Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions

J Biomed Inform. 2009 Dec;42(6):990-1003. doi: 10.1016/j.jbi.2009.05.010. Epub 2009 Jun 16.

Abstract

We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Area Under Curve
  • Computational Biology / methods*
  • Databases, Factual
  • Drug Interactions*
  • Evidence-Based Medicine
  • Humans
  • Hydroxymethylglutaryl-CoA Reductase Inhibitors / metabolism
  • Hydroxymethylglutaryl-CoA Reductase Inhibitors / pharmacokinetics
  • Hydroxymethylglutaryl-CoA Reductase Inhibitors / standards
  • Medical Informatics
  • Pharmaceutical Preparations / metabolism*
  • Pharmacokinetics*
  • Reproducibility of Results

Substances

  • Hydroxymethylglutaryl-CoA Reductase Inhibitors
  • Pharmaceutical Preparations