Interpretation of Cytochrome P-450 Inhibition and Induction Effects From Clinical Data: Current Standards and Recommendations for Implementation.

Agents that modify cytochrome P-450 (CYP) enzyme activity are characterized as strong, moderate, or weak inhibitors or inducers based on the magnitude of their impact on substrate exposure in clinical studies. Criteria for these classifications are simple and semiquantitative. However, assignment of a given agent to a CYP inhibitor or inducer category is often complicated by limitations of the published data, inconsistent study findings, and other factors. CYP inhibitor and inducer categories are commonly used as a basis for differentiating drug interaction management recommendations. For example, product labeling for a CYP substrate may recommend avoidance in combination with strong inhibitors and dose reduction in combination with moderate inhibitors. When such recommendations exist, ambiguity or variability in placement of inhibitors or inducers into categories can introduce potentially harmful variations in clinical drug interaction management. Failure to adequately reflect the drug interaction potential of an agent by under-categorizing it (e.g., calling it weak when data point to moderate effects), for example, may lead clinicians to respond inadequately to real risks, or to ignore potential interactions altogether. Over-categorization may lead to actions such as over-adjustment of substrate doses or unnecessary avoidance of optimal treatments. This review describes the current criteria for assignment of CYP inhibitor and inducer categories, summarizes common circumstances leading to ambiguous or variable CYP inhibitor and inducer categorizations, and proposes an approach to data interpretation and application of current criteria under uncertainty. When applied to > 1,000 CYP reviews, the approach described has identified a clear categorization in almost all cases.

Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.

BACKGROUND: Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. OBJECTIVE: The aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents. METHODS: A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. RESULTS: We developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated? CONCLUSION: Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

Inter-rater agreement among physicians on the clinical significance of drug-drug interactions.

BACKGROUND: Alert fatigue could potentially be improved if physicians agreed on which alerts were clinically significant. We conducted a study to determine the extent to which physicians agree on which drug-drug interactions are clinically significant. METHODS: Two groups of eight generalist physicians reviewed 100 randomly selected drug-drug interactions from the Medi-Span® Drug Therapy Monitoring System™ database and indicated whether they thought each interaction was clinically significant based on the full-text clinical discussion contained within each interaction monograph and their clinical experience. RESULTS: The Fleiss Kappa measure of inter-rater agreement was 0.19 (0.12, 0.26) for one group, 0.22 (0.14, 0.29) for the second group and 0.21 (0.15, 0.27) for the combined group. CONCLUSION: We found poor agreement among generalist physicians on which drug-drug interactions are clinically significant. Use of a feature to allow physicians to tailor alerts to their needs may be an important component in reducing alert fatigue.