PBPK Models for CYP3A4 and P‐gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

Hanke, Nina; Frechen, Sebastian; Moj, Daniel; Britz, Hannah; Eissing, Thomas; Wendl, Thomas; Lehr, Thorsten

doi:10.1002/psp4.12343

Cited by 169Pharmacogenetics and Drug MetabolismPharmaceutical studies and practicesDrug Transport and Resistance Mechanisms

PBPK Models for CYP3A4 and P‐gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

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Hanke, Nina, Frechen, Sebastian, Moj, Daniel, Britz, Hannah, Eissing, Thomas, Wendl, Thomas, Lehr, Thorsten·2018·CPT: Pharmacometrics & Systems Pharmacology

DOI 10.1002/psp4.12343PMC PMC6202474

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Abstract

According to current US Food and Drug Administration ( FDA ) and European Medicines Agency ( EMA ) guidance documents, physiologically based pharmacokinetic ( PBPK ) modeling is a powerful tool to explore and quantitatively predict drug‐drug interactions ( DDI s) and may offer an alternative to dedicated clinical trials. This study provides whole‐body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology ( OSP ) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration‐time curve ( AUC ) ratios and 94% of the peak plasma concentration (C max ) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme‐mediated and transporter‐mediated DDI s during model‐informed drug development. All presented models are provided open‐source and transparently documented.

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The findings overall were within the regulatory acceptance margin of error of 20% to 50% for these types of DDI assessments.
“These findings overall were within the regulatory acceptance margin of error which is 20% to 50% for these types of DDI assessments.12”
From GLP-1RA-induced delays in gastrointestinal motility: Predicted effects on coadministered drug absorption by PBPK analysis (2025) · cites this paper as [12]
PBPK models generally achieve AUC ratio predictions within 20% to 50% error for well-characterized drug-drug interactions.
“For reference, PBPK models generally achieve AUC ratio predictions within 20% to 50% error for well-characterized DDIs.12”
From GLP-1RA-induced delays in gastrointestinal motility: Predicted effects on coadministered drug absorption by PBPK analysis (2025) · cites this paper as [12]

