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

分类 模棱两可 诱导剂 药品 药理学 计算生物学 计算机科学 心理学 医学 化学 生物 人工智能 生物化学 基因 程序设计语言
作者
Stephen M. Stout,Carrie W. Nemerovski,Daniel S. Streetman,Melody L. Berg,Jamie Hoffman,Kayann Burke,Nina M. Bemben,Stephen J. Sklar
出处
期刊:Clinical Pharmacology & Therapeutics [Wiley]
卷期号:109 (1): 82-86 被引量:14
标识
DOI:10.1002/cpt.1918
摘要

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.

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