AERSMine:
analyze differential clinical outcomes across
the spectrum of human diseases and drugs
AERSMine is a multi-cohort analyzing application designed to mine data across
millions
of patient reports (currently 21,560,354) from the FDA’s Adverse Event Reporting System.
Perform focused {patients X meds} group comparisons, high dimensional subset–based correlation
analyses, view differential reporting patterns to identify high-risk demographics subgroups,
and unravel latent relationships within large clinical effects data. Gather new insights on
inter-correlated adverse events and population subgroups, recognize potential safety signals
and generate testable hypotheses based on risk-altering interactions. Our long-term hypothesis
is that by correlation of adverse reactions with known drug-phenotype-gene relationships, we
will improve our ability to modify therapeutic strategies and improve therapeutic efficacy.
Citation: Sarangdhar, M. et al.
Nat. Biotechnol.
34, 697–700 (2016).
Discover novel patterns across multiple population subgroups, generate analyzable data matrices,
identify unexpectedly high-risk subgroups, mechanistically linked ADRs, visualize and export analyses.
AERSMine:A novel framework for FAERS data mining
AERSMine is a multi-cohort analyzing application designed to mine data from the
Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, legacy AERS,
LAERS). AERSMine allows comparative analysis of differential clinical outcomes and
adverse events (reactions) in response to approved and/or investigational drugs
(therapeutics). Differential adverse event (ADR) risk profiles can be identified
via well-established disproportionality analysis methods used in pharmacovigilance
including relative risk (RR), and safety signal detection methods, such as
Information Component (IC) and drug-drug-interaction (DDI) metric Omega. In addition,
AERSMine can facilitate pharmacology-based novel discoveries via identification of
drug repositioning (repurposing) candidates to improve treatment strategies. With
AERSMine researchers can gather new insights on inter-correlated ADRs and population
subgroups, recognize potential safety signals and generate testable hypotheses based
on risk-altering interactions. AERSMine uses the Anatomical Therapeutic Chemical
Classification System (ATC-KEGG) and MedDRA, Medical Dictionary for Regulatory
Activities (MedDRA) ontologies for drugs, clinical indications and ADR aggregation
respectively. AERSMine was recently published in Nature Biotechnology (NatBiol,
Natbio, NBT).