Predicting Adverse Drug Reactions Using Publicly-Available PubChem BioAssay Data - Pouliot, Yannick (2011)
Adverse drug reactions (ADRs) can have severe consequences, such that the ability to predict ADRs prior to market introduction is desirable. Computational approaches applied to pre-clinical data might be one way to inform drug labeling and marketing with respect to potential ADRs. Based on the premise that some of the molecular actors of ADRs involve interactions detectable in large, and increasingly public, compound screening campaigns, we generated logistic regression models that correlate post-marketing ADRs with screening data from the PubChem BioAssay database. These models analyze ADRs at the level of organ systems, the System Organ Classes (SOCs). Nine of the 19 SOCs under consideration were found to be significantly correlated with pre-clinical screening data. For 6 of the 8 established drugs for which we could retropredict SOC-specific adversities, prior knowledge was found that support these predictions. We conclude by predicting SOC-specific adversities for three unapproved or recently introduced drugs.