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Expanded Physiologically‐Based Pharmacokinetic Model of Rifampicin for Predicting Interactions With Drugs and an Endogenous Biomarker via Complex Mechanisms Including Organic Anion Transporting Polypeptide 1B Induction
Asaumi, R., Menzel, K., Lee, W., Nunoya, K., Imawaka, H., Kusuhara, H., & Sugiyama, Y. (2019) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12457 · Cited by 37 · see citing works
Alternatives to rifampicin: A review and perspectives on the choice of strong CYP3A inducers for clinical drug–drug interaction studies
Bolleddula, J., Gopalakrishnan, S., Hu, P., Dong, J. Q., & Venkatakrishnan, K. (2022) · Clinical and Translational Science · doi:10.1111/cts.13357 · Cited by 35 · see citing works
Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions
Britz, H., Hanke, N., Taub, M. E., Wang, T., Prasad, B., Fernandez, É., Stopfer, P., Nock, V., & Lehr, T. (2020) · Pharmaceutical Research · doi:10.1007/s11095-020-02964-z · Cited by 34 · see citing works
Self-citation
Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates
Simon, F., Gautier‐Veyret, E., Truffot, A., Chenel, M., Payen, L. F., Stanke‐Labesque, F., & Tod, M. M. (2021) · Pharmaceutical Research · doi:10.1007/s11095-021-03019-7 · Cited by 34 · see citing works
The Pharmacokinetics of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone
Heinig, R., & Eißing, T. (2023) · Clinical Pharmacokinetics · doi:10.1007/s40262-023-01312-9 · Cited by 38 · see citing works
The Effects of Weak and Strong CYP3A Induction by Rifampicin on the Pharmacokinetics of Five Progestins and Ethinylestradiol Compared to Midazolam
Wiesinger, H., Klein, S., Rottmann, A., Nowotny, B., Riecke, K., Gashaw, I., Brudny-Kloeppel, M., Fricke, R., Höchel, J., & Friedrich, C. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1848 · Cited by 33 · see citing works
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
Frechen, S., Solodenko, J., Wendl, T., Dallmann, A., Ince, I., Lehr, T., Lippert, J., & Burghaus, R. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12636 · Cited by 32 · see citing works
Self-citation
GLP-1RA-induced delays in gastrointestinal motility: Predicted effects on coadministered drug absorption by PBPK analysis
Hooper, L., Liu, S., & Pai, M. P. (2025) · Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.70007 · Cited by 32 · see citing works
Data‐driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment
Fendt, R., Hofmann, U., Schneider, A. R. P., Schaeffeler, E., Burghaus, R., Yılmaz, A., Blank, L. M., Kerb, R., Lippert, J., Schlender, J., et al. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12646 · Cited by 30 · see citing works
Physiologically Based Pharmacokinetic Modelling of Cabozantinib to Simulate Enterohepatic Recirculation, Drug–Drug Interaction with Rifampin and Liver Impairment
Gerner, B., & Scherf‐Clavel, O. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13060778 · Cited by 28 · see citing works
“to simulate DDI between CAB and RIF [36].”
Influential
HybridML: Open source platform for hybrid modeling
Merkelbach, K., Schweidtmann, A. M., Müller, Y., Schwoebel, P., Mhamdi, A., Mitsos, A., Schuppert, A., Mrziglod, T., & Schneckener, S. (2022) · Computers and Chemical Engineering · doi:10.1016/j.compchemeng.2022.107736 · Cited by 31 · see citing works
P‐Glycoprotein and Breast Cancer Resistance Protein Transporter Inhibition by Cyclosporine and Quinidine on the Pharmacokinetics of Oral Rimegepant in Healthy Subjects
Bhardwaj, R., Collins, J. L., Stringfellow, J., Madonia, J., Anderson, M. S., Finley, J., Stock, D. A., Coric, V., Croop, R. S., & Bertz, R. (2022) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.1088 · Cited by 27 · see citing works
Robust physiologically based pharmacokinetic model of rifampicin for predicting drug–drug interactions via P‐glycoprotein induction and inhibition in the intestine, liver, and kidney
Asaumi, R., Nunoya, K., Yamaura, Y., Taskar, K. S., & Sugiyama, Y. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12807 · Cited by 27 · see citing works
A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators
Marok, F. Z., Wojtyniak, J., Fuhr, L. M., Selzer, D., Schwab, M. G., Weiß, J., Haefeli, W. E., & Lehr, T. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15020679 · Cited by 26 · see citing works
“Similarly, a successfully developed parent–metabolites PBPK model for itraconazole included its three metabolites that participated in the DDIs as well [22].”
BackgroundMethodSelf-citation
Physiologically‐Based Pharmacokinetic Modeling for the Prediction of a Drug–Drug Interaction of Combined Effects on P‐glycoprotein and Cytochrome P450 3A
