Helena Leonie Hanae Loer (1*), Simeon Rüdesheim (1,2*), Denise Feick (1), Dominik Selzer (1), Matthias Schwab (2,3,4), and Thorsten Lehr (1)
* Authors contributed equally; (1) Clinical Pharmacy, Saarland University, Saarbrücken, Germany; (2) Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany; (3) Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany; (4) Cluster of Excellence iFIT (EXC2180) “Image-guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
Introduction: Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) are key drivers of adverse drug reactions [1], posing substantial challenges in clinical practice and pharmacological research. When these interactions intertwine, they form complex drug-drug-gene interactions (DDGIs), further complicating patient management and therapeutic outcomes. In this context, the cytochrome P450 (CYP) 2D6 enzyme, responsible for the metabolism of 20–25% of clinically used drugs [2,3], is particularly important due to the high prevalence of structural and allelic variants leading to large interindividual variability in enzymatic activity. Hence, the impact of CYP2D6 DD(G)Is on the pharmacokinetics (PK) and consequently on the safety and efficacy of CYP2D6 substrates warrants in-depth investigations and quantification to improve personalized therapy. Here, physiologically based pharmacokinetic (PBPK) modeling serves as an optimal tool, offering a detailed mechanistic framework for examining and predicting the effects of DD(G)Is [4].
Objectives: Establishment and evaluation of a comprehensive CYP2D6 DD(G)I network extending and combining previously published networks and CYP2D6 victim and perpetrator models.
Methods: DD(G)I network development with the modeling software PK-Sim® (Version 11, www.open-systems-pharmacology.org) was initiated with a comprehensive literature search for clinical DD(G)I study data focusing on CYP2D6 victim drugs. DD(G)I studies were included based on the availability of PBPK models developed in the OSP suite for both victim and perpetrator drugs, and the availability of published plasma concentration-time profiles of the victim with and without interaction effects. In addition, a new PBPK model for the CYP2D6 substrate desipramine was developed analogous to previously published models. Simulated DD(G)I scenarios were visually assessed by comparing predicted and observed plasma profiles of the victim. Moreover, for each DD(G)I scenario, both predicted and observed ratios of two key PK parameters were calculated and compared: the area under the plasma concentration-time curve (AUC) and the maximum plasma concentration (Cmax). Geometric mean fold errors (GMFEs) were determined for all predicted DD(G)I PK parameter ratios.
Results: The developed DD(G)I network comprises PBPK models of 23 drugs including relevant metabolites [5–21]. It covers eight CYP2D6 victim drugs, i.e., atomoxetine, (E)-clomiphene, desipramine, dextromethorphan, metoprolol, mexiletine, paroxetine, and risperidone, as well as six CYP2D6 perpetrator drugs, i.e., atomoxetine, bupropion, cimetidine, fluvoxamine, paroxetine, and quinidine. Additionally, DD(G)Is mediated by CYP3A4 and P-glycoprotein involving substrates or inhibitors of CYP2D6 were included. Integrated interaction mechanisms were competitive inhibition, mechanism-based inactivation, down-regulation, and induction. A total of 45 clinical DDI and 7 DDGI studies were used, covering 32 drug combinations and 71 DD(G)I scenarios. Moreover, 30 CYP2D6 DGI studies involving CYP2D6 phenotypes and activity scores covering the whole range of CYP2D6 activity were included. The DD(G)I network showed good predictive performance with mean GMFE values (range) of 1.40 (1.00–6.19) and 1.29 (1.00–2.95) for the predicted DGI AUC and Cmax ratios as well as 1.38 (1.00–3.64) and 1.43 (1.01–3.41) for the predicted DDI AUC and Cmax ratios, respectively. For the simulated DDGI scenarios, mean GMFE values (range) of 1.56 (1.01–3.55) and 1.60 (1.02–3.71) were calculated for the predicted AUC and Cmax ratios.
Conclusions: In this study we successfully developed a comprehensive CYP2D6 DD(G)I network covering a broad range of CYP2D6 victim and perpetrator drugs to predict DGIs, DDIs, and DDGIs. The network can be extended with any existing and evaluated PK-Sim® victim or perpetrator model and can serve as a basis for the prediction of clinically untested CYP2D6 DD(G)I and even multiple DD(G)I scenarios involving more than one perpetrator. Due to the resulting large number of combinatorial possibilities of predictable interactions, the developed network can facilitate the determination of dose recommendations and can be applied to support model-based precision dosing for patients. Additionally, the network provides insights for model-informed drug development, such as conducting interaction screenings, thereby supporting safer and more effective drug use.
References:
[1] Pirmohamed, M.; James, S.; Meakin, S.; Green, C.; Scott, A.K.; Walley, T.J.; Farrar, Keith, Park, B.K.; Breckenridge, A.M. Adverse Drug Reactions as Cause of Admission to Hospital: Prospective Analysis of 18 820 Patients. BMJ 2004, 329, 15–19, doi:10.1136/bmj.329.7456.15.
[2] Bahar, M.A.; Setiawan, D.; Hak, E.; Wilffert, B. Pharmacogenetics of Drug-Drug Interaction and Drug-Drug-Gene Interaction: A Systematic Review on CYP2C9, CYP2C19 and CYP2D6. Pharmacogenomics 2017, 18, 701–739, doi:10.2217/pgs-2017-0194.
[3] Zanger, U.M.; Turpeinen, M.; Klein, K.; Schwab, M. Functional Pharmacogenetics/Genomics of Human Cytochromes P450 Involved in Drug Biotransformation. Anal. Bioanal. Chem. 2008, 392, 1093–1108, doi:10.1007/s00216-008-2291-6.
