A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug-Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates.

BACKGROUND AND OBJECTIVES: Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir. METHODS: A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively. RESULTS: Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (Cmax) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of Cmax and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all Cmax and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for Cmax and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively. CONCLUSIONS: The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.

Prediction of CYP-mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator.

Physiologically-based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug-drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism-based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (Cmax ) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both Cmax and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP-mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Prediction of drug clearance by glucuronidation from in vitro data: use of combined cytochrome P450 and UDP-glucuronosyltransferase cofactors in alamethicin-activated human liver microsomes.

Glucuronidation via UDP-glucuronosyltransferase (UGT) is an increasingly important clearance pathway. In this study intrinsic clearance (CL(int)) values for buprenorphine, carvedilol, codeine, diclofenac, gemfibrozil, ketoprofen, midazolam, naloxone, raloxifene, and zidovudine were determined in pooled human liver microsomes using the substrate depletion approach. The in vitro clearance data indicated a varying contribution of glucuronidation to the clearance of the compounds studied, ranging from 6 to 79% for midazolam and gemfibrozil, respectively. The CL(int) was obtained using either individual or combined cofactors for cytochrome P450 (P450) and UGT enzymes with alamethicin activation and in the presence and absence of 2% bovine serum albumin (BSA). In the presence of combined P450 and UGT cofactors, CL(int) ranged from 2.8 to 688 microl/min/mg for zidovudine and buprenorphine, respectively; the clearance was approximately equal to the sum of the CL(int) values obtained in the presence of individual cofactors. The unbound intrinsic clearance (CL(int, u)) was scaled to provide an in vivo predicted CL(int); the data obtained in the presence of combined cofactors resulted in 5-fold underprediction on average. Addition of 2% BSA to the incubation with both P450 and UGT cofactors reduced the bias in the clearance prediction, with 8 of 10 compounds predicted within 2-fold of in vivo values with the exception of raloxifene and gemfibrozil. The current study indicates the applicability of combined cofactor conditions in the assessment of clearance for compounds with a differential contribution of P450 and UGT enzymes to their elimination. In addition, improved predictability of microsomal data is observed in the presence of BSA, in particular for UGT2B7 substrates.

Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.

Analogous to the fraction unbound in microsomes (fu(mic)), fraction unbound in hepatocyte incubations (fu(hep)) is an important parameter in the prediction of intrinsic clearance and potential drug-drug interactions. A recent study by Austin et al. (Drug Metab Dispos 33:419-425, 2005) proposed a linear 1:1 relationship between the extent of binding to microsomes at 1 mg/ml and to hepatocytes at 10(6) million cells/ml. The current study collates a fu(mic) and fu(hep) database for 39 drugs to examine the relationship between binding in microsomes and hepatocytes. A new nonlinear empirical equation is proposed as an alternative to the linear relationship to relate binding between the two systems. The nonlinear equation results in higher prediction accuracy and lower bias in comparison to the linear relationship, in particular for drugs with fu(hep) < 0.4. The proposed equation is further extended to allow direct prediction of fu(hep) from drug lipophilicity data by substituting the fu(mic) term by the Hallifax and Houston predictive equation (Drug Metab Dispos 34:724-726, 2006). The prediction accuracy of this approach is high for relatively hydrophilic drugs (logP/D < or = 2.5), whereas less accurate prediction of the fu(hep) is observed for lipophilic drugs (logP > 3), consistent with the limitations observed for microsomal binding predictive tools. In conclusion, the proposed nonlinear equations provide an accurate predictive tool to estimate fu(hep) for the in vitro-in vivo extrapolation of intrinsic clearance and inhibition parameters.

Drug lipophilicity and microsomal protein concentration as determinants in the prediction of the fraction unbound in microsomal incubations.

Two predictive tools have been proposed by Austin et al. (Drug Metab Dispos 30:1497-1503, 2002) and Hallifax and Houston (Drug Metab Dispos 34:724-726, 2006) to estimate the fraction unbound in the incubation (fu(inc)). The current study was undertaken to elucidate the relative utility of these prediction tools over a range of drug lipophilicity and microsomal protein concentration. The fu(inc) data set (n = 127) comprised 35 drugs determined experimentally in this study and 92 collated from Austin and Hallifax data. The observed fu(inc) values at three microsomal concentrations were compared with the estimates obtained using the Austin and Hallifax equations. In addition, the impact of variability in the logP on the fu(inc) predictions was assessed. The current analysis highlights the importance of accurate estimation of lipophilicity for the prediction of the fu(inc), regardless of the prediction equation used. Both equations represent useful tools for estimation of fu(inc) for low lipophilicity drugs (logP/D = 0-3), especially at low microsomal protein concentration. However, the accuracy of fu(inc) predictions of highly lipophilic drugs was poor for both equations, implying that fu(inc) should be experimentally confirmed for drugs with logP/D >or= 3, unless the microsomal protein concentration is as low as 0.1 mg/ml, in which case a cutoff of logP/D >or= 5 can be applied. A significant difference in the predictions by the two proposed tools was observed in the area of intermediate lipophilicity (logP/D = 2.5-5), where the Hallifax equation provided more accurate fu(inc) predictions on average, irrespective of the microsomal protein concentration investigated.