Otsuka, Y., Choules, M. P., Bonate, P. L., & Komatsu, K. (2020) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12562 · Cited by 26 · see citing works
Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7‐Dihydroxybergamottin to Describe CYP3A4 Mediated Grapefruit‐Drug Interactions
Fuhr, L., Marok, F., Fuhr, U., Selzer, D., & Lehr, T. (2023) · Clinical pharmacology and therapy · doi:10.1002/cpt.2968 · Cited by 21 · see citing works
Self-citation
Management of Drug Interactions with Inducers: Onset and Disappearance of Induction on Cytochrome P450 3A4 and Uridine Diphosphate Glucuronosyltransferase 1A1 Substrates
Bettonte, S., Berton, M., Stader, F., Battegay, M., & Marzolini, C. (2023) · European Journal of Drug Metabolism and Pharmacokinetics · doi:10.1007/s13318-023-00833-9 · Cited by 24 · see citing works
“The disappearance of the inducing effect is governed by the t1⁄2 of the perpetrator and by the degradation constant (Kdeg) of the enzyme, which correlates with t1⁄2 of the enzyme [31].”
Background
Itraconazole and rifampicin, as CYP3A modulators but not P-gp modulators, affect the pharmacokinetics of almonertinib and active metabolite HAS-719 in healthy volunteers
Liu, L., Li, W., Yang, L., Guo, Z., Xue, H., Xie, N., & Chen, X. (2021) · Acta Pharmacologica Sinica · doi:10.1038/s41401-021-00710-8 · Cited by 24 · see citing works
Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine
Zheng, L., Yang, H., Dallmann, A., Jiang, X., Wang, L., & Hu, W. M. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.793346 · Cited by 24 · see citing works
“A sensitivity value of +1.0 denotes that a 10% increase of the examined parameter causes a 10% increase of the simulated AUC (Hanke et al., 2018).”
BackgroundInfluential
Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
Montanha, M. C., Fàbrega, F., Howarth, A., Cottura, N., Kinvig, H., Bunglawala, F., Lloyd, A. W., Denti, P., Waitt, C. J., & Siccardi, M. (2021) · Clinical Pharmacokinetics · doi:10.1007/s40262-021-01067-1 · Cited by 23 · see citing works
Physiologically based pharmacokinetic modeling of tacrolimus for food–drug and CYP3A drug–drug–gene interaction predictions
Loer, H. L. H., Feick, D., Rüdesheim, S., Selzer, D., Schwab, M. G., Teutonico, D., Frechen, S., van der Lee, M., Moes, D. J. A. R., Swen, J. J., et al. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12946 · Cited by 23 · see citing works
“Table S12: Drug-dependent parameters of the itraconazole PBPK model [7]”
BackgroundInfluentialSelf-citation
In Silico Clinical Trials: Is It Possible?
Arsène, S., Parès, Y., Tixier, E., Granjeon-Noriot, S., Martin, B., Bruezière, L., Couty, C., Courcelles, E., Kahoul, R., Pitrat, J., et al. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3449-3_4 · Cited by 24 · see citing works
A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Model of Dabigatran Etexilate, Dabigatran and Dabigatran Glucuronide in Healthy Adults and Renally Impaired Patients
Moj, D., Maas, H. J., Schaeftlein, A., Hanke, N., Gómez‐Mantilla, J. D., & Lehr, T. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00776-y · Cited by 23 · see citing works
Self-citation
Multiscale modelling of drug mechanism and safety
Zhang, J. D., Sach-Peltason, L., Krämer, C., Wang, K., & Ebeling, M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.12.009 · Cited by 22 · see citing works
A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies
Hanke, N., Türk, D., Selzer, D., Wiebe, S. T., Fernandez, É., Stopfer, P., Nock, V., & Lehr, T. (2020) · Pharmaceutics · doi:10.3390/pharmaceutics12060556 · Cited by 22 · see citing works
“(a) (b) 1 The rifampicin-verapamil DDI was predicted as induction of CYP3A4 verapamil metabolism and Pgp verapamil transport by rifampicin, with simultaneous competitive inhibition of CYP3A4 and Pgp [49].”
MethodInfluentialSelf-citation
The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development
Zhou, Z., Zhu, J., Jiang, M., Sang, L., Hao, K., & He, H. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13050704 · Cited by 22 · see citing works
Physiologically‐based pharmacokinetic modeling to evaluate in vitro‐to‐in vivo extrapolation for intestinal P‐glycoprotein inhibition
Yamazaki, S., Evers, R., & Zwart, L. D. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12733 · Cited by 22 · see citing works
Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax
Dong, J., Liu, S., Jony, M. R., Huang, C., & Miao, L.-Y. (2022) · Pharmaceutical Research · doi:10.1007/s11095-022-03289-9 · Cited by 22 · see citing works
“These values for the midazolam and alfentanil models (Supplementary Table S2 and S3) were taken from the publication (14).”
Background