[4] Türk, D.; Fuhr, L.M.; Marok, F.Z.; Rüdesheim, S.; Kühn, A.; Selzer, D.; Schwab, M.; Lehr, T. Novel Models for the Prediction of Drug–Gene Interactions. Expert Opin. Drug Metab. Toxicol. 2021, 17, 1293–1310, doi:10.1080/17425255.2021.1998455.
[5] Marok, F.Z.; Fuhr, L.M.; Hanke, N.; Selzer, D.; Lehr, T. Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network. Pharmaceutics 2021, 13, 331, doi:10.3390/pharmaceutics.
[6] Marok, F.Z.; Wojtyniak, J.-G.; Fuhr, L.M.; Selzer, D.; Schwab, M.; Weiss, J.; Haefeli, W.E.; Lehr, T. A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators. Pharmaceutics 2023, 12, 679.
[7] Rüdesheim, S.; Selzer, D.; Fuhr, U.; Schwab, M.; Lehr, T. Physiologically- Based Pharmacokinetic Modeling of Dextromethorphan to Investigate Interindividual Variability within CYP2D6 Activity Score Groups. CPT Pharmacometrics Syst. Pharmacol. 2022, 11, 494–511, doi:10.1002/psp4.12776.
[8] Rüdesheim, S.; Wojtyniak, J.G.; Selzer, D.; Hanke, N.; Mahfoud, F.; Schwab, M.; Lehr, T. Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions. Pharmaceutics 2020, 12, 1200, doi:10.3390/pharmaceutics12121200.
[9] Hanke, N.; Frechen, S.; Moj, D.; Britz, H.; Eissing, T.; Wendl, T.; Lehr, T. PBPK Models for CYP3A4 and P-Gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst. Pharmacol. 2018, 7, 647–659, doi:10.1002/psp4.12343.
[10] Rüdesheim, S.; Selzer, D.; Mürdter, T.; Igel, S.; Kerb, R.; Schwab, M.; Lehr, T. Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone. Pharmaceutics 2022, 14, 1734, doi:10.3390/pharmaceutics14081734.
[11] Kneller, L.A.; Abad-Santos, F.; Hempel, G. Physiologically Based Pharmacokinetic Modelling to Describe the Pharmacokinetics of Risperidone and 9-Hydroxyrisperidone According to Cytochrome P450 2D6 Phenotypes. Clin. Pharmacokinet. 2020, 59, 51–65, doi:10.1007/s40262-019-00793-x.
[12] Kanacher, T.; Lindauer, A.; Mezzalana, E.; Michon, I.; Veau, C.; Mantilla, J.D.G.; Nock, V.; Fleury, A. 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. Pharmaceutics 2020, 12, 1191, doi:10.3390/pharmaceutics12121191.
[13] Kovar, C.; Kovar, L.; Rüdesheim, S.; Selzer, D.; Ganchev, B.; Kröner, P.; Igel, S.; Kerb, R.; Schaeffeler, E.; Mürdter, T.E.; et al. Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling. Pharmaceutics 2022, 14, 2604, doi:10.3390/pharmaceutics14122604.
[14] 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. Physiologically-Based Pharmacokinetic Modeling of Quinidine to Establish a CYP3A4, P-Gp, and CYP2D6 Drug-Drug-Gene Interaction Network. CPT pharmacometrics Syst. Pharmacol. 2023, 12, 1143–1156, doi:10.1002/psp4.12981.
[15] Hanke, N.; Türk, D.; Selzer, D.; Ishiguro, N.; Ebner, T.; Wiebe, S.; Müller, F.; Stopfer, P.; Nock, V.; Lehr, T. A Comprehensive Whole‑body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals. Clin. Pharmacokinet. 2020, 59, 1419–1431, doi:10.1007/s40262-020-00896-w.
[16] Britz, H.; Hanke, N.; Volz, A.-K.; Spigset, O.; Schwab, M.; Eissing, T.; Wendl, T.; Frechen, S.; Lehr, T. Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam. CPT pharmacometrics Syst. Pharmacol. 2019, 8, 296–307, doi:10.1002/psp4.12397.
[17] Moj, D.; Hanke, N.; Britz, H.; Frechen, S.; Kanacher, T.; Wendl, T.; Haefeli, W.E.; Lehr, T. Clarithromycin, Midazolam, and Digoxin: Application of PBPK Modeling to Gain New Insights into Drug–Drug Interactions and Co-Medication Regimens. AAPS J. 2017, 19, 298–312, doi:10.1208/s12248-016-0009-9.
[18] Hanke, N.; Türk, D.; Selzer, D.; Wiebe, S.; Fernandez, É.; Stopfer, P.; Nock, V.; Lehr, T. A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies. Pharmaceutics 2020, 12, 556, doi:10.3390/pharmaceutics12060556.
[19] Fuhr, L.M.; Marok, F.Z.; Hanke, N.; Selzer, D.; Lehr, T. Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug–Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach. Pharmaceutics 2021, 13, 270, doi:10.3390/pharmaceutics13020270.
[20] PBPK Model Repository Alprazolam. Available online: https://github.com/Open-Systems-Pharmacology/Alprazolam-Model/tree/v1.1.
[21] PBPK Model Repository Erythromycin. Available online: https://github.com/Open-Systems-Pharmacology/Erythromycin-Model/tree/v1.3.
Reference: PAGE 32 (2024) Abstr 11232 [www.page-meeting.org/?abstract=11232]
Poster: Drug/Disease Modelling - Absorption & PBPK