Predicting adverse drug reactions using publicly available PubChem BioAssay data

Clin Pharmacol Ther. 2011 Jul;90(1):90-9. doi: 10.1038/clpt.2011.81. Epub 2011 May 25.


Adverse drug reactions (ADRs) can have severe consequences, and therefore the ability to predict ADRs prior to market introduction of a drug is desirable. Computational approaches applied to preclinical data could 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 that are detectable in large, and increasingly public, compound screening campaigns, we generated logistic regression models that correlate postmarketing ADRs with screening data from the PubChem BioAssay database. These models analyze ADRs at the level of organ systems, using the system organ classes (SOCs). Of the 19 SOCs under consideration, nine were found to be significantly correlated with preclinical screening data. With regard to six of the eight established drugs for which we could retropredict SOC-specific ADRs, prior knowledge was found that supports these predictions. We conclude this paper by predicting that SOC-specific ADRs will be associated with three unapproved or recently introduced drugs.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adverse Drug Reaction Reporting Systems*
  • Animals
  • Data Interpretation, Statistical
  • Data Mining
  • Databases, Factual
  • Drug Evaluation, Preclinical / statistics & numerical data
  • Drug-Related Side Effects and Adverse Reactions / epidemiology*
  • Forecasting
  • Humans
  • Logistic Models
  • Models, Statistical
  • Product Surveillance, Postmarketing
  • Risk Assessment