Quantitative Assessment of Levonorgestrel Binding Partner Interplay and Drug‐Drug Interactions Using Physiologically Based Pharmacokinetic Modeling
Cicali, B., Lingineni, K., Cristofoletti, R., Wendl, T., Hoechel, J., Wiesinger, H., Chaturvedula, A., Vozmediano, V., & Schmidt, S. (2020) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12572 · Cited by 22 · see citing works
“Sensitivity of a parameter was calculated as a ratio of AUC change to the relative variation of that parameter, as previously reported by Hanke et al.25 Briefly, local parameter sensitivity analysis was conducted by varying each parameter of interest by +/− 10%, while keeping all other parameters constant, and recordi…”
BackgroundMethodInfluentialSelf-citation
Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models
Najjar, A., Hamadeh, A., Krause, S., Schepky, A. G., & Edginton, A. N. (2024) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13256 · Cited by 21 · see citing works
Profiling of Drug-Metabolizing Enzymes and Transporters in Human Tissue Biopsy Samples: A Review of the Literature
Rodrigues, A. D., & Rowland, A. (2019) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.119.262972 · Cited by 21 · see citing works
“As discussed in the following sections, there are examples of published reports describing biopsy data to assess CYP3A4 inhibition after dosing of a mechanism-based inhibitor drug, modeling of rifampicin autoinduction by considering the induction of P-glycoprotein (Pgp), and studying the impact of CYP3A5 genotype on P…”
Background
Physiologically‐based pharmacokinetic modeling to predict CYP3A4‐mediated drug‐drug interactions of finerenone
Wendl, T., Frechen, S., Gerisch, M., Heinig, R., & Eißing, T. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12746 · Cited by 20 · see citing works
Self-citation
Predicting drug-drug interactions with physiologically based pharmacokinetic/pharmacodynamic modelling and optimal dosing of apixaban and rivaroxaban with dronedarone co-administration
Wen, H., He, Q., Xiang, X., Jiao, Z., & Yu, J. (2022) · Thrombosis Research · doi:10.1016/j.thromres.2022.08.007 · Cited by 20 · see citing works
In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools
Krstevska, A., Djuriš, J., Ibric, S. R., & Cvijić, S. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics15010107 · Cited by 20 · see citing works
Physiologically based pharmacokinetic modeling of brivaracetam and its interactions with rifampin based on CYP2C19 phenotypes
Yang, H., Yang, L., Zhong, X., Jiang, X., Zheng, L., & Wang, L. (2022) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2022.106258 · Cited by 20 · see citing works
A Physiologically Based Pharmacokinetic and Pharmacodynamic Model of the CYP3A4 Substrate Felodipine for Drug–Drug Interaction Modeling
Fuhr, L. M., Marok, F. Z., Mees, M., Mahfoud, F., Selzer, D., & Lehr, T. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14071474 · Cited by 19 · see citing works
“For erythromycin [30], itraconazole [31], carbamazepine [32], and phenytoin (unpublished, in-house), we used available PBPK models that have been previously evaluated for DDI predictions as CYP3A4 perpetrator models.”
MethodSelf-citation
Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations
Shen, C., Shao, W., Wang, W., Sun, H., Wang, X., Geng, K., Wang, X., & Xie, H. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12971 · Cited by 18 · see citing works
Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network
Marok, F., Fuhr, L., Hanke, N., Selzer, D., & Lehr, T. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13030331 · Cited by 16 · see citing works
“rifampicin [60], fluvoxamine [61], and voriconazole [59] are listed in the parameter tables in Section 4 of the Supplementary Materials.”
BackgroundSelf-citation
Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine
Huang, W., Czuba, L. C., & Isoherranen, N. (2020) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.120.264994 · Cited by 18 · see citing works
“In contrast, mechanistic modeling can be used to extrapolate drug disposition from known settings to unstudied scenarios, such as unstudied populations and unstudied drug coadministration (Wagner et al., 2015; Huang et al., 2017; Zhang et al., 2017; Hanke et al., 2018) and likely unstudied urine pH conditions.”
Background
Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions
Loer, H. L. H., Türk, D., Gómez‐Mantilla, J. D., Selzer, D., & Lehr, T. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14050915 · Cited by 16 · see citing works
Self-citation
Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling
