Drug-drug interactions via mechanism-based cytochrome P450 inactivation: points to consider for risk assessment from in vitro data and clinical pharmacologic evaluation
- PMID: 17584016
- DOI: 10.2174/138920007780866861
Drug-drug interactions via mechanism-based cytochrome P450 inactivation: points to consider for risk assessment from in vitro data and clinical pharmacologic evaluation
Abstract
This commentary discusses the approaches to, and key considerations in the in vitro-in vivo extrapolation of drug-drug interactions (DDI) resulting from mechanism-based inactivation (MBI) of cytochrome P450 (CYP) enzymes and clinical pharmacologic implications. In vitro kinetic assessment and prediction of DDI produced via reversible inhibition and MBI rely on operationally and conceptually distinct approaches. DDI risk assessment for inactivators requires estimation of maximal inactivation rate (k(inact)) and inactivator potency (KI) in vitro, that need to be considered in context of the biological turnover rate of the enzyme (kdeg) and clinical exposures of the inactivator (I), respectively, to predict interaction magnitude. Risk assessment cannot be performed by a simple comparison of inactivator potency against in vivo exposure since inactivation is both concentration and time-dependent. MBI contour plots tracking combinations of I:KI and k(inact):k(deg) resulting in identical fold-reductions in intrinsic clearance are proposed as a useful framework for DDI risk assessment. Additionally, substrate-specific factors like fraction of the total clearance of the object drug via the enzyme being inactivated (f(m(CYP) )) and the bioavailability fraction across the intestine for CYP3A substrates (F(G)) are important determinants of interaction magnitude. Sensitivity analysis of predicted DDI magnitude to uncertainty in input parameters is recommended to inform confidence in predictions. The time course of reversal of DDI resulting from CYP inactivation is determined by the half-life of the enzyme which is an important consideration in the design and interpretation of clinical DDI studies with inactivators.
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