Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Abstract

ABSTRACT:Accurate prediction of the extent of mechanism-based CYP3A inhibition is critical in determining the timing of clinical drug interaction studies in drug development. To evaluate the prediction accuracy of the static and Simcyp time-based approaches, 54 clinical drug interactions involving mechanism-based CYP3A inhibitors were predicted using both methods. The Simcyp timebased approach generated better prediction when 0.03 h ؊1 was used as the hepatic CYP3A enzyme degradation rate constant (k deg ) value. Of the predictions 87 and 55% had an error less than 2 and 0. Drug-drug interactions remain an area of focus in drug discovery and development. Mechanism-based inhibition of CYP3A is one of the major causes of clinical drug-drug interactions and generally leads to greater concern, as highlighted by the list of moderate and strong CYP3A inhibitors in the Food and Drug Administration (2006) Drug Interaction Guidance. The inhibitory effects of a mechanism-based CYP3A inhibitor persist long after the compound is eliminated from the body because the recovery of CYP3A enzyme activity requires de novo protein synthesis or slow release of the enzyme from the enzyme-inhibitor complex. Because of the primary role of CYP3A in drug disposition, prediction of the clinical drug interaction potential of a mechanism-based CYP3A inhibitor would be helpful in guiding the timing and design of clinical studies.Several approaches have been developed to predict clinical outcomes of mechanism-based inhibition. The static mathematical model developed by Mayhew et al. (2000) is a commonly used approach to predict mechanism-based drug interactions from in vitro estimated inactivation parameters. Several modifications to the original model have been made to incorporate the effects of intestinal wall metabolism (Wang et al., 2004b), competitive inhibition, and induction (Fahmi et al., 2009). Nonetheless, these static models are only capable of predicting the average magnitude of drug interactions across a population, assuming that the steady state of enzyme inhibition has been reached. Temporal changes in inhibitor concentrations and CYP3A enzyme activities as well as interindividual variability in CYP3A enzyme levels and rate constants of enzyme degradation are not considered. In addition, it is difficult to assess the effects of dosing regimens (e.g., irregular dosing) on the extent of drug interactions using a static model.Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al., 2000;Zhang et al., 2009;Fenneteau et al., 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models. They can be used to simulate drug concentrationtime profiles and to explore the effects of various dosing regimens. Simcyp (Simcyp Limited, Sheffield, UK) is a commercially available absorption, distribution, metabolism, and...

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model

“…On the basis of the comparison of crizotinib steadystate simulations between on and off (without an interacting drug), crizotinib CYP3A net-inhibition potential (i.e., on/off ratio) was predicted as 2-to 3-fold (Table 5). This moderate on/off ratio appeared consistent with the midazolam AUC R when taking into account the difference in f m,CYP3A4 and F g between crizotinib (0.8 and 0.9, respectively) and midazolam (0.9 and 0.6, respectively) (Wang, 2010). Conversely, the observed crizotinib NSI of 1.3 suggested that crizotinib CYP3A net-inhibition potential was negligible to weak (Table 5).…”

Evaluation of the Use of Static and Dynamic Models to Predict Drug-Drug Interaction and Its Associated Variability: Impact on Drug Discovery and Early Development

“…It is worthwhile to note that for 15 of the 16 interactions for which C max ratios were available, the DDI risk indicated by clinical C max ratios was less than or comparable to (AZ8-ketoconazole interaction) that corresponding to AUC ratios. This finding is in keeping with the smaller range of C max ratios compared with AUC ratios reported for 54 clinical DDIs involving mechanism-based CYP3A inhibitors (Wang, 2010). Assuming that C max is affected by first pass, this reflects a reduced importance of first pass (hepatic, intestinal, or both) in DDIs.…”

“…Other retrospective validations (Wang, 2010;Guest et al, 2011;Shardlow et al, 2011) indicate comparable predictions. Guest et al (2011) reported that Simcyp and static models predicted 71 and 77%, respectively, of the DDIs within 2-fold.…”

“…A combined static model was compared with Simcyp for 30 DDIs involving midazolam and various CYP3A4 inhibitors (Fahmi et al, 2009). Wang (2010) compared the models for 54 interactions perpetrated by mechanism-based inhibitors of CYP3A4. Guest et al (2011) used 35 DDIs to compare Simcyp V8's time-based model with its implementation of static models.…”

Determination of Time-Dependent Inactivation of CYP3A4 in Cryopreserved Human Hepatocytes and Assessment of Human Drug-Drug Interactions

“…These simulations usually incorporate TDI kinetic parameters that were obtained from HLM experiments, and these predictions using HLM data often yield gross overpredictions of the magnitude of effect when compared with in vivo clinical data (Xu et al, 2009;Wang, 2010). The reasons for this overprediction are not clearly understood, but they may reflect inaccuracies in system parameters such as k deg and a more complex interplay between metabolic activation and inactivation, protein degradation and synthesis, and/or hepatocellular concentrations because of active transport uptake or efflux.…”