardware, software, or
combination of
hardware and software to support, enable or provide for the connection and use
of multiple
display devices 224a-224n by the computing device 200. For example, the
computing device
200 may include any type and/or form of video adapter, video card, driver,
and/or library to
interface, communicate, connect or otherwise use the display devices 224a-
224n. In one
embodiment, a video adapter may comprise multiple connectors to interface to
multiple
display devices 224a-224n. In other embodiments, the computing device 200 may
include
multiple video adapters, with each video adapter connected to one or more of
the display
devices 224a-224n. In some embodiments, any portion of the operating system of
the
computing device 200 may be configured for using multiple displays 224a-224n.
In other
embodiments, one or more of the display devices 224a-224n may be provided by
one or more
other computing devices, such as computing devices 200a and 200b connected to
the
computing device 200, for example, via a network. These embodiments may
include any
type of software designed and constructed to use another computer's display
device as a
second display device 224a for the computing device 200. One ordinarily
skilled in the art
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will recognize and appreciate the various ways and embodiments that a
computing device
200 may be configured to have multiple display devices 224a-224n.
[0163] In further embodiments, an I/O device 230 may be a bridge between
the
system bus 250 and an external communication bus, such as a USB bus, an Apple
Desktop
Bus, an RS-232 serial connection, a SCSI bus, a FireWire bus, a FireWire 800
bus, an
Ethernet bus, an AppleTalk bus, a Gigabit Ethernet bus, an Asynchronous
Transfer Mode
bus, a HIPPI bus, a Super HIPPI bus, a SerialPlus bus, a SCl/LAMP bus, a
FibreChannel bus,
or a Serial Attached small computer system interface bus, or any other type
and form of
communication bus.
[0164] A computing device 200 of the sort depicted in FIGs. 2B and 2C
typically
operates under the control of operating systems, which control scheduling of
tasks and access
to system resources. The computing device 200 can be running any operating
system such as
any of the versions of the MICROSOFT WINDOWS operating systems, the different
releases
of the Unix and Linux operating systems, any version of the MAC OS for
Macintosh
computers, any embedded operating system, any real-time operating system, any
open source
operating system, any proprietary operating system, any operating systems for
mobile
computing devices, or any other operating system capable of running on the
computing
device and performing the operations described herein. Typical operating
systems include,
but are not limited to: WINDOWS 3.x, WINDOWS 95, WINDOWS 98, WINDOWS 2000,
WINDOWS NT 3.51, WINDOWS NT 4.0, WINDOWS 7, WINDOWS CE, WINDOWS XP,
and WINDOWS VISTA, all of which are manufactured by Microsoft Corporation of
Redmond, Washington; MAC OS, manufactured by Apple Inc., of Cupertino,
California;
OS/2, manufactured by International Business Machines of Armonk, New York; and
Linux, a
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freely-available operating system distributed by Caldera Corp. of Salt Lake
City, Utah, or any
type and/or form of a Unix operating system, among others.
[0165] The computing device 200 can be any workstation, desktop computer,
laptop
or notebook computer, server, handheld computer, mobile telephone or other
portable
telecommunication device, media playing device, a gaming system, mobile
computing
device, or any other type and/or form of computing, telecommunications or
media device that
is capable of communication and that has sufficient processor power and memory
capacity to
perform the operations described herein. In some embodiments, the computing
device 200
may have different processors, operating systems, and input devices consistent
with the
device. For example, in one embodiment, the computing device 200 is a TREO
180, 270,
600, 650, 680, 700p, 700w/wx, 750, 755p, 800w, Centro, or Pro smart phone
manufactured
by Palm, Inc. In some of these embodiments, the TREO smart phone is operated
under the
control of the PalmOS operating system and includes a stylus input device as
well as a five-
way navigator device.
[0166] In other embodiments the computing device 200 is a mobile device,
such as a
JAVA-enabled cellular telephone or personal digital assistant (PDA), such as
the i55sr, i58sr,
185s, i88s, 190c, i95c1, i335, i365, i570, 1576, i580, i615, i760, i836, i850,
i870, i880, i920,
i930, ic502, ic602, ic902, i776 or the im1100, all of which are manufactured
by Motorola
Corp. of Schaumburg, Illinois, the 6035 or the 7135, manufactured by Kyocera
of Kyoto,
Japan, or the i300 or i330, manufactured by Samsung Electronics Co., Ltd., of
Seoul, Korea.
In some embodiments, the computing device 200 is a mobile device manufactured
by Nokia
of Finland, or by Sony Ericsson Mobile Communications AB of Lund, Sweden.
[0167] In still other embodiments, the computing device 200 is a Blackberry
handheld
or smart phone, such as the devices manufactured by Research In Motion
Limited, including
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the Blackberry 7100 series, 8700 series, 7700 series, 7200 series, the
Blackberry 7520, the
Blackberry PEARL 8100, the 8700 series, the 8800 series, the Blackberry Storm,
Blackberry
Bold, Blackberry Curve 8900, and the Blackberry Pearl Flip. In yet other
embodiments, the
computing device 200 is a smart phone, Pocket PC, Pocket PC Phone, or other
handheld
mobile device supporting Microsoft Windows Mobile Software. Moreover, the
computing
device 200 can be any workstation, desktop computer, laptop or notebook
computer, server,
handheld computer, mobile telephone, any other computer, or other form of
computing or
telecommunications device that is capable of communication and that has
sufficient processor
power and memory capacity to perform the operations described herein.
[0168] In some embodiments, the computing device 200 comprises a
combination of
devices, such as a mobile phone combined with a digital audio player or
portable media
player. In one of these embodiments, the computing device 200 is a Motorola
RAZR or
Motorola ROKR line of combination digital audio players and mobile phones. In
another of
these embodiments, the computing device 200 is a device in the iPhone line of
smartphones,
manufactured by Apple Inc., of Cupertino, California. In still other
embodiments, the
computing device 200 may comprise a tablet computer, such as an iPad tablet
computer
manufactured by Apple, Inc., or any other type and form of tablet computer.
[0169] In one embodiment, a computing device 202a may request resources
from a
remote machine 206, while providing the functionality of a remote machine 206
to a client
202b. In such an embodiment, the computing device 202a may be referred to as a
client with
respect to data received from the remote machine 206 (which may be referred to
as a server)
and the computing device 202a may be referred to as a server with respect to
the second
client 202b. In another embodiment, the client 202 may request resources from
the remote
machine 206 on behalf of a user of the client 202.
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[0170] As shown in FIG. 2D, the computing device 200 may comprise multiple
processors and may provide functionality for simultaneous execution of
instructions or for
simultaneous execution of one instruction on more than one piece of data. In
some
embodiments, the computing device 200 may comprise a parallel processor with
one or more
cores. In one of these embodiments, the computing device 200 is a shared
memory parallel
device, with multiple processors and/or multiple processor cores, accessing
all available
memory as a single global address space. In another of these embodiments, the
computing
device 200 is a distributed memory parallel device with multiple processors
each accessing
local memory only. In still another of these embodiments, the computing device
200 has
both some memory which is shared and some memory which can only be accessed by
particular processors or subsets of processors. In still even another of these
embodiments, the
computing device 200, such as a multicore microprocessor, combines two or more
independent processors into a single package, often a single integrated
circuit (IC). In yet
another of these embodiments, the computing device 200 includes a chip having
a CELL
BROADBAND ENGINE architecture and including a Power processor element and a
plurality of synergistic processing elements, the Power processor element and
the plurality of
synergistic processing elements linked together by an internal high speed bus,
which may be
referred to as an element interconnect bus.
[0171] In some embodiments, the processors provide functionality for
execution of a
single instruction simultaneously on multiple pieces of data (SIMD). In other
embodiments,
the processors provide functionality for execution of multiple instructions
simultaneously on
multiple pieces of data (MIMD). In still other embodiments, the processor may
use any
combination of SIMD and MIMD cores in a single device.
CA 02800722 2013-01-04
[0172] In some embodiments, the computing device 200 may comprise a
graphics
processing unit. In one of these embodiments, depicted in FIG. 2E, the
computing device
200 includes at least one central processing unit 221 and at least one
graphics processing unit.
In another of these embodiments, the computing device 200 includes at least
one parallel
processing unit and at least one graphics processing unit. In still another of
these
embodiments, the computing device 200 includes a plurality of processing units
of any type,
one of the plurality of processing units comprising a graphics processing
unit.
[01731 In one embodiment, a resource may be a program, an application, a
document,
a file, a plurality of applications, a plurality of files, an executable
program file, a desktop
environment, a computing environment, or other resource made available to a
user of the
local computing device 202. The resource may be delivered to the local
computing device
202 via a plurality of access methods including, but not limited to,
conventional installation
directly on the local computing device 202, delivery to the local computing
device 202 via a
method for application streaming, delivery to the local computing device 202
of output data
generated by an execution of the resource on a third computing device 206b and
communicated to the local computing device 202 via a presentation layer
protocol, delivery
to the local computing device 202 of output data generated by an execution of
the resource
via a virtual machine executing on a remote computing device 206, or execution
from a
removable storage device connected to the local computing device 202, such as
a USB
device, or via a virtual machine executing on the local computing device 202
and generating
output data. In some embodiments, the local computing device 202 transmits
output data
generated by the execution of the resource to another client computing device
202b.
[0174[ In some embodiments, a user of a local computing device 202 connects
to a
remote computing device 206 and views a display on the local computing device
202 of a
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local version of a remote desktop environment, comprising a plurality of data
objects,
generated on the remote computing device 206. In one of these embodiments, at
least one
resource is provided to the user by the remote computing device 206 (or by a
second remote
computing device 206b) and displayed in the remote desktop environment.
However, there
may be resources that the user executes on the local computing device 202,
either by choice,
or due to a policy or technological requirement. In another of these
embodiments, the user of
the local computing device 202 would prefer an integrated desktop environment
providing
access to all of the resources available to the user, instead of separate
desktop environments
for resources provided by separate machines. For example, a user may find
navigating
between multiple graphical displays confusing and difficult to use
productively. Or, a user
may wish to use the data generated by one application provided by one machine
in
conjunction with another resource provided by a different machine. In still
another of these
embodiments, requests for execution of a resource, windowing moves,
application
minimize/maximize, resizing windows, and termination of executing resources
may be
controlled by interacting with a remote desktop environment that integrates
the display of the
remote resources and of the local resources. In yet another of these
embodiments, an
application or other resource accessible via an integrated desktop environment
¨ including
those resources executed on the local computing device 202 and those executed
on the remote
computing device 206 ¨ is shown in a single desktop environment.
[0175] In one embodiment, data objects from a remote computing device 206
are
integrated into a desktop environment generated by the local computing device
202. In
another embodiment, the remote computing device 206 maintains the integrated
desktop. In
still another embodiment, the local computing device 202 maintains the
integrated desktop.
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[01761 In some embodiments, a single remote desktop environment 204 is
displayed.
In one of these embodiments, the remote desktop environment 204 is displayed
as a full-
screen desktop. In other embodiments, a plurality of remote desktop
environments 204 is
displayed. In one of these embodiments, one or more of the remote desktop
environments are
displayed in non-full-screen mode on one or more display devices 224. In
another of these
embodiments, the remote desktop environments are displayed in full-screen mode
on
individual display devices. In still another of these embodiments, one or more
of the remote
desktop environments are displayed in full-screen mode on one or more display
devices 224.
101771 Referring now to FIG. 3A, illustrated is a block diagram of a system
for
multivariate analysis of adverse event data. In brief overview, a client 300
may comprise an
application 302 and, in some embodiments, genomic information 303. In some
embodiments, a client 300 may communicate with a server 304 via any type of
network, such
as those discussed herein. Although shown as a separate client-server system,
in many
embodiments, a client 300 and server 304 may be on the same physical machine.
In other
embodiments, server 304 may be executed by a virtual machine provided by a
cloud
computing environment. For example, server 304 may comprise a hosted service
or cloud
service, providing scalability and ease of management. In some embodiments, a
medical
literature server 340 and/or an adverse event data server 342 may also
communicate with a
server 304. In other embodiments not shown, a second client 300 may be used to
gather data
from a medical literature server 340 and/or an adverse event data server 342
and processed or
transferred to server 304. In some embodiments, a server 304 may comprise an
input/output
interface 306, a security module 308, and/or a display module 310. Server 304
may also
comprise one or more databases or data stores, including an adverse event
database 312, a
medication information database 314, a literature database 316, and a variant
database 318.
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Server 304 may, in some embodiments, comprise an analyzer 320 and/or a parser
322. In
some embodiments, server 304 may comprise a global molecular entity graph 324.
[0178] Still referring to FIG. 3A and in more detail, in some embodiments,
a client
300 may comprise a computing device of any type, such as a desktop computer,
portable
computer, smart phone, tablet computer, or any other type of computing device.
Client 300
may execute an application 302 for accessing server 304. In some embodiments,
application
302 may comprise a web browser, while in other embodiments, application 302
may
comprise a dedicated application for communicating with server 304.
[0179] In some embodiments, client 300 may store, include, or otherwise
access
genomic information 303. Genomic information 303 may comprise genetic data
about a
patient. For example, in some embodiments, genomic information 303 may
comprise a list of
genetic variants or mutations of the patient, a full or partial genetic
sequence, or any similar
information. In some embodiments, genomic information 303 may be utilized for
generating
personalized drug efficacy or risk information or identifying potential drug
interactions.
Although shown on client 300, in many embodiments, genomic information 303 may
be
stored externally to client 300, obtained from a third party or stored on a
second server or
network storage device, or otherwise be supplied to server 304.
[0180] Server 304 may comprise a computing device of any type, such as a
desktop
computer, portable computer, rackmount server, workstation, or any other type
of computing
device. In some embodiments, server 304 may comprise a virtual machine
executed by a
cloud service, a plurality of servers forming a grid or server farm 38 and
acting as a single
server 304, or any other type of server. Although shown with components 306-
324 as part of
server 304, in many embodiments, one or more of components 306-324 may be
external to
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server 304, on a second server (not illustrated), on an external storage
device, or otherwise
accessible to server 304.
[0181] In some embodiments, server 304 may execute an input/output
interface 306.
Input/output interface 306 may comprise an application, service, daemon,
routine, or other
executable logic for communicating with one or more clients 300 or other
servers, medical
literature servers 340, and/or adverse event data servers 342. In some
embodiments,
input/output interface 306 may comprise a web server or web page executed by a
web server.
Input/output interface 306 may provide an interface allowing a user to provide
queries, make
selections or identifications of drugs, indications, targets, pathways, or
other molecular
entities, define cohorts for analysis, or perform other functions. In some
embodiments,
input/output interface 306 may provide data tables, graphics, or other output
views to the
user. In many embodiments, input/output interface 306 may communicate via a
network with
application 302, while in other embodiments in which client 300 and server 304
comprise the
same computing device, application 302 may be executed on server 304 and may
communicate with input/output interface 306 via an API.