Sharma, S., Singh, D. K., Mettu, V. S., Yue, G., Ahire, D. S., Basit, A., Heyward, S. A., & Prasad, B. (2023) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.2c00950 · Cited by 16 · see citing works
Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy
Li, G., Yi, B., Liu, J., Jiang, X., Pan, F., Yang, W., Liu, H., Liu, Y., & Wang, G. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.746594 · Cited by 16 · see citing works
“Clinical PK studies of ketoconazole, delavirdine, and rifampicin were taken from the published literature2, 3(Hanke et al., 2018), which could be used in their respective PBPK model establishment and verification.”
Background
A Comprehensive CYP2D6 Drug–Drug–Gene Interaction Network for Application in Precision Dosing and Drug Development
Rüdesheim, S., Loer, H. L. H., Feick, D., Marok, F., Fuhr, L., Selzer, D., Teutonico, D., Schneider, A. R. P., Solodenko, J., Frechen, S., et al. (2025) · Clinical pharmacology and therapy · doi:10.1002/cpt.3604 · Cited by 16 · see citing works
Self-citation
PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug–drug interactions with clarithromycin and paroxetine
Cho, C., Kang, P., Jang, C., Lee, S., Lee, Y. J., Bae, J., & Choi, C. (2024) · Archives of Pharmacal Research · doi:10.1007/s12272-024-01495-0 · Cited by 15 · see citing works
“In the present study, the template clarithromycin model which is available in PK-Sim ® (Hanke et al. 2018) was leveraged without any adjustment and the previously established paroxetine model by Rüdesheim et al. (2022) was utilized with slight modification to the inhibition constant (K i ) of…”
A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions
Türk, D., Hanke, N., & Lehr, T. (2020) · Pharmaceutics · doi:10.3390/pharmaceutics12111074 · Cited by 15 · see citing works
“The parameter values to model these interactions were taken from literature (values and references are listed in the rifampicin drug-dependent parameter Table S17 in the Supplementary Materials) and have been evaluated in previous DDI analyses [21,22].”
MethodSelf-citation
Beyond the Michaelis–Menten: Accurate Prediction of Drug Interactions Through Cytochrome P450 3A4 Induction
Vu, N.-A. T., Song, Y.-M., Tran, Q., Yun, H.-Y., Kim, S. K., Chae, J., & Kim, J. K. (2022) · Clinical pharmacology and therapy · doi:10.1002/cpt.2824 · Cited by 13 · see citing works
Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone
Rüdesheim, S., Selzer, D., Mürdter, T. E., Igel, S., Kerb, R., Schwab, M. G., & Lehr, T. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14081734 · Cited by 14 · see citing works
Self-citation
In‐depth analysis of patterns in selection of different physiologically‐based pharmacokinetic modeling tools: Part II — Assessment of model reusability and comparison between open and non‐open source‐code software
Aldibani, H. K. A., Rajput, A. J., & Rostami‐Hodjegan, A. (2023) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2360 · Cited by 13 · see citing works
Physiologically‐based pharmacokinetic modeling to predict drug interactions of lemborexant with CYP3A inhibitors
Ueno, T., Miyajima, Y., Landry, I. S., Lalovic, B. B., & Schuck, E. L. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12606 · Cited by 14 · see citing works
Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation
Shriya, V. A., Nayak, U. Y., Sathyanarayana, M. B., Chaudhari, B. B., & Bhat, K. (2025) · AAPS PharmSciTech · doi:10.1208/s12249-025-03093-9 · Cited by 13 · see citing works
“In such case the integration of Ki,u in PBPK model becomes necessary to address drugs impact on exposure of victim drug [98].”
Population Pharmacokinetic and Pharmacodynamic Analysis of GLPG1690, an Autotaxin Inhibitor, in Healthy Volunteers and Patients with Idiopathic Pulmonary Fibrosis
Taneja, A., Desrivot, J., Diderichsen, P. M., Blanqué, R., Allamassey, L., Fagard, L., Fieuw, A. M., van der Aar, E., & Namour, F. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00755-3 · Cited by 13 · see citing works
Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies
Karnati, P., Murthy, A. N., Gundeti, M., & Ahmed, T. (2023) · The AAPS Journal · doi:10.1208/s12248-023-00831-4 · Cited by 13 · see citing works
“In the drug development process, PBPK modeling is frequently applied to waive off drug-drug interaction studies [11, 61].”
Background
Physiologically based pharmacokinetic modeling of imatinib and N‐desmethyl imatinib for drug–drug interaction predictions
Loer, H. L. H., Kovar, C., Rüdesheim, S., Marok, F., Fuhr, L., Selzer, D., Schwab, M., & Lehr, T. (2024) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13127 · Cited by 7 · see citing works
Self-citation
Does the circulating ketoconazole metabolite N-deacetyl ketoconazole contribute to the drug-drug interaction potential of the parent compound?
