Modeling and Simulation of Pharmacokinetic Aspects of Cytochrome P450-Based Metabolic Drug–Drug Interactions

Abstract

The prediction of drug–drug interactions (DDIs) is becoming more important, with an increasing expectation for these interactions to be minimized or avoided wherever possible. The majority of these interactions involve effects on drug-metabolizing enzymes, particularly cytochrome(s) P450 (CYP), and changes in the pharmacokinetics of drugs whose clearance is predominantly mediated by CYPs will be the subject of this chapter. Inhibition of enzyme activity is the most common effect seen, resulting in an increase in exposure to the affected drug. However, some compounds act as enzyme inducers, producing an increased clearance of the affected drug due to the increased synthesis of new CYP enzyme. The utility of in vitro data using recombinant human CYPs, microsomes (hepatic subcellular fractions) and hepatocytes has greatly enhanced the understanding of CYP-based metabolic DDIs. In recent years, more use has been made of such data for extrapolation to the in vivo situation, by scaling the effect seen in vitro and incorporating appropriate models of hepatic metabolism to describe the altered clearance. The gut may also play a role in the metabolism of substrates (particularly for those drugs metabolized by the enzyme CYP3A4) and can be included in the DDI assessment. In this way, the effects of various mechanisms of CYP-based metabolic DDI such as reversible and time-dependent inhibition (where a proportion of the enzyme is inactivated) can be quantitated, incorporating the fraction of the drug metabolized by the inhibited CYP pathway (fmCYP). The equations, which describe the changes in metabolic clearance by CYPs, can be used sequentially to calculate the magnitude of a CYP-based metabolic DDI, using kinetic constants generated in vitro to describe drug metabolism and inhibition/induction of the CYP enzymes. Software packages now incorporate such equations together with human physiological variability, so that a virtual population can be generated and used to investigate pharmacokinetic changes resulting from metabolic DDIs.