[0182] In some embodiments, server 304 may execute a security module 308.
Security module 308 may comprise an application, service, daemon, routine, or
other
executable logic for receiving user credentials or login information and/or
computing device
credentials, such as a network address, operating system version or other
identification, and
processing the credentials to allow or deny access to server 304. Security
module 308 may,
in some embodiments, comprise a user and password database or similar features
to control
access to functions of server 304.
[0183] In some embodiments, server 304 may execute a display module 310.
Display
module 310 may comprise an application, service, daemon, routine, or other
executable logic
CA 02800722 2013-01-04
for generating graphic displays for presentation by input/output interface 306
and/or
application 302 to a user. In some embodiments, display module 310 may
generate graphs,
tables, radial graphs, charts, biological network diagrams, or other graphical
entities. In some
embodiments, input/output interface 306 and display module 310 may be provided
as part of
a web server or application, while in other embodiments, these services may
comprise
separate executable modules.
101841 Server 304 may include an adverse event database 312 and/or a
medication
information database 314. In some embodiments, adverse event database 312
and/or
medication information database 314 may be stored on server 304, while in
other
embodiments, adverse event database 312 and/or medication information database
314 may
be stored on a data storage server, external storage device, within a cloud
storage system, or
otherwise accessible to parser 322 and/or analyzer 320. An adverse event
database 312 may
comprise a database, flat file, data array, or other data file for storing
molecular data
regarding adverse events. Similarly, a medication information database 314 may
comprise a
database, flat file, data array, or other data file for storing molecular
entity information for
one or more drugs. As discussed above in connection with FIG. 1B, stored data
may
comprise identifications of one or more drugs 102, indications 104, reactions
106, outcomes
108, pathways 110, targets 112, metabolizing enzymes or transporters 114, and
drug classes
116. In many embodiments, adverse event data may comprise demographic
information of a
patient, trial participant, or other person that experienced the adverse
event. In many
embodiments, adverse event data 102-108 from adverse event reporting systems
may be
combined and linked with molecular entity data 110-116 in the adverse event
database 312
and/or medication information database 314. In some embodiments, molecular
entity data
110-116 for a drug may be retrieved from pharmaceutical manufacturer
literature, research
literature or white papers, or other literature from one or more medical
literature servers 340.
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In many embodiments, adverse event database 312 and medication information
database 314
may comprise a single database, while in other embodiments, databases 312-314
may be
linked to allow associations between entities and adverse event data. In some
embodiments,
associations may be one-to-one, such as a single outcome for a single patient,
while in other
embodiments, associations may be one-to-many, such as a plurality of
prescribed and co-
prescribed drugs for the patient, or many-to-many, such as a plurality of
indications
associated with each of a plurality of drugs. Accordingly, a adverse
event/molecular entity
database comprising adverse event database 312 and medication information
database 314
may comprise a multi-dimensional database allowing associations between
adverse events
and biological information. Such a database may be used for novel univariate
analyses, such
as generating an ordered list of metabolizing enzymes most frequently
associated with a
specified side effect (by numbers of adverse event reports for the side effect
or reaction
including a drug, the drug associated with the metabolizing enzyme in medical
literature).
Similarly, such a database may be used for multivariate analyses, such as
comparing reported
side effects of all drugs targeting a first protein with side effects of all
drugs targeting a
second protein.
[0185] In some
embodiments, medication information database 314 may comprise or
be associated with a literature database 316. Literature database 316 may
comprise a
database, data array, flat file, or other data comprising one or more items of
literature about
one or molecular entities. Literature database 316 may comprise white papers,
research
papers, theses, dissertations, abstracts of literature, publicly available
literature, proprietary
manufacturer literature, research data, or other literature. In some
embodiments, literature
database 316 may comprise medication information, which may be extracted to
generate a
medication information database 314. In some embodiments, a server 304 may
retrieve or
receive literature from one or more medical literature servers 340. For
example, in one
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embodiment, server 304 may retrieve abstracts or full papers from the PubMed
database
provided by the National Institutes of Health of Bethesda, Maryland. Such
papers or
abstracts may be parsed to identify drug names, drug classes, protein targets,
metabolizing
enzymes, transporters, gene variants or wild types, or other molecular
entities. Once
identified, the entities and associations between identified entities may be
added to literature
database 316, medication information database 314, adverse event database 312,
or a
combined multi-dimensional molecular data database.
101861 In some embodiments, adverse event database 312 may further comprise
identification of patient genetic variants or mutations, or may be associated
with a variant
database 318. A variant database may comprise a database, data file, flat
file, data array, or
other file comprising a full genetic sequence for one or more patients,
clinical trial
participants, or other persons, or may comprise a partial sequence, or may
comprise an
identification of one or more variants or mutated gene sequences for a
patient, participant, or
person. In some embodiments, a variant database may further comprise
identifications of one
or more proteins corresponding to a variant, in which expression or activation
of the protein
is affected by the mutation. For example, in one such embodiment, a database
may comprise
an identification of a variant and an identification of a protein activated by
the wild type
corresponding to the variant. By linking variant identifications, protein
activation or
deactivation, and drug target proteins, a user may identify potential
decreased efficacy of a
drug or high risk biological interactions.
10187] In some embodiments, a server 304 may comprise an analyzer or
analysis
module 320. Analyzer 320 may comprise an application, service, daemon,
routine, or other
executable logic for performing univariate or multivariate analysis. In some
embodiments,
analyzer 320 may identify associated entities from a database, such as
reactions associated
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with a target protein, or outcomes associated with a genetic variant. In many
embodiments,
analyzer 320 may generate one or more lists of associated entities based on an
input or
requested first entity. Such lists may be ordered, for example, by a
percentage of total
associations or by number of associations in the database. Accordingly, for a
query of
adverse reactions associated with a first drug, analyzer 320 may return an
ordered list
indicating that, for example, of all reported adverse reactions associated
with the first drug,
nausea occurs in 60% of cases, fatigue occurs in 50% of cases, and a rash
occurs in 40% of
cases. Due to the possibility of patients experiencing multiple adverse
events, totals may
exceed 100%. Similarly, for a query of targets associated with an adverse
reaction such as
fatigue, analyzer 320 may return a list of molecular targets ordered by
proportional reporting
ratio (PRR), such as dihydroorotase having a PRR of 32.91, DNA polymerase i
having a PRR
of 16.45, and cytochrome b having a PRR of 8.22. Such proportional reporting
rations may
be determined based on a proportion of reactions to the molecular entity
compared to the
same proportion for all such entities in the database. In some embodiments,
analyzer 320
may further comprise functionality for performing multivariate analyses and
comparisons.
For example, analyzer 320 may comprise logic for extracting subsets of
statistical data of
adverse events associated experienced by an identified first cohort of
patients or trial
participants and an identified second cohort, and comparing the two subsets to
identify
adverse event differences between the cohorts. Phenotype or genotype
distinctions between
the cohorts may then be identified as the likely cause or mitigation of
adverse events.
[0188] In some
embodiments, server 304 may comprise a parser 322. Parser 322 may
comprise an application, service, daemon, routine, or other executable logic
for reading and
interpreting medical literature obtained from a medical literature server 340
or stored in a
literature database 316. Reading and interpreting medical literature may
comprise scanning
literature for identifications of one or more molecular entities. Inclusion of
identifications of
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a plurality of entities within a single item of literature may indicate an
association between
those entities. Such associations may then be incorporated into a medication
information
database 314 and/or adverse event database 312. For example, parser 322 may
scan medical
literature and identify that the terms "headache" and "aspirin" frequently
appear in the same
items of literature. Accordingly, parser 322 may identify the indication
"headache" as related
to the drug "aspirin" in a medication information database 314. Similarly, in
some
embodiments, parser 322 may identify associations within literature between
drugs, targets,
transporters, metabolizing enzymes, drug classes, genetic variants, side
effects, indications,
reactions, outcomes, patient demographic information, or any other such
information. Parser
322 may scan white papers, abstracts, articles, theses, research documents,
manufacturer
literature, or any other type of document for associations between molecular
entities. In some
embodiments, parser 322 may score the identified associations responsive to
one or more
factors, such as frequency, proximity, and secondary citations. For example,
parser 322 may
give a low association score to two molecular entities that appear in only a
single item of
literature once. However, parser 322 may give a higher association score to
the two
molecular entities, if they appear in close proximity to each other within the
literature, such
as in the same sentence or paragraph. In some embodiments, parser 322 may give
a higher
association score to associations between two entities that appear in a
plurality of items of
literature than an association between two entities that appears repeatedly in
only a single
item of literature. In such embodiments, parser 322 may thus identify
associations that are
commonly understood by researchers, rather than unconfirmed or proposed
associations. In
some embodiments, parser 322 may further identify secondary items of
literature that cite a
first item of literature, and give a higher score to associations identified
within the first item
of literature. Frequently cited literature thus may become more authoritative
regarding
associations.
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[0189] In some embodiments, server 304 may comprise a global molecular
entity
graph 324. Global molecular entity graph 324 may comprise a graph, database,
or other data
file for identifying a plurality of molecular entities and relationships
between entities. Global
molecular entity graph 324 may comprise a system-wide representation of some
or all
biological systems within the human body. For example, referring briefly to
FIG. 3B,
illustrated is a diagram of an example embodiment of a global molecular entity
graph 324.
The graph may comprise a plurality of molecular entities 350, such as
proteins, enzymes,
transporters, or other entities, and each entity 350 may be associated with
one or more other
entities 350 via a relationship 352. In some embodiments, a global molecular
entity graph
324 may be used by an analyzer 320 to extract subgraphs 354, which may
comprise portions
of the molecular entity graph important to a particular entity. For example, a
subgraph 354
may comprise all entities and relationships between entities associated with a
first identified
entity, such as a drug target. In some embodiments, multiple subgraphs 354 may
be extracted
and compared to identify common entities and/or relationships between the
subgraphs. For
example, referring briefly to FIG. 3C, illustrated is a diagram of an example
embodiment of
two extracted subgraphs, 354a and 354b, intersected to identify an
intersection subgraph
354c. A first subgraph 354a may be extracted for a first drug target (P1), and
a second
subgraph 354b extracted for a second drug target (P2). The intersection
subgraph 354c may
identify one or more molecular entities 350 affected by each of PI and P2.
These dual-
affected entities may be causes of adverse effects experienced when drugs
targeting P1 and
P2 are taken simultaneously, but not experienced when drugs targeting P1 and
P2 are taken
separately. By using multivariate analysis of adverse event data and
extracting subgraphs for
identified entities with disparate adverse event data, server 304 may be able
to identify one or
more molecular entities associated with a particular side effect, even when
the association
would be normally hidden in univariate analyses.
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[0190] Returning to FIG. 3A, in some embodiments, server 304 may
communicate
with a medical literature server 340 and/or an adverse event data server 342.
Medical
literature server 340 may comprise any server, database, online storage
system, cloud storage
device, offline storage system, computing device, or other device for storing
medical
literature, including research documents, theses, white papers, manufacturer
data, or other
literature. In some embodiments, server 304 may access medical literature
server 340 to
retrieve documents to fill literature database 316, medication information
database 314,
variant database 318, or for parsing one or more items of literature via
parser 322 as
discussed above. Similarly, adverse event data server 342 may comprise any
server,
database, online storage system, cloud storage device, offline storage system,
computing
device, or other device for storing adverse event data, such as the Adverse
Event Reporting
System provided by the U.S. Food & Drug Administration. In some embodiments,
server
304 may access an adverse event data server 342 to retrieve records to fill an
adverse event
database 312 or for parsing by parser 322 or analysis by analyzer 320, as
discussed above.
[0191] In some embodiments, a safety profile, sometimes referred to as an
adverse
event profile or side effect profile, may comprise a list of all adverse event
reports associated
with a molecular entity, such as all adverse event reports for a prescribed or
co-prescribed
medication. ln other embodiments, a safety profile may comprise a statistical
table of
adverse event reports associated with a molecular entity, such as a table
identifying frequency
of occurrence of one or more adverse events with patients or trial
participants consuming a
specified drug. A molecular entity multivariate analysis system may be used to
compare the
safety profiles of a plurality of molecular entities, allowing identification
of entities
responsible for adverse event differences between safety profiles. For
example, in some
embodiments, a safety profile for a first drug or medication may be compared
to a safety
profile for a second drug or medication. Similarly, safety profiles may be
generated based on
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molecular entities associated with adverse event reports. For example, a
patient that
experienced an adverse event may have been prescribed a first drug. The first
drug may be
known to target a first protein. Accordingly, by correlating this information
with the adverse
event report, a safety profile for the protein may be generated. Thus, in some
embodiments, a
safety profile for a protein target may be compared to a safety profile for a
second protein
target,
101921 Similarly, safety profiles may be generated and compared for
indications
themselves. Such safety profiles may comprise a list of medications prescribed
or co-
prescribed to patients identified as being treated for the indication. In one
embodiment, such
a list may be ordered by percentage of patients prescribed or co-prescribed
the medication,
while in another embodiment, such a list may be ordered by percentage of
patients prescribed
or co-prescribed the medication who experienced an adverse event, or a
particular outcome or
outcomes. Accordingly, in some embodiments, a multivariate analysis system may
be able to
determine if two similar indications, such as depression and post-partum
depression, have a
different prioritization of drugs responsible for adverse events. Although
discussed primarily
in terms of similar indications, in many embodiments, any two or more
indications may
compared, allowing complex analysis of similarities between apparently diverse
indications.
For example, and referring briefly to FIG. 4A, illustrated is a block diagram
of an
embodiment of a method for identifying molecular entities responsible for
adverse event
differences between indications. A multivariate analysis system may retrieve a
safety profile
for a first indication 402 from adverse event data 400, and may generate a
list of medications
404A-404n ordered by percentage of medication-consumers experiencing an
adverse event
406A-406n. In some embodiments, the list may be ordered by percentage of
medication-
consumers experiencing any adverse event, while in other embodiments, the list
may be
narrowed to include only percentages of medication-consumers experiencing a
specific
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adverse event. Similarly, the multivariate analysis system may retrieve a
second safety
profile for a second indication 402', and may generate a list of medications
404A-404n
ordered by percentage of medication-consumers experiencing an adverse event
406A'-406n'.
In some embodiments, safety profiles may include different medications 404A-
404N,
although in most embodiments, a medication 404A-404n may appear in both safety
profiles.
Additionally, medications may appear in different priorities in each ordered
list, such as
medication 404C and medication 404F in the example lists of FIG. 4A.
Differences in order
may be due to physiological specificities of either indication and their
differential effect on
drug pharmacokinetics or dynamics. Accordingly, through analysis of the
different molecular
entities (e.g. entities 408A-408D) associated with a medication appearing in a
first position in
one safety profile for a first indication and in a second, different position
in another safety
profile for a second indication (e.g. medication 6 404F), molecular entities
affected
differently by each indication may be immediately identified. In many
embodiments, such
second indication may comprise an indication similar to the first. This may
provide
opportunities for more targeted therapies for one or both indications.
