Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets

• DOI: 10.7554/elife.25818
• Publication Date: 2017-08-08T00:00:14Z
• Authors: Mateusz Maciejewski; Eugen Lounkine; Steven Whitebread; Pierre Farmer; William DuMouchel; Brian K Shoichet; Laszlo Urban
• Keywords: medicine; QH301-705.5; Science; data analysis; 03 medical and health sciences; big data; Product Surveillance, Postmarketing; Adverse Drug Reaction Reporting Systems; Humans; human; Biology (General); Human Biology and Medicine; Pharmacological Phenomena; adverse drug reactions; 0303 health sciences; United States Food and Drug Administration; FAERS; Q; R; human biology; United States; Product Surveillance; Postmarketing; 3. Good health; 5.1 Pharmaceuticals; Medicine; Biochemistry and Cell Biology; Development of treatments and therapeutic interventions

The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.