Physiologically‐Based Pharmacokinetic Models for CYP1A2 Drug–Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam

Abstract

This study provides whole‐body physiologically‐based pharmacokinetic models of the strong index cytochrome P450 ( CYP )1A2 inhibitor and moderate CYP 3A4 inhibitor fluvoxamine and of the sensitive CYP 1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug–drug interaction ( DDI ) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP 1A2 substrate), rifampicin (moderate CYP 1A2 inducer), and midazolam (sensitive index CYP 3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve ( AUC ) ratios ( AUC during DDI / AUC control) and seven of seven of the predicted DDI peak plasma concentration (C max ) ratios (C max during DDI /C max control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community.

Physiologically Based Precision Dosing Approach for Drug‐Drug‐Gene Interactions: A Simvastatin Network Analysis

“…In contrast, PBPK models are well-suited to tackle this limitation and are emphasized by regulatory agencies to investigate new, untested scenarios. 4,8,[10][11][12] At the moment, most wholebody PBPK models purely account for interindividual variability by adapting the physiology of the underlying virtual patient. Hence, the estimation of individual parameters, as it is accomplished in Bayesian methods, is hardly feasible.…”

“…The final model covered four important polymorphisms in the ABCB1, SLCO1B1, ABCG2, and CYP3A5 genes 20,[31][32][33][34][35] relevant for simvastatin's PK and was tested using previously developed and evaluated models for the perpetrators itraconazole, rifampicin, clarithromycin, gemfibrozil, and the victim midazolam. [10][11][12]23 The simvastatin network showed overall good descriptive and predictive performance and was hence used for further dose optimization analysis. Despite good performance, the model has some limitations, which are primarily caused by insufficient or lacking model input data.…”

“…This is not surprising, because the originally published clarithromycin and itraconazole models did not include CYP3A5 inhibition (see Supplementary Material, chapter 3). 10,23 Although there are hints of CYP3A5 inhibition by itraconazole or clarithromycin in past studies, information available were too sparse to include this process in the models. 40,41 This lack of information is most likely due to the fact that the CYP3A5*3/*3 nonexpressor genotype is the major genotype in many populations without recent African ancestry.…”

“…4 The reliability of this technique has already been demonstrated in several DDI and DGI studies and is acknowledged by regulatory agencies. [10][11][12] However, although PBPK modeling has been accepted as a useful option to predict the extent of DDGIs, examples on how PBPK modeling can be used for model informed precision dosing (MIPD) are still scarce. 12 Thus, the aim of this work was to illustrate the complexity of DDIs, DGIs, and DDGIs based on the example of simvastatin.…”

Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

“…The model includes rifampicin transport via OATP1B1 and P-glycoprotein (Pgp), metabolism via the arylacetamide deacetylase (AADAC), as well as auto-induction of OATP1B1, Pgp and AADAC (26). The good DDI performance of the model was demonstrated in many different applications (26,(28)(29)(30)(31). Mathematical implementation of the DDI processes is specified in Section 1 of the ESM.…”