Furthermore, when
safety profiles for each of the indication are narrowed by a specific adverse
event, differences
between each safety profile may identify potentially unknown interactions
between molecular
entities associated with the indication and molecular entities associated with
the adverse
event. For example, if a large percentage of patients with a first indication
taking a first
medication experience a specific adverse event, but a small percentage of
patents with a
second indication taking the first medication experience the specific adverse
event, this may
indicate differences between each indications interaction with the molecular
entities
responsible for the adverse event. Although shown ordered by percentage in
FIG. 4A, in
many embodiments, each list may be in any order, with comparisons performed on
percentage values associated with each medication as opposed to order.
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[0193] Referring now to FIG. 4B, illustrated is a flow chart of an
embodiment of a
method for identifying molecular entities responsible for adverse event
differences between
indications. In brief overview, a multivariate analyzer such as analyzer 320
of a computing
device 304 may receive an identification of a first indication at step 422.
The analyzer may
receive an identification of a second indication at step 424. In many
embodiments, the
second indication may be similar to the first indication. At step 426, in some
embodiments as
discussed above, the analyzer may receive an identification of an adverse
event. At step 428,
the analyzer may retrieve from an adverse event database a first list of
medications prescribed
to patients for the first indication, the list comprising percentages of
patients prescribed each
medication who experienced an adverse event. In some embodiments, the list may
be limited
to adverse event data for the identified adverse event, and accordingly, the
list may comprise
percentages of patients prescribed the medication who experienced the
identified adverse
event. At step 430, the analyzer may retrieve from the adverse event database
a second list of
medications prescribed to patients for the second indication, the list
comprising percentages
of patients prescribed each medication who experienced an adverse event. In
some
embodiments, the list may be limited to adverse event data for the identified
adverse event,
and accordingly, the list may comprise percentages of patients prescribed the
medication who
experienced the identified adverse event. At step 432, in some embodiments,
the analyzer
may compare the first list and second list to identify one or more medications
with a different
percentage value in each list. At step 434, the analyzer may retrieve one or
more lists of
molecular entities associated with a corresponding each of the identified one
or more
medications. At step 436, an output module of the computing device may present
the
retrieved one or more lists of molecular entities to the user as lists of
molecular entities
potentially affected by only one of the first indication and the second
indication.
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[0194] Still referring to FIG. 4B and in more detail, at step 422, an
analyzer 320 may
receive an identification of a first indication. As discussed above, an
indication may
comprise a disease, a symptom, an adverse effect, or any other such
circumstance which
indicates the advisability or necessity of a specific medical treatment or
procedure. In some
embodiments, analyzer 320 may receive the identification of a first indication
from an
input/output module, such as a web interface or application interface. In some
embodiments,
a user may select the first indication or input a name of the first indication
into a text entry
field, and an input module may pass the identification of the indication to
the analyzer. In
other embodiments, the user may select the first indication from a list of
indications. In many
embodiments, analyzer 320 may receive the identification of the indication
from a second
computing device operated by or on behalf of the user.
[0195] At step 424, the analyzer may receive an identification of a second
indication.
The second indication may be similar to the first indication, in some
embodiments, while in
other embodiments, the second indication may comprise any indication.
Indications may be
similar if they share symptoms; are subsets of a category of indication (e.g.
different types of
cancer); if they are commonly or functionally associated (e.g. nausea and
vomiting); or via
other similar associations. In some embodiments, indications may be similar if
they are
involve the same pathway, protein, or other molecular entity. In some
embodiments,
analyzer 320 may receive the identification of the second indication from an
input/output
module, such as a web interface or application interface. In some embodiments,
a user may
select the second indication or input a name of the second indication into a
text entry field,
and an input module may pass the identification of the indication to the
analyzer. In other
embodiments, the user may select the second indication from a list of
indications. In many
embodiments, the analyzer may receive the identification of the second
indication from a
second computing device operated by or on behalf of the user.
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[0196] At step 426, in some embodiments, the analyzer may receive an
identification
of an adverse event. In some embodiments, the adverse event may comprise an
adverse event
distinct from the first indication and second indication. The adverse event
may thus be
suspected of being caused by one or more drugs prescribed or co-prescribed to
patients with
the first or second indication. For example, in one embodiment, the two
similar indications
may comprise depression and post-partum depression, and the adverse event may
comprise a
rash. As depression is not typically associated or functionally identified as
causing a rash,
clinicians may suspect that the adverse event is not caused by the indication,
but by a
medication. Thus, in many embodiments, the adverse event may not be an adverse
event
corresponding to one of the indications (e.g. an adverse event of fatigue for
an indication of
chronic fatigue syndrome).
[01971 At step 428, the analyzer may retrieve a first list of medications
prescribed to
patients with the first indication who experienced the identified adverse
event, and a second
list of medications prescribed to patients with the second indication who
experienced the
identified adverse event. Retrieving the lists of medications may comprise
searching an
adverse event database for reports corresponding to the identified adverse
event. Each report
may comprise patient demographic information, an identification of the adverse
event, an
identification of an indication, an identification of an outcome, and an
identification of one or
more medications consumed by the patient. The adverse event database may
comprise a
collated index of adverse events, normalized to be searchable with standard
terms and
definitions (for example, replacing abbreviations with full titles, etc.). In
some embodiments,
the analyzer may retrieve a subset of adverse event reports that include the
identification of
the adverse event. The analyzer may then extract a second subset of adverse
event reports
that include the identification of the first indication, and extract a third
subset of adverse
event reports that include the identification of the second indication. The
analyzer then, in
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some embodiments, may iteratively sort or count the extracted subsets of
adverse event
reports to generate a table of medications identified in the extracted
subsets, sorted by count
or percentage of listing in the extracted subsets. In other embodiments, the
tables may be
unsorted. For example, referring briefly to FIG. 4C, illustrated is a flow
chart of an
embodiment of a method 428 for retrieving a list of medications for an
indication and adverse
event. At step 450, as discussed above, the analyzer may retrieve the first
subset of adverse
event reports for the identified adverse event, and at step 452, the analyzer
may extract a
second subset of adverse event reports from the first subset including the
indication.
Although shown in this order, in many embodiments, these steps may be
reversed. For
example, the analyzer may extract a subset of adverse event reports for the
indication, and
may then extract a further subset of adverse event reports corresponding to
the identified
adverse event. Furthermore, in some embodiments, these steps may be performed
simultaneously as part of a Boolean search.
101981 At step 454 of FIG. 4C, the analyzer may identify a first medication
in the
extracted subset of adverse event reports for the indication and identified
adverse event. At
step 456, the analyzer may then search the extracted subset to identify the
number and/or
percentage of times that the first medication is listed in the adverse event
reports. In some
embodiments, the analyzer may search the extracted subsets for records in
which the first
medication is listed as the medication suspected of causing the identified
adverse reaction as
opposed to being a co-prescribed or concomitant medication, while in other
embodiments, the
analyzer may search the extracted subsets for all appearances of the first
medication. At step
458, the analyzer may add the first medication and the count or percentage to
a list. In some
embodiments, a percentage of the reports in which the medication appears out
of the total
number of adverse event reports for the indication and adverse event may be
more useful,
while in other embodiments, a raw count may be preferred. The list may be
similarly sorted
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by either number. In many embodiments, analyzer may iteratively repeat steps
454-458 for
each additional medication identified in the extracted subset of adverse event
reports. At step
460, in some embodiments utilizing raw counts, the analyzer may determine a
percentage for
each medication as discussed above. In some embodiments, the analyzer may sort
the list by
the identified count or percentage to generate an ordered list. Sorting may be
done through
any sort algorithm, such as a bubble sort, quick sort, merge sort, or any
other type of sorting.
[0199] Returning to FIG. 413, at step 430, the analyzer may retrieve a
second list of
medications for the second indication and the identified adverse event.
Although shown for
step 428 of FIG. 4B, embodiments of the method shown in FIG. 4C may also be
applied to
step 430 for retrieval of the second list of medications. In some embodiments,
steps 428 and
430 may be performed in any order, or simultaneously, such as by a multi-
threaded
processor.
[0200] At step 432, the analyzer may compare the first list and second list
to identify
a medication with a different percentage value in each list. In some
embodiments, if the
medication appears in 90% of adverse event reports for the first indication,
but only 20% of
adverse event reports for the second indication, the difference in percentages
may indicate an
important distinction between the two indications. Accordingly, in many
embodiments, the
analyzer may identify a medication with a difference between the count or
percentage in the
first list and the count or percentage in the second list that is greater than
a predetermined
threshold amount. Such a threshold may be a percentage, such as 5%, 10%, 20%
or any other
value, or may be a number, such as 100 reports, 1000 reports, or any other
value. As
discussed above, in many embodiments, ordering by percentages may be useful
for certain
comparisons, such as where a first indication has a greater number of adverse
event reports
than a second indication. In such embodiments, percentages may be more easily
compared
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than raw counts. In other embodiments, the analyzer may determine differences
based on
each medication's position in each list, the list being ordered by percentage
or count. This
may be useful in embodiments in which raw counts are used, for example. In
similar
embodiments, the list may comprise an index number for each entry, and the
analyzer may
compare index numbers of a medication in both lists.
[0201] At step 434, in some embodiments, the analyzer may retrieve a third
list of
molecular entities associated with the identified medication from a medication
information
database. As discussed above, in some embodiments, a medication information
database may
comprise part of or be joined with an adverse event database. The medication
information
database may identify a medication and known targets, pathways, enzymes,
transporters, or
other molecular entities associated with the medication.
[0202] At step 436, in some embodiments, the analyzer may present the
retrieved
third list to the user as a list of molecular entities potentially affected by
only one of the first
indication and the second indication. As discussed above, if a first
indication causes
activation of a particular protein and a second indication does not, and a
medication's
interaction with the activated protein causes the adverse effect, such adverse
effect
differences may be detected in the adverse event reports, indicating that the
first indication
and second indication interact with the molecular entities affected by the
medication in
different ways. This may be useful in identifying potential avenues for
research for the two
indications.
[0203] In some embodiments, the analyzer may repeat steps 432-434 for
additional
medications appearing in both the first list and second list. In one such
embodiment, the
analyzer may present a plurality of lists of molecular entities for each
identified medication,
while in other embodiments, the analyzer may merge the lists of molecular
entities. In one
CA 02800722 2013-01-04
embodiment, the analyzer may generate a combined list including all molecular
entities in
each retrieved list, while in other embodiments, the analyzer may generate an
intersection list
including only molecular entities in all retrieved lists. In still other
embodiments, the
analyzer may generate a combined list comprising a score for each molecular
entity. In one
embodiment, each score may comprise a default score. The analyzer may increase
the default
score for each molecular entity appearing in a plurality of lists and/or
decrease the default
score for each molecular entity appearing in one list. In some embodiments,
each molecular
entity may be scored responsive to the number of retrieved lists in which it
appears. This
may be used to generate a priority of which molecular entities are most likely
associated with
the adverse event rate differences. With a greater number of medications
inducing or
suppressing an adverse effect at a different rate in each indication, the
analyzer may be able
to generate more accurate priorities of molecular entities associated with the
adverse event
rate differences.
[0204] As discussed above, in some embodiments, a computing device may
comprise
global molecular entity graph. Such a graph may comprise a linked network of
nodes
representing molecular entities, such as proteins or enzymes, and functional
interactions
between the entities, such as a link between an enzyme and an organic compound
catalyzed
by the enzyme. In some embodiments, the graph may comprise a hypergraph with
edges
connecting to more than two nodes, while in other embodiments, the graph may
comprise a
two-dimensional graph with intermediate reaction nodes.
[0205] A global molecular entity graph may be used for identifying
molecular entities
associated with a side effect or indication and building an indication or side
effect-specific
model of molecular interactions. Although the global molecular entity graph is
not indication
or side effect specific, an analyzer may extract subgraphs or subnetworks from
the global
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molecular entity graph to generate a model of entities related to a specified
indication.
Building an indication or side effect specific molecular entity model may
allow for targeted
pharmacological research regarding entities previously unassociated with the
indication or
side effect. In some embodiments, the analyzer may utilize an adverse event
database to
identify medications associated with the specified indication and/or adverse
event. The
analyzer may then use a medication information database to identify molecular
entities, such
as a proteins and enzymes, related to the identified medications. In other
embodiments, as
discussed above, medication information may be integrated into the adverse
event database
such that each adverse event record further includes or is linked to
identifications of
molecular entities associated with the prescribed or consumed medications of
the patient that
experienced the adverse event. Accordingly, in such embodiments, the analyzer
may utilize
the database to identify molecular entities associated with the specified
indication and/or
adverse event. In some embodiments, the analyzer may identify molecular
entities or
medications that are most highly associated with the selected indication or
side effect. For
example, as discussed above, in some embodiments, the analyzer may sort a
retrieved list of
medications or molecular entities associated with adverse event reports for
the selected
indication or side effect. In a further embodiment, the analyzer may discard
medications or
molecular entities with a count or percentage below a predetermined threshold.
For example,
in building a side effect-specific model, it may be advantageous to focus on
molecular
entities associated with the side effect in more than 50% of the adverse event
reports for the
side effect, and discard entities in fewer than 50% of the reports. The
predetermined
threshold may be any value, and, in some embodiments, may even include 0% or
100%,
either allowing in all associated entities, or restricting to entities that
appear in every adverse
event record. Medications or entities may be sorted and ordered by various
statistical
techniques, including proportional reporting ratios (PRR), regularized PRR
(normalized such
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that older medications do not outweigh newer medications in the adverse event
reports
merely due to amount of data collected, for example), logistic regression, or
other algorithms.
[0206] In many
embodiments, the molecular entities identified at this stage may
include only entities known to be associated with the identified medications.
For example,
the entities may include known target proteins, but may not include unknown
off-target
proteins or intermediate molecular entities involved in catalyzing or
metabolizing the
medication. Furthermore, as multiple medications may be associated with an
indication or
side effect, the identified entities may comprise disjoint regions of the
global molecular entity
graph. For example, referring briefly to FIG. 5A, illustrated is a chart
diagram of an
embodiment of a global molecular entity graph 500. Multiple molecular entities
or nodes
may be linked to show functional interaction. A first subset of entities 502
may be known to
be associated with a first medication, and a second subset of entities 504 may
be known to be
associated with a second medication, the first medication and second
medication associated
with a selected indication or side effect. Including only the subsets 502 and
504 may
comprise an incomplete list of the entities responsible for or associated with
experiencing the
selected indication or side effect.