Weiß, J., Foerster, K. I., Weber, M., Burhenne, J., Mikus, G., Lehr, T., & Haefeli, W. E. (2021) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2021.106076 · Cited by 13 · see citing works
“…based pharmacokinetic (PBPK) model for drug-drug interactions suggested that the inhibitor action cannot be fully explained by the parent compound (Lehr et al., unpublished data) and is possibly related to metabolites with inhibitory potential, as seen for itraconazole (Hanke et al., 2018).”
BackgroundSelf-citation
Impact of interleukin‐6 on drug transporters and permeability in the hCMEC/D3 blood–brain barrier model
Simon, F., Guyot, L., Garcia, J., Vilchez, G., Bardel, C., Chenel, M., Tod, M., & Payen, L. (2020) · Fundamental & Clinical Pharmacology · doi:10.1111/fcp.12596 · Cited by 11 · see citing works
Drug Interaction PBPK Modeling: Review of the Literature Exposes the Need for Increased Verification of Model Inputs and Outputs as Part of Credibility Assessment
Rodrigues, D., Gibson, C. R., & Isoherranen, N. (2025) · Clinical and Translational Science · doi:10.1111/cts.70299 · Cited by 12 · see citing works
“Therefore, induction of CYP3A4 in the intestine versus liver by different agents has garnered the attention of various investigators [59, 61–66].”
Physiologically-based pharmacokinetic modeling to inform dosing regimens and routes of administration of rifampicin and colistin combination against Acinetobacter baumannii
Zhang, J., Song, C., Wu, M., Yue, J., Zhu, S., Zhu, P., Oo, C., Schlender, J., Lv, Z., Zhu, Y., et al. (2023) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2023.106443 · Cited by 12 · see citing works
Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine’s Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes
Yin, X., Cicali, B., Rodríguez‐Vera, L., Lukáčová, V., Cristofoletti, R., & Schmidt, S. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16060737 · Cited by 12 · see citing works
“The sensitivity of a parameter was calculated based on Equation (2), reported by Hanke et al. [44].”
Method
Determining the Effects of Chronic Kidney Disease on Organic Anion Transporter1/3 Activity Through Physiologically Based Pharmacokinetic Modeling
Dubinsky, S., Malik, P. R. V., Hajducek, D. M., & Edginton, A. N. (2022) · Clinical Pharmacokinetics · doi:10.1007/s40262-022-01121-6 · Cited by 13 · see citing works
Physiologically‐based pharmacokinetic modeling of quinidine to establish a CYP3A4 , P‐gp, and CYP2D6 drug–drug–gene interaction network
Feick, D., Rüdesheim, S., Marok, F. Z., Selzer, D., Loer, H. L. H., Teutonico, D., Frechen, S., van der Lee, M., Moes, D. J. A. R., Swen, J. J., et al. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12981 · Cited by 12 · see citing works
Self-citation
Predicting Interactions between Rifampin and Antihypertensive Drugs Using the Biopharmaceutics Drug Disposition Classification System
Liu, W., Yan, T., Chen, K., Yang, L., Benet, L. Z., & Zhai, S. (2020) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.2380 · Cited by 12 · see citing works
Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study
Asai, Y., Tashiro, T., Kondo, Y., Hayashi, M., Arihara, H., Omote, S., Tanio, E., Yamashita, S., Higuchi, T., Hashimoto, E., et al. (2023) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.b22-00823 · Cited by 11 · see citing works
Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective
Reddy, M. B., Cabalu, T. D., Zwart, L. D., Ramsden, D., Dowty, M. E., Taskar, K. S., Badée, J., Bolleddula, J., Boulu, L. G., Fu, Q., et al. (2024) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3477 · Cited by 11 · see citing works
Understanding Voriconazole Metabolism: A Middle-Out Physiologically-Based Pharmacokinetic Modelling Framework Integrating In Vitro and Clinical Insights
Saleh, A., Schulz, J., Schlender, J., Aulin, L. B. S., Konrad, A.-P., Kluwe, F., Mikus, G., Huisinga, W., Kloft, C., & Michelet, R. (2024) · Clinical Pharmacokinetics · doi:10.1007/s40262-024-01434-8 · Cited by 11 · see citing works
Physiologically based pharmacokinetic combined JAK2 occupancy modelling to simulate PK and PD of baricitinib with kidney transporter inhibitors and in patients with hepatic/renal impairment
Wang, Z., Liu, W., Li, X., Chen, H.-J., Qi, D., Pan, F., Liu, H., Yu, S., Yi, B., Wang, G., et al. (2022) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2022.105210 · Cited by 11 · see citing works
Results From Drug–Drug Interaction Studies In Vitro and In Vivo Investigating the Inhibitory Effect of Finerenone on the Drug Transporters BCRP, OATP1B1, and OATP1B3
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BackgroundMethodInfluentialSelf-citation

