Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions.
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.
Boyce
Richard
R
Department of Biomedical Informatics, University of Pittsburgh, VALE M, PA 15260, USA. boycerd@upmc.edu
Collins
Carol
C
Horn
John
J
Kalet
Ira
I
D000465:Algorithms; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004347:Drug Interactions; D019317:Evidence-Based Medicine; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000378:metabolism / Q000493:pharmacokinetics / Q000592:standards; D008490:Medical Informatics; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010599:Pharmacokinetics; D015203:Reproducibility of Results
D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D004364:Pharmaceutical Preparations