[0207] Accordingly,
the global molecular entity graph may be used to expand or
augment the identified set of entities by identifying additional entities
functionally related to
known and identified entities, such as subsets 502 and 504. In one embodiment,
the set of
entities may be augmented by performing a shortest path analysis between
disjoint pairs of
known entities, such as a first entity identified as associated with a first
medication (e.g.
subset 502) and a second entity identified as associated with a second
medication (e.g. subset
504). In some embodiments, edges between nodes may be weighted based on
relationships to
other entities. For example, edges to an intermediate node between two
entities may be more
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heavily weighted if the intermediate node is further connected to a second
intermediate node
between both entities. In other embodiments, edges between nodes may be
weighted
responsive to identification of the node as related to an organ associated
with the side effect
or indication, such as aspartate transaminase (AST) being related to the liver
with an
indication of hepatitis. Accordingly, weights may vary depending on the
identified indication
or side effect. The analyzer may perform any type or form of shortest path
analysis,
including Dijkstra's algorithm, a Bellman-Ford algorithm, or any other type
and form of
routing algorithm. Such analysis may, for example, indicate to include
entities 506 and not
include entities 508 in the example embodiment of FIG. 5A.
[0208] In other embodiments, the set of entities may be augmented by
scoring nodes
in the global molecular entity graph with respect to their inclusion in a
subnetwork with
desired properties. In one embodiment, modifying scores may include increasing
scores
related to an organ associated with the indication or side effect and reducing
scores of
unrelated nodes. In another embodiment, scores may be modified by increasing
scores of
nodes well connected to other nodes within the subnetwork and decreasing
scores of nodes
well connected to other nodes external to the subnetwork. This may minimize
connectivity to
the remainder of the network, reducing the likelihood of false positives and,
if incorporated
with the above discussed embodiments, decreasing complexity of a shortest path
analysis.
[0209] In still other embodiments, pre-defined pathways within the global
molecular
entity network (e.g. glycolysis, cAMP-dependent pathway, etc.) may be scored
with respect
to their coverage of the indication-relevant entities or entities known to be
associated with
identified medications associated with the indication or side effect. Merging
high-scoring
pathways may thus allow generating an indication-specific subnetwork.
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[0210] Referring now to FIG. 5B, illustrated is a flow diagram of an
embodiment of a
method for extracting an indication-specific model from a global molecular
entity graph. In
brief overview at step 522, an analyzer or an input/output module in
communication with an
analyzer may receive an identification of an indication or side effect. At
step 524, the
analyzer may identify molecular entities known to be associated with the
indication or side
effect. At step 526, the analyzer may extract a subgraph of the identified
molecular entities
from a global molecular entity graph. At step 528, the analyzer may augment
the extracted
subgraph to include additional molecular entities and inter-connections. At
step 530, the
analyzer may present the extracted subgraph to the user.
[02111 Still referring to FIG. 5B and in more detail, at step 522, an
analyzer executed
by a computing device may receive an identification of an indication or side
effect. In some
embodiments, the analyzer may receive the indication from an input/output
module of the
computing device. A user may select or enter the indication or side effect
into an input
interface, such as an application interface or web page interface. In many
embodiments, the
user may use an application on a second computing device to enter or select
the indication,
and the second computing device may transmit the entered indication to the
input/output
module of the computing device.
[0212] At step 524, in some embodiments, the analyzer may identify one or
more
molecular entities known to be associated with the selected or identified
indication.
Identifying a molecular entity known to be associated with the selected or
identified
indication may comprise, in some embodiments, retrieving adverse event data
associated with
the selected or identified indication. As discussed above, adverse event data
associated with
the indication may comprise one or more adverse event records including
identification of
consumed medications. In some embodiments, the medications in adverse event
records may
CA 02800722 2013-01-04
be identified in or linked to corresponding molecular entity information, such
as via a
medication information database. Accordingly, by identifying an indication,
then
medications associated with the indication, and then molecular entities such
as protein targets
associated with the medications, the analyzer may identify molecular entities
associated with
the indication. In some embodiments, such as where an adverse event database
comprises
medication information as discussed above, adverse event records may comprise
molecular
entity information, and thus, the analyzer may directly identify medications
associated with
the indication.
[0213] As discussed above, in some embodiments, the analyzer may generate a
list of
identified molecular entities. Such list may be ordered through various
statistical techniques,
including PRR, regularized PRR, logistic regression, or other means. In many
embodiments,
the analyzer may include in the list only entities appearing in adverse event
records at a
greater rate than a predetermined percentage or number threshold or
corresponding to
medications appearing in adverse event records at a greater rate than the
predetermined
percentage or number threshold. This may help reduce false positives and
incidental,
unrelated signals.
[0214] At step 526, the analyzer may extract a subgraph of the identified
molecular
entities from a global molecular entity graph. Extracting the subgraph may
comprise
identifying a network comprising each of the identified molecular entities and
augmenting the
network at step 528 with one or more additional entities and/or connections,
using any of the
techniques discussed above. For example, in some embodiments, extracting the
subgraph
may comprise selecting pairs of the identified molecular entities and
performing a shortest
path analysis to identify one or more intermediate entities to be included in
the subgraph. In
other embodiments, extracting the subgraph may comprise scoring additional
nodes in the
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network and adding the nodes to the subgraph based on node scores being above
a
predetermined threshold. As discussed above, nodes may be scored based on
their
relationship to the indication, their relationship to an organ associated with
the indication,
their relationship to a pathway associated with the indication, their
relationship to other nodes
external to the subgraph or internal to the subgraph (for example, decreasing
the score of a
node with large numbers of connections to nodes not included in the subgraph
or increasing
the score of a node with large numbers of connections to nodes included in the
subgraph), or
other similar relationships. In some embodiments, extracting the subgraph may
comprise
scoring pre-defined pathways in the global molecular entity graph with respect
to their
coverage of the identified molecular entities and merging high scoring pre-
defined pathways
to generate the subgraph network. Accordingly, in many embodiments, steps 526
and 528
may be considered as combined steps of extracting a subgraph based on the
identified
molecular entities and augmenting the subgraph with additional nodes using the
techniques
discussed herein.
[0215] At step 530, in some embodiments, the analyzer or an output module
connected to the analyzer may present the extracted and augmented subgraph to
a user. In
some embodiments, the subgraph may be presented as a visual graph. In many
such
embodiments, the visual graph may be generated by a display module, as
discussed above.
For example, the display module may generate a visual graph of the molecular
entities and
interconnections as an image, and may relocate entities as necessary to avoid
intersecting
connections. In some embodiments, the display module may generate an
interactive image
allowing entities to be selected for additional information, moved or
highlighted, or otherwise
manipulated. In some embodiments, the subgraph may be presented as an index or
array of
molecular entities and connected entities. In a further such embodiment,
entities in the
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subgraph may be ordered based on number of connections to other entities in
the subgraph,
identifying entities that may be most important to the selected indication.
[0216] In some
instances, activating a pathway or protein may result in different side
effects or adverse events than deactivating the pathway or protein. Using the
multivariate
analysis techniques discussed herein, these differences may be readily
examined by
extracting, from a subset of adverse event data associated with a pathway or
protein, a further
subset of adverse event data based on whether a drug was an agonist or
activator of the
protein or pathway, or whether the drug was an antagonist or inhibitor of the
protein or
pathway. For example, referring briefly to FIG. 5C, illustrated is an example
diagram of an
embodiment of a subset of a global entity graph associated with a pathway 550.
The subset
may be extracted from a global molecular entity graph using any of the
techniques discussed
above. In some embodiments, the extracted graph may comprise one or more
molecular
entities 552. Some of the molecular entities may comprise entities 554a-554c
that are known
to be activated or inactivated by agonist or antagonist drugs. For example, a
medication
information database may indicate that a first molecular entity 554a is
activated by a first
medication, or that a second molecular entity 554b is inactivated by a second
medication. In
some embodiments, a molecular entity may be activated by a first medication
and inactivated
by a second medication. Thus, in many embodiments, a pathway or protein may be
activated
by one or more medications and deactivated by one or more medications. By
comparing
subsets of adverse event data associated with the pathway or protein based on
whether the
patient experiencing the adverse event consumed an agonist or antagonist, a
side effect
profile specific to activating or inactivating the pathway or protein may be
generated, and
compared to general adverse event data for the pathway or for a different
activating state to
generate distinct adverse event comparison profiles.
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[0217] Referring now to FIG. 5C, illustrated is a flow chart of an
embodiment of a
method for extracting and comparing subsets of adverse event data based on
activation state
of a molecular entity. In brief overview, at step 570, a multivariate analyzer
may receive,
from a user, an identification of a molecular entity. In some embodiments, the
entity may
comprise a pathway, while in other embodiments, the entity may comprise a
protein, or any
other entity. At step 572, the analyzer may retrieve, from a medication
information database,
an identification of one or more medications affecting the pathway or entity.
At step 574, the
analyzer may identify a subset of the one or more medications that are
agonists or activators
of the entity or one or more entities of the pathway, or a subset of
antagonists or inhibitors of
the entity or one or more entities of the pathway. At steps 576, the analyzer
may retrieve,
from an adverse event database, adverse event data records including the
identified subset of
agonists or antagonists. In some embodiments, steps 574-576 may be repeated.
In other
embodiments, adverse event data records may be retrieved for the medications
identified at
step 572, to compare an overall side effect profile with an activation state
profile. At step
578, the extracted records for different subsets or for the entire set of
identification
medications may be compared to identify one or more differences in the adverse
event
profiles for the activation states.
102181 Still referring to FIG. 5D and in more detail, in some embodiments,
at step
570, an analyzer may receive an identification of a molecular entity from a
user, such as a
pathway or protein. In some embodiments, the analyzer may receive the
identification via a
web interface or application interface, from a remote computing device
operating on behalf of
the user, or from an input device connected to the computing device executing
the analyzer.
In many embodiments, the analyzer may receive an identification of a pathway,
and may then
retrieve from a global molecular entity graph or a molecular entity
information database, an
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identification or subset of entities associated with the pathway, using any of
the techniques
discussed herein.
[0219] At step 572, the analyzer may retrieve, from a medication
information
database, an identification of medications associated with the entity. For
example, in one
embodiment in which the entity is a protein, the analyzer may retrieve an
identification of
medications known to affect the protein. In another embodiment in which the
entity is a
pathway, the analyzer may identify, from the global molecular entity graph or
an entity
database, a set of entities, including proteins, associated with the pathway.
The analyzer may
then retrieve, from the medication information database, an identification of
medications
known to affect the set of entities associated with the pathway.
[0220] At step 574, in some embodiments, responsive to a request from the
user, the
analyzer may identify a subset of the medications responsive to their
activation or
inactivation of one or more of the entities of the pathway or an identified
protein. For
example, in one embodiment, a user may request to identify adverse event data
based on
activation of the pathway, and the analyzer may identify a subset of the
medications that are
agonists or activators of entities of the pathway. In another embodiment, the
user may
request to identify adverse event data based on inhibition of the pathway, and
the analyzer
may identify a subset of the medications that are antagonists or inhibitors of
entities of the
pathway. In many embodiments, whether a medication is an agonist or antagonist
of an
entity may be identified in a medication information database. In some
embodiments in
which a medication is an agonist of one entity in the pathway and an
antagonist of another
entity of the pathway, such medications may be excluded from the identified
subset. In other
embodiments, such medications may be included in the identified subset.
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[0221] At step 576, the analyzer may retrieve, from an adverse event
database,
adverse event data associated with the identified subset of medications. In
some
embodiments, retrieving the adverse event data may comprise retrieving adverse
event
records for a medication in the identified subset of medications, while in
other embodiment,
retrieving the adverse event data may comprise querying a database for records
associated
with the medication. In some embodiments, the analyzer may retrieve adverse
event records
of patients only taking medications in the identified subset of medications.
In other
embodiments, the analyzer may retrieve adverse event records of patients
taking medications
in the identified subset of medications and other medications unrelated to the
pathway, but
excluding medications with the other activation state of the pathway. For
example, for a
request for adverse event data associated with activating a pathway, the
analyzer may retrieve
adverse event records of patients taking any medication identified as an
agonist for a protein
in the pathway, but excluding any adverse event records of patients taking any
medication
identified as an antagonist for a protein in the pathway. This may be done to
exclude adverse
event data associated with patients who are consumed both activating and
inhibiting
medications.
[0222] In some embodiments, it may be more helpful to identify adverse
event
records associated with activating or inhibiting a plurality of molecular
entities in a pathway.
For example, inhibiting one protein in a pathway may not have the effect of
inhibiting the
entire pathway. Accordingly, in some embodiments, the analyzer may identify a
plurality of
molecular entities in a pathway, and may identify which medication in the
identified subset of
medications activates or inactivates which of the plurality of molecular
entities. In such
embodiments, the analyzer may retrieve adverse event records for patients
consuming one or
more medications, such that all of the identified entities was activated or
inactivated by the
medications. For example, in one such embodiment in which a first protein is
activated by a
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first medication, and a second protein is activated by a second medication,
the analyzer may
retrieve only adverse event records associated with patients consuming both
medications.
Similarly, if a third medication activates both proteins, the analyzer may
retrieve adverse
event records associated with patients consuming the third medication. Thus,
the analyzer
may build a side effect profile for patients who have, through one or more
medications,
activated or inactivated all of the identified entities in the pathway. In
some embodiments, all
of the entities may be identified, while in other embodiments, certain
entities of interest may
be identified. Additionally, though discussed in terms of pure activation or
inactivation
states, the above techniques may be applied to mixed activation or
inactivation states of a
plurality of entities. Thus, in one example embodiment, the analyzer may
retrieve adverse
event of patients taking a medication that activated a first protein and
inhibited a second
protein, or a first medication that activated the first protein and a second
medication that
inhibited a second protein, allowing complex analyses.
[0223] In many embodiments, steps 574-576 may be repeated for different
activation
states, such as for activating a pathway vs. inhibiting the pathway. In some
embodiments,
adverse event data may be retrieved for all medications associated with the
pathway,
regardless of activation state. This may be done to provide a control group or
allow
comparisons to a particular activation state.
[0224] In some embodiments, at step 578, the analyzer or a display module
may
display side effect profiles or adverse event profiles associated with the one
or more sets of
adverse event data retrieved at step 576. Such profiles may comprise
identifications of
adverse events experienced by patients in the extracted subset of records,
including
identifications of adverse events overtime, proportional reporting rates, an
ordered list of
medications, an ordered list of indications, an ordered list of outcomes, or
any other data. In
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some embodiments, the analyzer may generate a difference profile or identify
one or more
differences between two profiles. For example, the analyzer may identify
indications in
different positions or percentages between two profiles, identify differences
in the rates of
adverse events, or perform other comparisons. Such difference profiles or
differences may
further be displayed to the user, allowing investigation into adverse event
differences.
102251 Adverse
event data may also be used to predicatively identify unknown targets
for medications. Because adverse events may be due to physiological reactions
from
interaction of molecular entities with pharmaceutical compounds, a "backwards"
analysis of
observed adverse event data may enable identification of molecular entities
previously
unknown to interact with the pharmaceutical compound. Referring now to FIG.