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High-performance PBPK model for predicting CYP3A4 induction-mediated drug interactions: a refined and validated approach
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Authors: Hanke, N., Frechen, S., Moj, D., Britz, H., Eissing, T., Wendl, T., & Lehr, T.
Affiliations: Saarland University (DE)
Bayer (Germany) (DE)
Corresponding author: Lehr, Thorsten
ORCID: Hanke, Nina
Frechen, Sebastian
Eissing, Thomas
Wendl, Thomas
Lehr, Thorsten
Venue: CPT: Pharmacometrics & Systems Pharmacology
Publisher: Wiley
Published: 2018-09-07
Volume: 7
Issue: 10
Pages: 647-659
ISSN: 2163-8306 (ISSN-L)
DOI: 10.1002/psp4.12343
PMC: PMC6202474
OpenAlex: W2885852526
Cited by: 169
References: 48
Citations by year: 2026: 14 · 2025: 23 · 2024: 22 · 2023: 24 · 2022: 29 · 2021: 27 · 2020: 18 · 2019: 12
FWCI: 15.68
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
Locations: CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Publications of the UdS (Saarland University)
Funders: Bayer
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
Keywords: physiologically based pharmacokinetic modeling, drug-drug interactions, CYP3A4, P-glycoprotein, rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, digoxin, model-informed drug development
MeSH terms: Digoxin, Drug Interactions, Humans, Midazolam, Models, Biological, Rifampin, Alfentanil, Clarithromycin, Itraconazole, ATP Binding Cassette Transporter, Subfamily B, Member 1, Cytochrome P-450 CYP3A

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PBPK Models for CYP3A4 and P‐gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

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