S-Warfarin Limited Sampling Models to Estimate Area Under the Concentration Versus Time Curve for Cytochrome P450 2C9 Baseline Activity and After Induction

S-warfarin limited sampling strategy with a population pharmacokinetic approach to estimate exposure and cytochrome P450 (CYP) 2C9 activity in healthy adults

PURPOSE

S-warfarin is used to phenotype cytochrome P450 (CYP) 2C9 activity. This study evaluated S-warfarin limited sampling strategy with a population pharmacokinetic (PK) approach to estimate CYP2C9 activity in healthy adults.

METHODS

In 6 previously published studies, a single oral dose of warfarin 10 mg was administered alone or with a CYP2C9 inducer to 100 healthy adults. S-warfarin concentrations were obtained from adults during conditions when subjects were not on any prescribed medications. A population PK model was developed using non-linear mixed effects modeling. Limited sampling models (LSMs) using single- or 2-timepoint concentrations were compared with full PK profiles from intense sampling using empiric Bayesian post hoc estimations of S-warfarin AUC derived from the population PK model. Preset criterion for LSM selection and validation were a correlation coefficient (R²) >0.9, relative percent mean prediction error (%MPE) >-5 to <5%, relative percent mean absolute error (%MAE) ≤ 10%, and relative percent root mean squared error (%RMSE) ≤ 15%.

RESULTS

S-warfarin concentrations (n=2540) were well described with a two-compartment model. Mean apparent oral clearance was 0.56 L/hr and volume of distribution was 35.5 L. Clearance decreased 33% with the CYP2C9 *3 allele and increased 42% with lopinavir/ritonavir co-administration. During CYP2C9 constitutive conditions, LSMs at 48 hr and at 72 hr as well as 2-timepoint LSMs were within acceptable limits for R², %MPE, %MAE, and %RMSE. During CYP2C9 induction, S-warfarin LSMs had unacceptable %MPE, %MAE, and %RMSE.

CONCLUSIONS

Phenotyping studies with S-warfarin in healthy subjects can utilize a single- and/or a 2-timepoint LSM with a population PK approach to estimate constitutive CYP2C9 activity.

Clinical Studies on Drug-Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation

Many drug-drug interactions (DDIs) are based on alterations of the plasma concentrations of a victim drug due to another drug causing inhibition and/or induction of the metabolism or transporter-mediated disposition of the victim drug. In the worst case, such interactions cause more than tenfold increases or decreases in victim drug exposure, with potentially life-threatening consequences. There has been tremendous progress in the predictability and modeling of DDIs. Accordingly, the combination of modeling approaches and clinical studies is the current mainstay in evaluation of the pharmacokinetic DDI risks of drugs. In this paper, we focus on the methodology of clinical studies on DDIs involving drug metabolism or transport. We specifically present considerations related to general DDI study designs, recommended enzyme and transporter index substrates and inhibitors, pharmacogenetic perspectives, index drug cocktails, endogenous substrates, limited sampling strategies, physiologically-based pharmacokinetic modeling, complex DDIs, methodological pitfalls, and interpretation of DDI information.

Assessment of Pharmacokinetic Drug-Drug Interactions in Humans: In Vivo Probe Substrates for Drug Metabolism and Drug Transport Revisited

Pharmacokinetic parameters of selective probe substrates are used to quantify the activity of an individual pharmacokinetic process (PKP) and the effect of perpetrator drugs thereon in clinical drug-drug interaction (DDI) studies. For instance, oral caffeine is used to quantify hepatic CYP1A2 activity, and oral dagibatran etexilate for intestinal P-glycoprotein (P-gp) activity. However, no probe substrate depends exclusively on the PKP it is meant to quantify. Lack of selectivity for a given enzyme/transporter and expression of the respective enzyme/transporter at several sites in the human body are the main challenges. Thus, a detailed understanding of the role of individual PKPs for the pharmacokinetics of any probe substrate is essential to allocate the effect of a perpetrator drug to a specific PKP; this is a prerequisite for reliably informed pharmacokinetic models that will allow for the quantitative prediction of perpetrator effects on therapeutic drugs, also in respective patient populations not included in DDI studies.