6A,
illustrated is a diagram of a method of utilizing side effect profile
dissimilarities to identify
likely unknown targets of a medication. A first medication may have a first
side effect
profile 602 comprising a statistical index of one or more side effects
experienced by patients
or clinical trial participants consuming the medication, in some embodiments,
sorted by
frequency or percentage of occurrence, as discussed above. A second, similar
medication,
may have a second side effect profile 604 that may share some, but not all,
characteristics
with the first side effect profile 602. In some embodiments, the second
similar medication
may comprise a second medication in the same drug class as the first
medication, while in
other embodiments, the second similar medication may comprise a second
medication with
an identified known target shared with the first medication, or known to be
affecting the same
molecular entity as the first medication. In some embodiments, the second side
effect profile
604 may include one or more different side effects from the first side effect
profile 602, or
may include different frequencies or percentages of occurrence for one or more
side effects
from those of the first side effect profile 602. A multivariate analyzer may
generate a
difference profile 606 that identifies differences between the first side
effect profile 602 and
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the second side effect profile 606. For example, a first medication such as
lapatinib, may
have a first side effect profile 602 that includes rash as a side effect at a
very high rate, and
may be known to bind to Human Epidermal Growth Factor Receptor 2 (HER2). A
second
medication may be selected that also binds to HER2, such as Herceptin, which
may have a
second side effect profile 604 that does not include rash as a side effect or
includes rash only
at a very low frequency. Accordingly, an analyzer may generate a difference
profile or
subset of the first medication side effect profile 606 that includes rash at a
high frequency.
[0226] The analyzer may compare the difference profile 606 to other
medication side
effect profiles to identify another medication that includes the identified
differences in its side
effect profile 608. In some embodiments, the analyzer may limit the comparison
to other
medications in the same drug class or type, such as kinase inhibitors. For
example, given a
difference profile 606 including rash at a high frequency, the analyzer may
identify that rash
is also commonly associated with medications such as gefitinib and erlotinib.
Known targets
of the identified other medication may then be indicated as likely targets of
the first
medication. For example, Epidermal Growth Factor Receptor (EGFR) is a known
target of
gefitinib and erlotinib (as well as being a known target of lapatinib, but not
Herceptin). If it
was not known that lapatinib bound to EGFR, comparison of its difference side
effect profile
to the side effect profiles of gefitinib or erlotinib would indicate that EGFR
is a likely target
of lapatinib. Thus, through side effect profile comparisons and difference
profiles,
previously-unknown affected molecular entities for medications may be quickly
identified for
confirmation through targeted research.
[0227] Referring now to FIG. 6B, illustrated is a flow chart of an
embodiment of a
method for identifying unknown likely targets of a first medication via
comparison of adverse
event data. In brief overview, at step 622, an analyzer may receive an
identification of a first
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medication. At step 624, the analyzer may identify a second, similar
medication. At step
626, the analyzer may retrieve side effect profiles for the first medication
and the second
medication. At step 628, the analyzer may generate a difference profile for
the first
medication. At step 630, the analyzer may identify a third medication with a
side effect
profile similar to the difference profile. At step 632, the analyzer may
retrieve a list of
molecular entities or targets associated with the third medication. In some
embodiments,
steps 630 and 632 may be repeated for a plurality of medications. At step 634,
the analyzer
may present the retrieved list as potential targets of the first medication.
[0228] Still referring to FIG. 6B and in more detail, at step 622, an
analyzer executed
by a computing device may receive an identification of a first medication. In
some
embodiments, the analyzer may receive the identification of the first
medication from an
input/output module of the computing device. A user may select or enter the
medication into
an input interface, such as an application interface or web page interface. In
many
embodiments, the user may use an application on a second computing device to
enter or
select the medication, and the second computing device may transmit the
entered medication
to the input/output module of the computing device.
[02291 At step 624, the analyzer may identify a similar second medication.
In some
embodiments, the second similar medication may comprise a second medication in
the same
drug class as the first medication, while in other embodiments, the second
similar medication
may comprise a second medication with an identified known target shared with
the first
medication, or known to be affecting the same molecular entity as the first
medication. In
still other embodiments, the second similar medication may comprise a
medication
structurally similar to the first medication.
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[0230] At step 626, the analyzer may retrieve a first side effect profile
associated with
the first medication and a second side effect profile associated with the
second medication.
As discussed above, a side effect profile may comprise a statistical index of
one or more side
effects experienced by patients or clinical trial participants consuming the
medication. The
analyzer may retrieve each side effect profile by searching an adverse event
database for
adverse event records including the medication. In some embodiments, the
analyzer may sort
each side effect profile by frequency or percentage of occurrence of each side
effect, as
discussed above.
[0231] At step 628, the analyzer may generate a difference profile that
identifies
differences between the first side effect profile and the second side effect
profile. In some
embodiments, generating a difference profile may comprise subtracting a
frequency of
occurrence of a side effect in the second side effect profile from a frequency
of occurrence of
the side effect in the first side effect profile. In other embodiments,
generating a difference
profile may comprise discarding each side effect in the first side effect
profile for which the
second side effect profile includes the side effect at a frequency of
occurrence within a
predetermined threshold. For example, if a first side effect profile includes
a first side effect
with an 80% occurrence rate, and the second side effect profile includes the
first side effect
with a 75% occurrence rate, and the predetermined threshold is 10%, then the
first side effect
may be discarded from the resulting difference profile.
[0232] At step 630, the analyzer may identify a third medication with a
third side
effect profile similar to or comprising the difference profile. In one
embodiment, a side
effect profile is similar to the difference profile if the side effect profile
includes one or more
of the side effects in the difference profile at a frequency of occurrence
within a
predetermined threshold of the value in the difference profile. For example,
if the difference
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profile includes a side effect with an 80% occurrence rate, and the side
effect profile includes
the side effect with a 65% occurrence rate, and the predetermined threshold is
20%, the side
effect profile may be considered similar to the difference profile. In such
embodiments, a
predetermined threshold for similarity between the difference profile and the
side effect
profile may be the same as, or different from the predetermined threshold
discussed above for
generating the difference profile. In other embodiments, the analyzer may
subtract a
frequency of occurrence of a side effect in the difference profile from a
frequency of
occurrence of the side effect in the third side effect profile, and if the
result is zero or within a
predetermined value, the profiles may be identified as similar. In many
embodiments, either
of the difference profile or the third side effect profile may include
additional side effects not
included in the corresponding other profile. Nonetheless, a profile may be
identified as
similar based on similar values for identified side effects. In some
embodiments, similarities
must exist between a plurality of side effect occurrence frequencies before a
third side effect
profile may be identified as similar.
102331 In one embodiment, the analyzer may identify the third medication by
searching an adverse event database for all records including a first side
effect in the
difference profile. For each medication in the identified records, the
analyzer may then
search the adverse event database for all adverse events associated with the
medication. The
analyzer may then identify a frequency of occurrence of the first side effect
by identifying the
percentage of adverse event records for the medication which include the first
side effect.
This process may be repeated iteratively for additional medications and/or
additional side
effects to build a side effect profile for the medication. Additionally, in
many embodiments,
the analyzer may pre-generate side effect profiles for medications, allowing
identification at
step 630 to be performed quickly using the pre-generated profiles. In some
embodiments, the
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analyzer may limit the comparison and identification to other medications in
the same drug
class or type.
[0234] At step 632, the analyzer may retrieve a list of targets associated
with the
identified third medication. In some embodiments, as discussed above, the
analyzer may
retrieve the list of targets from a medication information database. In many
embodiments,
steps 630-632 may be repeated iteratively to identify additional medications
with side effect
profiles similar to the difference profile.
[0235] At step 634, the analyzer may present the retrieved list of targets
as potential
unknown targets of the first medication. In some embodiments, the analyzer may
remove
from the retrieved list any known targets of the first medication, while in
other embodiments,
the analyzer may add any known targets of the first medication not included in
the retrieved
list. In some embodiments in which steps 630-632 are repeated for a plurality
of
medications, the analyzer may generate a union of the retrieved lists of
targets, while in other
embodiments, the analyzer may take an intersection of the retrieved lists of
targets. This may
be done to increase the number of potential targets or decrease the number of
potential
targets, respectively. For example, utilizing an intersection of lists of
targets of medications
identified as having side effect profiles comprising or at least partially
similar to the
difference profile may result in removing targets that are associated with
less than all of the
medications, and thus may not contribute to the occurrence of the side effect.
[0236] Molecular entity interactions, even for a single drug, may be
complex. With
multiple drugs consumed by a patient, and information about each medication in
a text-based
form, it may difficult to identify interactions or treatment redundancies. As
a result,
physicians tend to use only known drug-drug interactions in considering
prescriptions.
Furthermore, in many instances, patients may be prescribed drugs with
redundant
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interactions, resulting in potential unpredictable side effects. For example,
a first drug may
need to be catalyzed by a first enzyme into a bioavailable compound, and the
drug dosage
may be calculated based on normal levels of the enzyme. If a patient is
prescribed a second
drug that is also catalyzed by the first enzyme, the enzyme may not be
available in sufficient
amounts to catalyze both drugs. In such cases, the first drug may not be
present in sufficient
amounts of its bioavailable form to treat the indication, or may be present in
its non-catalyzed
form at potentially toxic levels. Even if non-toxic, in some instances, the
combination of
drugs may result in one being excreted unprocessed by the patient, resulting
in potentially
expensive waste. Accordingly, it may be useful to physicians and patients self-
managing
care, as well as insurance companies or health care providers, to have an
intuitive tool for
examining molecular dependencies of a patient's prescription load, including
all drugs, and
the targets, carriers, metabolizing enzymes, transporters, pathways, and other
molecular
entities involved with each medication.
[0237] Referring now to FIG. 7A, illustrated is a screenshot of an example
of an
embodiment of a molecular entity dependency graph that provides intuitive
identification of
redundancies and molecular interactions between medications in a patient's
prescription load.
In some embodiments, a display module, embodiments of which are discussed
above, may
generate the dependency graph responsive to identification of a patient's
prescription load.
The display module and/or an analyzer may retrieve, from a medication
information database,
an identification of molecular entities associated with each medication
prescribed to the
patient and their associations and inter-associations for display in the
dependency graph. In
some embodiments, the dependency graph may comprise a radial graph of a
plurality of
molecular entities as radial entries. The molecular entities may be grouped
into sub-groups
of medications 702 prescribed to a patient; targets 704 of the medications
702; enzymes 706
catalyzing the medications 702; membrane transporters 708 of the medications
702; carriers
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710 such as a carrier protein utilized by the medications 702; and/or pathways
712 associated
with the medications 702. Molecular entities in the radial graph may be
visually linked by
entity associations 714. In some embodiments, the radial entries may include
mapped
mutational information for the patient, such as identified genetic variants
for the patient.
Such variants may be linked with other molecular entities in the graph, for
example,
corresponding protein targets 704 whose activation is modified by the variant.
Although
shown linking entities 704-712 to medications 702, in some embodiments,
pathways 712 may
be visually linked to other molecular entities such as target proteins 704
associated with the
pathway. As shown, in many embodiments, entity associates 714 may comprise
splines, and
may be generated to be grouped with other associations between a first
subcategory of
entities and a second subcategory of entities. This may help to visually
separate out entity
associations, as opposed to depicting entity associations with straight lines
from one radial
entry to another. For example, a straight line from a first medication 702 to
a first carrier 710
may intersect with a straight line from a second medication 702 to a target
704, potentially
visually confusing the two lines. Additionally, through the use of splines as
shown in FIG.
7A, a plurality of entity associations 714 from one subgroup of entities to
another subgroup
of entities may be substantially parallel until splitting out at each end,
reducing visual
confusion.
102381 In some embodiments, the dependency graph may be interactive. For
example, a display module may provide the dependency graph to an input/output
module,
such as a web server or server-side application, which may allow user
interaction with the
graph. In some embodiments, the user may select a first molecular entity, such
as by clicking
on the first molecular entity. In one such embodiment, the display module
and/or
input/output module may hide entity associations 714 not connected to the
selected molecular
entity. Referring now to FIG. 78, illustrated is a screenshot of an example of
an embodiment
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of a dependency graph allowing user interaction. As shown, in such
embodiments, a user
may select an entity 716, and a subgroup of entity associations 714 associated
with only that
entity 716 may be displayed. In some embodiments, radial entries connected to
the subgroup
may be highlighted or in darker text, as shown, while other radial entries may
be faded or
presented in lighter text, to visually distinguish associated entities and non-
associated entities.
[0239] Referring
briefly to FIG. 7C, illustrated is another screenshot of an example of
an embodiment of a dependency graph allowing user interaction. As shown, in
some
embodiments, the display module and/or input/output module may be configured
to allow a
user to select a plurality of entities 716a-716b. The display module may
display
corresponding entity associations 714a-714b for each of the plurality of
selected entities,
allowing direct comparison of two molecular entities, such as two medications
702. In some
embodiments, the display module may show entity associations 714a for a first
selected entity
716a in a first color or shade, and entity associations 714b for a second
selected entity 716b
in a second color or shade. This may be particularly helpful when each
selected entity is
associated with the some of the same other entities. For example, as shown in
FIG. 7C, the
two selected entities 716a-716b have associations with many of the same
molecular entities.
In a further embodiment, associations connected to a first selected entity may
be displayed in
a first color, associations connected to a second selected entity may be
displayed in a second
color, and display module may merge the colors of overlapping associations to
display a third
color representing shared associations. Returning briefly to FIG. 7A, as
shown, in some
embodiments, the display module may be configured to optionally display
selected entities
and corresponding associations in a highlighted or darker color, and non-
selected entities and
corresponding associations in a non-highlighted or lighter color. In one such
embodiment, a
user need not click to select an entity, but rather the display module may
highlight entities
and corresponding associations 714 as the user moves a cursor over each radial
entry.
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[0240] In some embodiments, the dependency graph may allow a user to easily
identify redundant medications. For example, a patient may be prescribed a
first pain reliever
and a second pain reliever, which may act in a similar way. The two
medications may both
be associated with many of the same molecular entities. If the two medications
target
different proteins, but utilize the same enzymes, transporters, and pathways,
a simple target
comparison may not identify a potential interaction (as well as potentially
missing off-target
interactions with proteins) that may cause an adverse effect or reduced
efficacy of one or both
medications. As the dependency graph intuitively highlights such interactions,
a patient self-
managing care or an insurance provider who lacks an advanced biology education
may still
be able to identify potential concerns or reduced efficacies for further
discussion with a
physician. In some embodiments, this may also allow identification of drugs
with similar or
identical interactions, raising questions of whether both drugs are needed for
treatment.
Reducing or eliminating one may reduce patient or insurance provider cost,
increase efficacy
of the remaining drug or drugs, and reduce unpredictable effects due to drug-
drug
interactions.
