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We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKB's evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.

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.