A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug–Drug–Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine

Abstract

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

“…Of note there was no inhibition of CYP2C19 observed for moclobemide in hepatocytes despite a clinically relevant interaction with omeprazole (AUCR = 2.07). Moclobemide has been reported to inhibit CYP2C19 in vitro and TDI parameters were estimated based on clinical observations of autoinhibition (Kanacher et al, 2020).…”

A generic framework for the physiologically‐based pharmacokinetic platform qualification of PK‐Sim and its application to predicting cytochrome P450 3A4–mediated drug–drug interactions

“…The core of the herein presented established qualification framework represents an automated workflow that generates comprehensive qualification reports based on predefined qualification plans with prespecified qualification measures and charts assessing the predictive performance to demonstrate the platform's overall capability for the particular use case. Although similarly comprehensive reports have been published in the past with other PBPK platforms, such as Simcyp™ for use cases such as “Prediction of drug‐drug interactions using PBPK models of CYP450 modulators,” 10 “PBPK Modeling of Drugs Extensively Metabolized by Major Cytochrome P450 s in Children,” 11 or “PBPK models of Renally Cleared Drugs in Children” 12 or with PK‐Sim ® for the use cases “CYP3A4 and P‐gp DDI Prediction” 13 and “Prediction of CYP1A2 and CYP2C19 Drug‐Drug‐Gene Interactions,” 14 all of these high‐quality reports just reflect a snapshot in time being only applicable for the current version of the used PBPK platform. Contrary to this, the framework presented here focused on sustainability and was designed to easily recreate the qualification report and thus requalify the use case with every upcoming version of PK‐Sim ® .…”

“…8 Similarly, the FDA asks in its current guidance on PBPK reporting for a rigorous demonstration of the level of confidence in PBPK analyses for their intended uses. 9 Although several reports demonstrated good predictive performance for different PBPK platforms in various application areas, [10][11][12][13][14] all of these potential "qualifications" reflect just a snapshot in time in terms of a temporary qualification of the current version of the respective PBPK platform. Even if a use case from such a publication was considered "qualified," changes and updates in the PBPK platform (e.g., adjusted model structure, changes in model parameterization) with new software version releases would require (re-)qualification.…”

Drug Interactions Involving 17α‐Ethinylestradiol: Considerations Beyond Cytochrome P450 3A Induction and Inhibition

“…Even when mechanism‐based (time‐dependent) inhibition is observed in vitro , the determined parameters ( K I , half‐maximal rate of inactivation > 800 nM; and k inact , maximal rate of inactivation = 0.04–0.25 min −1 ) cannot at face value be used in models to rationalize clinically observed DDI. In particular, CYP1A2 has garnered the attention of various investigators and some have evoked physiologically‐based PK (PBPK) model input k inact values for EE as high a 200 min −1 to recapitulate the observed AUC ratio (AUCR; AUC inhibitor /AUC control ) with caffeine and tizanidine ( Table 2 , Table ) 15 . Unfortunately, CYP1A2 is weakly inhibited by EE in vitro (vs. other CYPs) and there is no evidence for such “time‐dependent” inhibition ( Table ) 14 …”

“…So how is it possible that low dose EE‐containing OC bring about significant inhibition of CYP1A2 in vivo , and yet EE is a relatively weak reversible inhibitor of the enzyme in vitro with no time‐dependency evident ( Table 2 , Figure , Table )? 14 Despite no evidence for time‐dependency in vitro , some investigators have used very high PBPK model k inact input values (200 min −1 ) to recapitulate the observed DDI in their models 15 . A simple static analysis revealed that it is possible to predict the AUCR of CYP1A2 drugs, such as caffeine and theophylline ( Table ), but admittedly k inact values approaching 100 min −1 for any CYP are unprecedented.…”