[0241] In some embodiments, adverse event data related to dangerous or
efficacious
combination therapies may be used with patient-specific genomic information to
optimize or
de-risk therapy for the patient. For example, in one embodiment, adverse event
data may
indicate that a combination therapy targeting a first protein (protein A) with
a first medication
(drug A) and targeting a second protein (protein B) with a second medication
(drug B) may
have a high rate of adverse side effects and/or negative outcomes. In addition
to recognizing
that drug A and drug B should not be co-prescribed to a patient, by
identifying patient
variants associated with the molecular entities protein A and protein B, it
may even be
determined that either of drug A or drug B should not be prescribed to the
patient alone. For
example, if the patient has a genetic mutation that inactivates protein B and
drug B is an
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antagonist (such that normal operation of drug B blocks binding of protein B,
for example),
then physiologically, the patient's system may be equivalent to a normal
patient consuming
drug B. Accordingly, prescribing drug A alone to the patient may
unintentionally result in
adverse events normally seen through the combination of drug A and drug B.
[0242] Similar relationships may result based on whether the mutation is
inactivating
or activating of the protein, and whether the drug is an agonist or
antagonist. For example, in
an embodiment in which drug A is an agonist, drug B is an agonist, and the
combination of
drug A and drug B results in an adverse event:
a. If the patient has an activating mutation for protein A, then drug B should
be
contraindicated.
b. If the patient has an inactivating mutation for protein A, then drug B may
be
indicated.
c. If the patient has no mutation (i.e. a wildtype) for protein A, then drug B
may be
indicated.
d. If the patient has an activating mutation for protein B, then drug A should
be
contraindicated.
e. lithe patient has an inactivating mutation for protein B, then drug A may
be
indicated.
f. If the patient has no mutation (i.e. a wildtype) for protein B, then drug A
may be
indicated.
[0243] Similarly, if drug A is an antagonist, drug B is an antagonist, and
the
combination of drug A and drug B results in an adverse event:
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a. If the patient has an inactivating mutation for protein A, then drug B
should be
contraindicated.
b. If the patient has an activating mutation for protein A, then drug B may be
indicated.
e. If the patient has no mutation (i.e. a wildtype) for protein A, then drug B
may be
indicated.
d. If the patient has an inactivating mutation for protein B, then drug A
should be
contraindicated.
e. If the patient has an activating mutation for protein B, then drug A may be
indicated.
f. If the patient has no mutation (i.e. a wildtype) for protein B, then drug A
may be
indicated.
[0244] Likewise, if
drug A is an agonist, drug B is an antagonist, and the combination
of drug A and drug B results in an adverse event:
a. If the patient has an activating mutation for protein A, then drug B should
be
contraindicated.
b. If the patient has an inactivating mutation for protein A, then drug B may
be
indicated.
e. If the patient has no mutation (i.e. a wildtype) for protein A, then drug B
may be
indicated.
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d. If the patient has an inactivating mutation for protein B, then drug A
should be
contraindicated.
e. If the patient has an activating mutation for protein B, then drug A may be
indicated.
f. If the patient has no mutation (i.e. a wildtype) for protein B, then drug A
may be
indicated.
[0245] Similarly,
if drug A is an antagonist, drug B is an agonist, and the combination
of drug A and drug B results in an adverse event:
a. If the patient has an inactivating mutation for protein A, then drug B
should be
contraindicated.
b. If the patient has an activating mutation for protein A, then drug B may be
indicated.
c. If the patient has no mutation (i.e. a wildtype) for protein A, then drug B
may be
indicated.
d. If the patient has an activating mutation for protein B, then drug A should
be
contraindicated.
e. If the patient has an inactivating mutation for protein B, then drug A may
be
indicated.
f. If the patient has no mutation (i.e. a wildtype) for protein B, then drug A
may be
indicated.
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[0246] Although discussed in terms of a pair of interacting drugs, in many
embodiments, the analysis may be extended to any number of interacting
medications. For
example, if it is observed that four drugs prescribed in combination results
in a high rate of
adverse events, patient genetic variant information relating to the molecular
entities targeted
by each drug may be analyzed to determine if a single drug, pair of drugs, or
trio of drugs
should be contraindicated, responsive to corresponding variants for three
targets, two targets,
or one target respectively. In other embodiments, a drug may have a plurality
of target
proteins, and the system may contraindicate other drugs responsive to the
patient having
corresponding variants for each protein. Thus, for example, if drug A is an
antagonist of
proteins A and C, in some embodiments, drug B may be contraindicated only if
the patient
has inactivating mutations for both of proteins A and C.
[0247] Referring now to FIG. 8, illustrated is a flow chart of an
embodiment of a
method for personalized de-risking of medications based on genomic information
of a patient
and adverse event data of combination therapies. In brief overview, at step
802, an analyzer
executed by a computing device may receive an identification of a genomic
variant of a
patient altering activity of a first protein. At step 804, the analyzer may
identify a first
medication targeting the first protein. At step 806, the analyzer may receive
an identification
of a second medication targeting the second protein considered as a potential
medication to
be prescribed. At step 808, the analyzer may identify a likelihood of an
adverse event
occurring from co-medication of the first medication and second medication. At
step 810, the
analyzer may determine that an adverse event is likely to occur for the
patient. At step 812,
the analyzer may contraindicate the second medication.
[0248] Still referring to FIG. 8 and in more detail, in one embodiment, an
analyzer
may receive an identification of a genomic variant of a patient altering
activity of a first
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protein. In one embodiment, the analyzer may receive a list of variants of the
patient. In
some embodiments, in which the analyzer receives a plurality of variants, the
analyzer may
select a variant and repeat the method of FIG. 8 iteratively. In some
embodiments, the list of
variants may explicitly identify corresponding proteins, while in other
embodiments, the
analyzer may retrieve identifications of one or proteins corresponding to each
variant from a
genetic information database. In some embodiments, the analyzer may receive
the
identification of genomic variants from an input/output module, as discussed
above. In some
embodiments, a user of a second computing device may transfer or upload a list
of variants to
the analyzer, such as via a web interface or application.
[0249] At step 804, the analyzer may identify a first medication targeting
the first
protein. In one embodiment, the analyzer may search a medication information
database for
medications identified as targeting the first protein. ln another embodiment,
the analyzer
may utilize an adverse event database that includes in adverse event records
identification of
target proteins targeted by medications consumed by the person experiencing
the adverse
event. The analyzer may query the database to retrieve a list of medications
associated with
the first protein.
[0250] At step 806, the analyzer may receive an identification of a second
medication
for consideration for prescription to the patient. The second medication may
target a second
protein. In some embodiments, a user may select a second medication from a
list of
medications, while in other embodiments, the user may enter a name or part of
a name of a
medication through a web interface or application interface, as discussed
above.
[0251] At step 808, the analyzer may determine whether an adverse event is
likely to
occur if both the first medication and second medication are prescribed to a
patient. In some
embodiments, the analyzer may query an adverse event database to retrieve an
identification
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of a number of adverse event records including both medications as consumed by
the person
experiencing the adverse event. The adverse event database may, in some
embodiments,
identify a number of times each drug was prescribed or number of times the
combinations of
drugs were prescribed, such that the analyzer may determine a ratio of adverse
event
occurrences to total number of prescriptions. In other embodiments, such as
where such non-
adverse event data is unavailable, the analyzer may query the adverse event
database to
determine a ratio of serious outcomes to total number of adverse events for
the combination
of medications. For example, if a serious outcome, such as death or disability
occurs in the
majority of adverse event reports for the two medications, the combination may
be
considered to have very high risk. In comparison, if a serious outcome occurs
in only a slim
minority or none of the adverse event reports, with non-serious outcomes
dominating the
records, then the combination may be considered to have a low risk. Thus, in
such
embodiments, the analyzer may determine whether an adverse event including a
serious
outcome is likely to occur if both the first medication and second medication
are prescribed
to a patient.
102521 At step 810, the analyzer may determine that an adverse event is
likely to
occur for the patient if the patient is prescribed the second medication,
responsive to
determining that an adverse event is likely to occur if the patient
comedicated with the first
medication and the second medication and that the patient has a genetic
mutation affecting a
protein corresponding to activity of the first medication with the protein. As
discussed above,
this determination may be responsive to whether the mutation is activating or
non-activating,
and whether the medication is an agonist or antagonist, respectively.
[02531 At step 812, responsive to determining that an adverse event is
likely to occur
for the patient if the patent is prescribed the second medication, the
analyzer may
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contraindicate the second medication. In some embodiments, contraindicating
the medication
may comprise generating a list of contraindicated medications for display to
the user.
[0254] As discussed above, in many embodiments, steps 806-812 may be
iteratively
repeated for additional medications, to de-risk a patient's prescription load.
Accordingly, at
step 808, the analyzer may search for adverse events with a pair of
medications, trio of
medications, or more medications, responsive to the number of medications
identified by the
user. Additionally, in some embodiments, steps 806-812 may be iteratively
repeated for
alternate, similar medications to the identified second medication. For
example, in one such
embodiment, having determined that the patient will likely experience an
adverse event upon
consuming the identified second medication, the analyzer may repeat steps 806-
812 for a
third medication in the same drug class or type as the second medication. For
example, if the
analyzer identifies that, due to a genetic mutation in a patient and based on
adverse event
data, the patient will likely experience an adverse event upon consuming
gefitinib, the
analyzer may repeat the analysis for erlotinib, another kinase inhibitor. If
the analyzer
determines that the third medication may not induce an adverse effect in the
patient, the
analyzer may identify the third medication as a potential alternate
prescription. This may
allow the system to automatically identify safer alternative medications for
consideration.
[0255] Furthermore, in a similar embodiment, patient genomic information
may be
used to determine if, for example, a mutation in a protein will decrease the
binding affinity of
a specific drug, leading to the drug building up to toxic levels and causing
adverse events if
consumed by the patient. Such proteins may comprise any proteins that interact
with and/or
are critical to the mode of action, metabolism, or passage of the drug through
the patient
system, or otherwise directly interact with the drug at the pharmacokinetic or
pharmacodynamics levels. Accordingly, in such embodiments, the model of the
drug's
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passage and mode of action within the patient system may be analyzed against
patient variant
information. This may allow identification of mutations in genes that do not
directly interact
with the drug, but whose functions regulate the activity of a gene or protein
that does.
Similarly, in some embodiments, the above methods and systems may be used to
identify
mutations in genes that affect the expression or binding affinities for off-
target proteins that
may lead to adverse events. For example, over-expressed off-target proteins
may act as
"molecular sinks" for a drug, decreasing the therapeutic efficacy of the
medication.
Identifying such interactions with the above-discussed systems may allow
contraindication of
apparently unrelated medications, reducing the incidence of previously
unpredictable adverse
events.
[0256] Furthermore, by collecting and analyzing patent-specific genomic
information,
adverse event profiles may be generated based on a genetic mutation. For
example, variant
identifications of patients that suffered a specific adverse event may be
compared to identify
genetic commonalities, which may be used to potentially de-risk new patients.
[0257] In another embodiment, homologous family members of proteins may be
identified as likely off-target candidates. For example, using knowledge about
the diseases
caused by mutations in these candidates, the analysis system may predict
potential adverse
events induced by consumption of drugs targeting the homologous family members
by the
patient.
[0258] In some embodiments, a multivariate analysis system may be able to
reduce
false signals in planned clinical trials by identifying medications to be
contraindicated for a
cohort. For example, in many instances, a disease and a side effect may differ
only due to the
side effect being drug-induced. Accordingly, the side effect may be thought of
as a drug-
induced disease. For manufacturers and researchers developing new
pharmaceuticals, it may
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be important during trials to avoid including patients taking other drugs that
may induce the
same side effect as the disease in question. Furthermore, it may be desirable
to screen all
patient co-medications for drug interactions at many levels, including on a
molecular basis.
[0259] In some embodiments, it may be desirable to exclude drugs from a
proposed
clinical trial with side effect profiles that include side effects
corresponding to a disease that
is the subject of the clinical trial. For example, in one embodiment, if a
proposed clinical trial
is examining the effect of drug A in indication A, but adverse event data
indicates that a side
effect corresponding with indication A is also inducible by drug B, then the
analysis system
may contraindicate drug B from the clinical trial. The inclusion of such
contraindicated drugs
may result in false negatives, as they have a chance of counteracting any
therapeutic effects
of drug A on the disease. In another embodiment, if a clinical trial is
examining the combined
effects of two approved drugs for investigation into potential combination
therapies, the
analysis system may be used to examine the safety profile of the combination
and include
potential safety issues in the trial protocol.
[0260] In some embodiments, as discussed above, analysis may be performed
on a
molecular basis. For example, in one such embodiment with a first drug
targeting a first
protein to be used for a clinical trial, a multivariate analysis system may
retrieve a side effect
profile for the protein, based on adverse event data for all medications
targeting the protein.
In other embodiments, molecular entities functionally related to the protein
may be identified,
and side effect profiles for medications targeting those molecular entities
may be retrieved.
In many embodiments in which molecular entity information is integrated into
adverse event
records as discussed above, side effect profiles may be generated for the
molecular entities
directly, and then medications associated with high risk entities may be
identified for
contraindication.
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10261] Referring now to FIG. 9, illustrated is a flow chart of an
embodiment of a
method for identifying a medication for contraindication from a clinical trial
of another
medication. In brief overview, at step 902, an analyzer executed by a
computing device may
receive an identification of an indication for a clinical trial. At step 904,
the analyzer may
retrieve adverse event data for a side effect corresponding to the indication.
At step 906, the
analyzer may generate an ordered list of one or more medications consumed by
patients that
experienced the side effect. At step 908, the analyzer may select one or more
medications
from the list, and at step 910 may display the one or more medications as
contraindicated
from the clinical trial.
[0262] Still referring to FIG. 9 and in more detail, at step 902, an
analyzer executed
by a computing device may receive an identification from a user of an
indication for a clinical
trial. In some embodiments, the user may select or enter the indication via a
web interface or
application interface. The user may utilize the same computing device or a
second
computing device connected to the first computing device via a network.
[0263] In some embodiments, at step 904, the analyzer may retrieve adverse
event
data for a side effect corresponding to the indication from an adverse event
database. In
some embodiments, the analyzer may query the database for records including
the side effect
corresponding to the indication. Such records may comprise identifications of
the side effect
and outcome experienced by the patient, medications consumed by the patient,
patient
demographic information, and any other relevant information. In some
embodiments, the
records may comprise identifications of molecular entities corresponding to
the medications,
while in other embodiments, such identifications may be in a second medication
information
database.
