Database of adverse events associated with drugs and drug combinations

Abstract

Due to the aging world population and increasing trend in clinical practice to treat patients with multiple drugs, adverse events (AEs) are becoming a major challenge in drug discovery and public health. In particular, identifying AEs caused by drug combinations remains a challenging task. Clinical trials typically focus on individual drugs rather than drug combinations and animal models are unreliable. An added difficulty is the combinatorial explosion in the number of possible combinations that can be made using the increasingly large set of FDA approved chemicals. We present a statistical and computational technique for identifying AEs caused by two-drug combinations. Taking advantage of the large and increasing data deposited in FDA’s postmarketing reports, we demonstrate that the task of predicting AEs for 2-drug combinations is amenable to the Likelihood Ratio Test (LRT). Our pAERS database constructed with LRT contains almost 77 thousand associations between pairs of drugs and corresponding AEs caused solely by drug-drug interactions (DDIs). The DDIs stored in pAERS complement the existing data sets. Due to our stringent statistical test, we expect many of the associations in pAERS to be unrecorded or poorly documented in the literature.

Prevention of Early Alzheimer’s Disease by Erinacine A-Enriched Hericium erinaceus Mycelia Pilot Double-Blind Placebo-Controlled Study

“…The overall incidence was 8.2% during the entire 49weeks. However, due to the increasing trend in clinical practice to treat elderly patients with multiple medications (Poleksic and Xie, 2019), it remains a challenge to identify if these adverse events were caused by EAHE consumption. Yet, reports on genotoxicity (Li et al, 2014a), acute toxicity (Li et al, 2018a), 28 days subchronic toxicity (Li et al, 2014b) 90 days subchronic toxicity (Lee et al, 2019) and teratotoxicity (Li et al, 2018a) have been conducted in animals and showed no adverse effects.…”

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

“…Drugs can be toxic or cause adverse events. Toxicity can be detected via ad-hoc in vitro ed in vivo experiments or it can be predicted via in silico models [Zhang et al, 2018],[Ganter et al, 2005],[Igarashi et al, 2015],[Alexander-Dann et al, 2018],[Poleksic and Xie, 2019]. Any of these techniques should be applied downstream to the use of DrugMerge for extra safety.…”

Predicting adverse drug reactions of two‐drug combinations using structural and transcriptomic drug representations to train an artificial neural network

“…The scope of this project only covers the inclusion of ADRs resulting from DDIs reported in the TWOSIDES data table. Further improvements through enrichment of observed ADRs for drug pairs can be achieved by integrating the above with DrugBank's DDI (Wishart et al, 2018) , all drug-drug side effects reported in SIDER (Kuhn et al, 2016) and PAERS (Poleksic & Xie, 2019) .…”