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[0264] At step 906, the analyzer may generate a list of medications
identified in each
retrieved record. In some embodiments, the analyzer may count the number of
times each
medication appears in the retrieved records in order to order the list via
frequency of
appearance. In some embodiments, each medication may be scored in the list or
have an
associated frequency value and/or statistical percentage or rate of
appearance. In some
embodiments, the analyzer may determine one or more statistical measures for
the
medication, such as reporting odds ratio (ROR), incidence rate ratio, or
proportional reporting
ratio (PRR), or may apply one or more statistical algorithms, such as a multi-
item gamma
Poisson shrinker (MGPS) algorithm.
[0265] At step 908, the analyzer may identify one or more medications from
the list
to be contraindicated. In some embodiments, the analyzer may select all
medications in the
list to be contraindicated, while in other embodiments, the analyzer may
select a subset of
medications in the list. For example, in one embodiment, the analyzer may
select all
medications in the list associated with a particular organ that is the subject
of the clinical trial.
In another embodiment, the analyzer may select all medications in the list of
a particular drug
class or type. In still another embodiment, the analyzer may select
medications having a
statistical value or ratio above a predetermined threshold. For example, the
analyzer may
select all medications having a PRR or MGPS value over 2 and discard other
medications
from the list.
[0266] At step 910, the analyzer may display the identified one or more
medications
as medications to be contraindicated from the trial. In some embodiments, the
analyzer may
display one or more statistically likely side effects that may be induced by
each
contraindicated medication.
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102671 In some embodiments, the analyzer may further identify combinations
of
medications to be contraindicated for the trial. For example, in some
instances, a side effect
corresponding to the indication may appear when two medications are consumed
by a patient,
but not when either is consumed alone. From the adverse event data, the
analyzer may
identify that each medication is included individually in adverse event
records for the side
effect. The analyzer may then compare pairs or sets of identified medications
for frequency
of co-appearance within each retrieved record. Medications that appear
together at a high
frequency within the adverse event records may be identified as a
contraindicated
combination.
[02681 In some embodiments, a multivariate analysis of adverse event data
may be
further used to identify novel combination therapies for research by
generating cohorts of
patients conforming to specific clinical and treatment variables. Cohorts can
be compared in
terms of patient outcomes, with variables examined for potential clinical
effects. For
example, adverse event data for a first cohort of patients with cancer who
have taken an anti-
neoplastic agent may be retrieved and compared to adverse event data for a
second cohort of
patients with cancer who have taken an anti-neoplastic agent plus another
class of drug. The
sets of adverse event data for each cohort may be compared to identify if the
other class of
drug has any effect on the death rate of cancer patients across cancer
indications. Drugs
which appear to decrease the death rate or are associated with a lower death
rate in adverse
event reports may then be potential candidates for combination therapy.
Furthermore, such
analysis may be done for any molecular entity.
102691 For example, and referring briefly to FIG. 10A, illustrated is a
Venn diagram
of an example of an embodiment of defining cohorts within adverse event data
and extracting
difference profiles for a cohort. Adverse event data for an indication 1002
may be retrieved
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from an adverse event database through a query by an analyzer. The query may
further
comprise additional variables to define cohorts 1004A-1004C or patients
defined by the
variable, and adverse event data for each cohort may be retrieved. In many
embodiments,
patients may be in multiple cohorts. For example, a first cohort may be
defined as patients
who consumed a first drug, and a second cohort may be defined as patients who
consumed a
second drug. Accordingly, patients consuming both drugs may be placed in both
cohorts.
Variables for defining cohorts may be of different types. For example, a first
cohort may be
defined as patients who are over a specified age, and a second cohort may be
defined as
patients who consumed a medication that was catalyzed by a specified enzyme.
The analyzer
may extract a distinct adverse event profile for a cohort 1006. In some
embodiments, the
analyzer may compare adverse event profiles between cohorts to generate a
difference
profile, while in other embodiments, the analyzer may generate a query that
excludes
members of other cohorts from the cohort for which the distinct profile is
created. In still
other embodiments, the analyzer may retrieve identifications of adverse event
records for
each cohort, and then eliminate any records shared by each cohort. The
analyzer may then
determine rates of various outcomes for the records identified in the
difference profile, and
may compare this to rates of various outcomes for other cohorts, or the
indication as a whole.
Differences in the rates may thus indicate potential combination therapies.
[0270] Referring now to FIG. 10B, illustrated is a flow chart of an
embodiment of a
method for identifying potential combination therapies for research via
adverse event data. In
brief overview, at step 1022, an analyzer may receive an identification of an
indication. At
step 1024, the analyzer may retrieve adverse event data for the identified
indication. At step
1026, the analyzer may receive an identification of a patient cohort. In many
embodiments,
the patient cohort may be defined by a molecular entity, while in other
embodiments, the
patient cohort may be defined by demographic information or a genotype. At
step 1028, the
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analyzer may extract a subset of adverse event data for the patient cohort. In
some
embodiments, steps 1026-1028 may be repeated for additional cohorts. At step
1030, the
analyzer may compare the extracted subsets to generate a collated list of
differences between
the patient cohorts. At step 1032, the analyzer or an output module connected
to the analyzer
may display the collated list of differences. Although shown in one order in
FIG. 10B, as
discussed above, in some embodiments in which the analyzer uses multivariate
queries with
Boolean operations to retrieve adverse event data from the adverse event
database, many of
the steps may be collapsed into a single step.
[0271] Still referring to FIG. 10B and in more detail, in one embodiment at
step 1022,
an analyzer may receive an identification of an indication from a user. In
some
embodiments, the analyzer may receive the identification via a web interface
or application
interface communicating via an input/output module. As discussed above, the
user may
operate an application on the same computing device as the analyzer, or on a
different
computing device communicating with the first computing device via a network.
[0272] At step 1024, in some embodiments, the analyzer may retrieve adverse
event
data for the identified indication from an adverse event database. As
discussed above,
adverse event data may comprise records of adverse events experienced by
patients, and may
identify an indication for which the patient was being treated or may identify
a side effect
experienced by the patient corresponding to the indication.
[0273] At step 1026, the analyzer may receive an identification of a first
patient
cohort. The patient cohort may be defined by a molecular entity, such as
patients consuming
a first medication, patients consuming a medication targeting a first protein,
patients
consuming a medication targeting a first pathway, patients consuming a
medication related to
a first drug class, etc. In other embodiments, the patient cohort may be
defined by
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demographic information, such as age or gender, or may be defined by patients
having
specified genetic mutations or wildtypes. In many embodiments, multiple
variables may be
used to define a patient cohort, such as men over 50 being treated for high
cholesterol.
102741 At step 1028, the analyzer may extract a subset of adverse data
experienced by
the identified first patient cohort. In some embodiments, the analyzer may
extract data
relating to side effects experienced by the first patient cohort being treated
for the identified
indication, while in other embodiments, the analyzer may extract data relating
to patient
outcomes of the first patient cohort. Such data may comprise raw numbers of
adverse events
for each side effect and/or outcome, or proportional reporting ratios or other
statistical
identifiers for each side effect and/or outcome. The analyzer may repeat steps
1026-1028 for
a plurality of cohorts with at least one modified variable, such as an
included or excluded
molecular entity, changed demographic information, etc.
[0275] At step 1030, the analyzer may compare the extracted subsets for
different
patient cohorts to identify statistical differences between side effects
and/or outcomes
between cohorts. In one embodiment, comparing the extracted subsets may
comprise
generating difference values for each statistical value of a side effect
and/or outcome. For
example, if 30% of a first cohort is listed as having died as a result of the
indication and/or
side effect, and 10% of a second cohort is listed as having died as a result
of the indication
and/or side effect, a difference value of -20% may be identified for the
second cohort. In
many embodiments, difference values beyond a predetermined threshold may
indicate a
potentially significant result of the modified variable between the cohorts.
In some
embodiments, comparing the extracted subsets of adverse event data may
comprise
generating an index of side effects and/or outcomes experienced by the
patients and sorting
the index by percentage or raw number. The analyzer may then compare the
positions of
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individual side effects and/or outcomes within the generated index for each
cohort. In many
embodiments, the analyzer may generate a collated list of one or more
statistical differences
between the side effect profiles for each cohort. As discussed above, in many
embodiments,
the list may be limited to statistical differences above a predetermined
threshold, such as
difference percentages over a predetermined rate, or altered index positions
greater than a
predetermined number.
[0276] At step 1032, the analyzer or a display module or output module
connected to
the analyzer may display the generated list of statistical differences to the
user. The list may
be used to identify statistically significant differences in adverse events
experienced by each
cohort, and potentially attributable to the modified variable or variables
between the cohorts.
This may point to potential combination therapies for reducing risk or
increasing efficacy of
therapy.
[0277] By integrating an adverse event database with molecular entity
information,
such as the global molecular entity graph discussed above, a multivariate
analysis system
may be able to predict a likely side effect profile for even new, untested
medications.
Specifically, a predicted side effect profile may be generated based on
intersections of side
effect profiles of other medications that affect the same or related molecular
entities, such as
the nearby target proteins, involve the same pathways, or are otherwise
similarly related. To
generate a predicted side effect profile for a new drug targeting a novel or
previously un-
targeted protein target, an analyzer may query an adverse event database for
records
pertaining to patients who have taken drugs or combinations of drugs that
target or affect
molecular entities in the vicinity of the novel target within a global
molecular entity graph.
By examining the side effect profiles associated with the connected targets,
one can look for
commonalities that might also be expected with the novel target. For example,
referring
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briefly to FIG. 11A, illustrated is a graph of an example of a region of an
example
embodiment of a global molecular entity graph or molecular entity network
comprising a
plurality of molecular entities 1106 connected via functional links. To
generate a predicted
side effect profile for a new drug targeting novel target protein 1102, an
analyzer may query
an adverse event database for adverse event records of patients who consumed a
first
approved drug targeting a first protein A 1104A; adverse event records of
patients who
consumed a second approved drug targeting a second protein B 11048; and
records of
patients who consumed both drugs. Intersections and/or difference profiles may
be generated
based on these retrieved adverse event records to a generate side effect
profile of adverse
event records that likely involved the novel target 1102, even if it was not
realized at the
time. For example, a patient who consumed both the first drug and second drug
targeting
proteins A and B likely affected their processing of the novel target protein
1102, for example
by reducing availability of an enzyme needed to catalyze the protein 1102,
resulting in higher
systemic levels of the protein than normal. In some embodiments, this may have
a similar
effect as a novel drug that acts as an agonist of the protein, for example.
Accordingly, side
effects experienced by such a patient may be similar to side effects that may
be experienced
by a patient consuming the novel drug.
[0278] Referring now to FIG. 11B, illustrated is a flow chart of an
embodiment of a
method for generating a predicted side effect profile for a medication
targeting a novel target.
In brief overview, at step 1122, an analyzer or input module may receive an
identification of
a novel drug target. At step 1124, the analyzer may identify a second target
functionally
connected to the novel drug target in a global molecular entity graph. At step
1126, the
analyzer may identify a medication targeting the second target. At step 1128,
the analyzer
may retrieve a side effect profile for the identified medication targeting the
second target. In
some embodiments, the analyzer may output the retrieved side effect profile at
step 1132 for
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display to the user as a predicted side effect profile of the novel drug
target. In many
embodiments, the analyzer may repeat steps 1126-1128 to retrieve side effect
profiles for one
or more additional medications targeting the second target, while in other
embodiments, the
analyzer may repeat steps 1124-1128 to identify one or more additional targets
and additional
medications. At step 1130, the analyzer may generate an intersection side
effect profile of
the retrieved side effect profiles, and at step 1132, may output the retrieved
side effect profile
for display to the user as a predicted side effect profile of the novel drug
target.
[0279] Still referring to FIG. 11B and in more detail, at step 1122, an
analyzer
executed by a computing device may receive an identification of a novel drug
target from a
user. The novel drug target may comprise a molecular entity, such as a
protein, enzyme,
transporter, or other entity that may be known, but not previously targeted by
a medication.
Functional relationships or connections to other molecular entities from the
novel drug target
may also be known, such as the inclusion of the novel drug target in a global
molecular entity
graph. In some embodiments, the analyzer may receive the identification of the
novel drug
target via an application executed by the computing device used by the user,
while in other
embodiments, the analyzer may receive the identification via a web interface
or application
interface via a network from a second computing device.
[0280] At step 1124, the analyzer may identify a second target functionally
connected
to the novel drug target in a global molecular entity graph. In one
embodiment, the analyzer
may select a nearby drug target using a shortest path algorithm. In another
embodiment, the
analyzer may select a nearby drug target with the most interconnections to
nodes also
connected to the novel drug target. For example, if the novel drug target is
connected to five
additional nodes, two of which are also connected to a first target and three
of which are
connected to a second target, the analyzer may select the second target based
on the
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additional shared node. In some embodiments, a combination of these approaches
may be
used. For example, the analyzer may select a nearby target that has the most
independent
paths to the novel target of less than a predetermined length. In some
embodiments, the
analyzer may even select such a target over a second target that has fewer,
but shorter paths.
For example, if a first nearby target has five paths to the novel target, each
path traversing
one intermediate node (i.e. length two), the analyzer may select this target
over a second
nearby target that has only one path that directly connects to the novel
target (i.e. length one).
In some embodiments, nearby targets may be selected based on their
relationship to the same
organ involved with the first target. In other embodiments, nearby targets may
be scored
based on their inclusion in a common pathway or pathways with the novel
target, and the
analyzer may select the highest scoring target. In still other embodiments,
nearby targets may
be scored based on their number of connections to nodes in a shared pathway
with the novel
target. In a further embodiment, a target's score may be reduced based on its
number of
connections to nodes in pathways not shared with the novel target. In still
other
embodiments, combinations of a plurality of these techniques may be used to
generate a score
for each nearby target, and the analyzer may select a high scoring target. In
repeated
iterations, the analyzer may select additional targets scoring above a
predetermined threshold.
[0281] At step 1126, the analyzer may identify a medication targeting the
second
target. In one embodiment, the analyzer may query a medication information
database for
one or more medications identified as targeting the second target. In some
embodiments, the
analyzer may identify medications that are known to have off-target effects on
the second
target. In some embodiments, the analyzer may identify a plurality of
medications targeting
the second target and may repeat steps 1126-1130 iteratively for each of the
plurality of
medications.
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102821 At step 1128, in some embodiments, the analyzer may retrieve from an
adverse event database or generate from records retrieved from the adverse
event database a
side effect profile for the identified medication. As discussed above, the
side effect profile
may comprise an identification of all side effects or adverse events listed in
the adverse event
database as experienced by patients consuming the medication, along with a
score, raw
number, percentage or proportional reporting ratio, or other metric to
identify a statistical rate
for each side effect. In some embodiments, the analyzer may return the side
effect profile as
a predicted side effect profile for the novel target at step 1132 for display
to the user. This
may be done, for example, if the second target is only targeted by one
medication. Typically,
however, the analyzer may repeat steps 1126-1128 for additional medications
identified as
targeting the second target, and/or steps 1124-1128 for additional targets
nearby the novel
target in the global molecular entity graph.
[02831 At step 1130, in some embodiments, the analyzer may compare a
plurality of
retrieved side effect profiles to generate an intersection profile. In one
embodiment, an
intersection profile may comprise one or more side effects or adverse events
present in each
retrieved side effect profile. In another embodiment, an intersection profile
may comprise
one or more side effects or adverse events present in each retrieved side
effect profile with a
similar reporting percentage or PRR, such as within a predetermined range.
This may be
useful to discard false positives where a side effect profile includes large
numbers of side
effects only associated with a few records. In some embodiments, an
intersection profile may
be further differentiated by outcome. For example, the intersection profile
may comprise one
or more side effects or adverse events present in each retrieved side effect
profile with a
similar reporting percentage and similar rate of serious or non-serious
outcomes. This may
be an important distinction, for example, if two side effect profiles
experience a side effect at
the same rate, but one has a much higher rate of serious outcomes.
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[0284] At step 1132, the intersection profile may be presented to the user
as a
predicted side effect profile for the drug targeting the novel target. In one
embodiment, a
display module or output module may generate a table, list or index of the
intersection profile
for display to the user. In some embodiments, the intersection profile may be
transmitted to a
second computing device for display to the user. Such predicted side effect
profiles may be
used to establish safety measures for a trial protocol for the drug.
Furthermore, in some
embodiments, while an intersection profile may be more narrowly tailored to
the target
protein, the analyzer may instead generate a union or combination profile at
step 1130. This
may be done to ensure that all potential side effects are included in the
predicted side effect
profile. In such embodiments, the combination profile may comprise a
combination of the
retrieved side effect profiles. In some embodiments, duplicate entries in the
side effect
profiles, such as one side effect that appears in each profile at a similar
rate, may be removed.
In other embodiments, duplicate entries may be more highly scored, such as
with a
confidence value. Thus, a side effect that appears in only one profile may be
included in the
combination profile but scored lower than a side effect that appears in a
plurality of profiles
at similar rates. The latter may be more likely to occur with the new drug.
Scores or
confidence values may be displayed to the user along with profile to aid in
predicting likely
side effects.
[0285] In some embodiments, by integrating patient or trial participant-
specific
genetic information with adverse event data, a multivariate analysis system
may be able to
identify genetic variants associated with adverse events in a clinical trial.
This may enable
deeper levels of interpretation of safety signals than are available through
purely
observational means, allowing in-depth insights into the molecular
protagonists and pathways
involved in eliciting drug side effects. On the one hand, a multivariate
analysis may detect
drugs that induce specific clinical side effects. Exploration of the
underlying molecular
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mechanisms of offending drugs allows researchers and clinicians to hone in on
the activity of
targets and off-targets whose drug-induced perturbation leads to specific
adverse phenotypes.
On the other hand, the multivariate analysis may capture and contextualize
relevant published
information, providing another level of gene prioritization in association
with specific side
effects. Combining these techniques and integrating other clinico-molecular
information may
provide the ability to efficiently analyze patient specific genomic
information in search of
genetic factors that influence a drugs risk profile.
102861 For example, and referring briefly to the block diagram illustrated
in FIG.
12A, in one embodiment involving a clinical trial where a serious and
unexpected adverse
reaction is encountered, a researcher may generate complete genome sequence
information
for the affected patient or patients, and then attempt to identify a causal
genetic predisposition
or predispositions to the observed effect. Such sequence information may
comprise
identifications of the patient's specific genetic mutations and variants. In
many
embodiments, the sequence information may be obtained from an external
provider of
genomic information. The sequence may be analyzed to detect variants from
wildtypes, and
each variant may be mapped to one or more corresponding molecular entities
based on their
relationship to the entities, such as whether they are activating or
inactivating of a protein,
etc. By combining information and knowledge about the molecular mechanisms
associated
with side effects with complete genomic sequencing, researchers can quickly
identify genetic
factors that may increase a patient's risk of drug-induced side effects. The
multivariate
analyzer may determine, from adverse event data associated with molecular
entity
information, which molecular entities may be responsible for an adverse event,
and
correspondingly, whether the event may be likely to occur in the general trial
population or
whether it is associated with a specific variant or variants of the affected
patient.
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[0287] Referring now to FIG. 12B, illustrated is a flow chart of an
embodiment of a
method of identifying genetic variants associated with adverse events. In
brief overview, at
step 1202, an analyzer executed by a computing device may receive an
identification of an
adverse event experienced by a patient or participant in a clinical trial of a
first medication.
At step 1204, the analyzer may query an adverse event database for records
associated with
the adverse event to generate an ordered list of one or more protein targets
most associated
with the event. At step 1206, the analyzer may receive an identification of
one or more
genetic variants of the participant or patient. At step 1208, the analyzer may
modify the order
of the list of one or more protein targets responsive to targets in the list
corresponding to the
identified one or more genetic variants. At step 1210, the analyzer or an
output module
connected to the analyzer may output the modified list to a user as a
prioritized list of variants
potentially responsible for the adverse event.
[0288] Still referring to FIG. 12B and in more detail, at step 1202, a
multivariate
analyzer executed by a computing device may receive, from a user, an
identification of an
adverse event experienced by a participant of a clinical trial of a first
medication. In some
embodiments, the analyzer may receive the identification of the adverse event
via an input
module, such as a web interface or application interface. In many embodiments,
the analyzer
may receive the identification from a second computing device via a network.
[0289] At step 1204, the analyzer may query an adverse event database for
one or
more adverse event records associated with the adverse event. As discussed
above, in some
embodiments, each record may comprise or be linked to identifications of one
or more
protein targets targeted by drugs consumed by the person who experienced the
adverse event
for which the record was generated. In other embodiments, each record may
comprise
identifications of one or more medications consumed by the person who
experienced the
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adverse event, and the analyzer may retrieve one or more corresponding protein
targets for
the one or more medications from a medication information database. The
analyzer may
generate an ordered list of the proteins based on the frequency with which the
protein (or a
medication targeting the protein) appears in the adverse event records. In
some
embodiments, the analyzer may include a PRR or percentage rate with which each
protein
appears in or is associated with the adverse event records. In one embodiment,
the analyzer
may generate a score for each protein based on the order of the protein within
the list or the
identified rate.
[0290] At step 1206, the analyzer may receive an identification of one or
more
genetic variants of the participant who experienced the adverse event in the
clinical trial. In
some embodiments, the user of the computing device may provide a list of
variants to the
analyzer, while in other embodiments, the user of the computing device may
provide a full or
partial genetic sequence of the participant, and the analyzer may identify one
or more variants
within the genetic sequence through comparison with a database of genetic
wildtypes.
[0291] At step 1208, the analyzer may modify the order of the list of
proteins for
protein targets corresponding to identified genetic variants of the
participant. In some
embodiments, the analyzer may increase a score associated with each protein in
the ordered
list responsive to the participant having a variant associated with the
protein, or decrease
scores associated with each protein in the ordered list responsive to the
participant not having
a variant or having a wildtype associated with the protein. In a further
embodiment, the
analyzer may increase a score of a protein targeted by the first medication if
the participant
has a genetic variant corresponding to the protein. In some embodiments, the
analyzer may
increase the scores of proteins in the list associated with an organ related
to the adverse event,
such as increasing the score of proteins associated with the kidneys if the
participant
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experienced renal failure. Accordingly, the analyzer may modify the order of
the list of
proteins and/or score of each protein to generate a prioritized list of
potential targets inducing
the adverse event in the participant. At step 1210, the analyzer or an output
module may
present the modified list to the user as a prioritized list of proteins
potentially responsible for
the experienced adverse event. In a further embodiment, the analyzer or output
module may
present the modified list with corresponding genetic variants of the patient.
Accordingly, the
list may identify the genetic variants and proteins most likely to be
associated with inducing
of the adverse event.
[0292] It may be helpful to briefly discuss examples of embodiments of an
interface
for performing multivariate analysis of adverse event data. One skilled in the
art may readily
appreciate that many other interfaces may be utilized, and as such, the
examples should be
considered non-limiting.
[0293] Referring first to FIGs. 13A-13Y, illustrated are screenshots of
example
embodiments of an interface for performing multivariate analysis of adverse
event data. In
some embodiments, the interface may be accessed through a web browser, while
in other
embodiments, the interface may be provided as part of an application. As shown
in FIG.
13A, the interface may comprise a home page or screen with one or more search
boxes or
links. As shown in FIG. 13B, in response to a user entering a full or partial
search term, the
interface may display a list of results, comprising entity names matching the
search, type of
entity, number of adverse events in an adverse event database associated with
the entity, most
frequent drugs co-medicated with the entity, most frequent indications for
which the entity is
prescribed, and most frequent reactions associated with the entity in the
adverse event
database. Similarly, as shown in FIG. 13C, searches may be done for other
entities or entity
types.
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[0294] Once an entity is selected from the search results, the interface
may display a
dashboard of statistical data as shown in the embodiment of FIG. 13D.
Statistical data may
include graphs of: numbers of adverse events associated with the entity by
year; number of
adverse events by indications; number of adverse events by reactions; number
of adverse
events by outcomes; and number of adverse events by drugs. In many
embodiments, only the
highest numbered indications, reactions, or drugs may be displayed on the
dashboard, due to
space limitations.
[0295] Navigation links in FIG. 13D provide access to further detailed
information.
For example, as shown in FIG. 13E, the interface may provide a list of drugs
associated with
the entity in adverse event data, along with statistical data regarding their
frequency in the
reports. Similarly, as shown in FIG. 13F, the interface may provide a list of
Anatomical
Therapeutic Chemical (ATC) classes, grouped by level, associated with the
entity in adverse
event data, along with statistical data regarding their frequency in the
reports. In some
embodiments, similar lists may be displayed by the interface, including
indications (as shown
in FIG. 13G); reactions (as shown in FIG. 13H); molecular targets (as shown in
FIG. 131);
and molecular mechanisms (as shown in FIG. I3J).
[0296] In many embodiments, as shown in FIG. 13K, the interface may provide
access to individual adverse event reports for the entity. In some
embodiments, the interface
may also provide identifications of numbers of adverse events for the entity
associated with
individual drugs (Fig. 13L); ATC classes (FIG. 13M); indications (FIG. 13N);
reactions (FIG.
130); molecular targets or molecular mechanisms (not shown for brevity). The
interface may
further provide access to literature associated with the entity in a medical
literature server or
accessible over a network, as shown in FIG. 13P. In some embodiments, as shown
in FIG.
13Q, the interface may provide detailed information about the entity.
Similarly, the interface
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may provide information about molecular mechanisms associated with the entity,
as shown in
FIG. 13R.
[0297] As discussed above in connection with FIG. 13K, the interface may
provide
access to individual adverse event reports for the entity, as shown in FIG.
I3S. The adverse
event reports may comprise demographic information for the patient who
experienced the
adverse event, and information regarding outcomes, consumed medications,
reactions, and
indications. As discussed above, in many embodiments, the interface may
provide a radial
dependency graph, specific to the adverse event report, as shown in FIG. 13T.
[0298] In some embodiments, the interface may provide information regarding
pathways, such as a graph or portion of a global molecular entity graph
showing functional
relationships among entities associated with a pathway, as shown in FIG. 13U.
As discussed
above, in many embodiments, the interface may also provide such graphs as a
result of
analysis of a global molecular entity graph.
[0299] In many embodiments, the interface may provide functions for
comparing two
entities directly. For example, as shown in FIG. 13V, the interface may
provide for side-by-
side searching of entities, including different entity types, as well as side-
by-side comparison
of adverse event data, as shown in FIG. 13W.
[0300] In some embodiments, as discussed above, the interface may provide
functions
to generate cohorts for extraction of cohort-specific adverse event data.
Boolean queries may
be crafted defining the cohort and managed through a cohort interface, as
shown in FIG. 13X.
Upon processing and extraction, adverse event data specific to the cohort may
be displayed
and investigated, as shown in FIG. 13Y. In some embodiments, the interface may
comprise a
utility for building cohort definitions, as well as providing a preview of
what records may be
included in the defined cohort.
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[0301] Referring briefly to FIGs. 14A-C, as discussed above, in some
embodiments, a
multivariate analyzer may compare side effect profiles to generate
intersection or union
profiles for investigation of combination therapies, prediction of side
effects for novel targets,
or other purposes. Referring first to FIG. 14A, illustrated is an example
embodiment of a list
of a side effect profile for a first medication. The list may be sorted based
on frequency of
reaction, for example, or based on frequency of a particular outcome, such as
death.
Similarly, in FIG. 14B, illustrated is an example embodiment of a list of a
side effect profile
for a second medication. As shown, lists may be of different length, for
example, due to less
data being available or due to a reduced variety of side effects for one
medication. As shown
in FIG. 14C, in some embodiments, side effect profiles may be directly
compared and cross
referenced, allowing determinations of differences in reactions between
medications and
generation of intersection or union profiles.
[0302] In summary, by permitting the direct assessment of relationships
between the
human proteome and drug-induced phenotypes, the systems and methods discussed
herein
provide efficient and intuitive approaches to the analysis and molecular
dissection of adverse
event data information. Patient specific clinico-molecular data may be
integrated with the
systems, providing advanced treatment decision support.
[0303] It should be understood that the systems described above may provide
multiple ones of any or each of those components and these components may be
provided on
either a standalone machine or, in some embodiments, on multiple machines in a
distributed
system. The systems and methods described above may be implemented as a
method,
apparatus or article of manufacture using programming and/or engineering
techniques to
produce software, firmware, hardware, or any combination thereof. In addition,
the systems
and methods described above may be provided as one or more computer-readable
programs
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embodied on or in one or more articles of manufacture. The term "article of
manufacture" as
used herein is intended to encompass code or logic accessible from and
embedded in one or
more computer-readable devices, firmware, programmable logic, memory devices
(e.g.,
EEPROMs, ROMs, PROMs, RAMs, SRAMs, etc.), hardware (e.g., integrated circuit
chip,
Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit
(ASIC),
etc.), electronic devices, a computer readable non-volatile storage unit
(e.g., CD-ROM,
floppy disk, hard disk drive, etc.). The article of manufacture may be
accessible from a file
server providing access to the computer-readable programs via a network
transmission line,
wireless transmission media, signals propagating through space, radio waves,
infrared
signals, etc. The article of manufacture may be a flash memory card or a
magnetic tape. The
article of manufacture includes hardware logic as well as software or
programmable code
embedded in a computer readable medium that is executed by a processor. In
general, the
computer-readable programs may be implemented in any programming language,
such as
LISP, PERL, C, C#, PROLOG, or in any byte code language such as JAVA. The
software programs may be stored on or in one or more articles of manufacture
as object code.
[0304] Having described certain embodiments of methods and systems for
providing
systems and methods for molecular analysis of adverse event data, it will now
become
apparent to one of skill in the art that other embodiments incorporating the
concepts of the
invention may be used.
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