Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective.
Journal
Clinical pharmacology and therapeutics
ISSN: 1532-6535
Titre abrégé: Clin Pharmacol Ther
Pays: United States
ID NLM: 0372741
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
received:
29
07
2021
accepted:
18
11
2021
pubmed:
5
12
2021
medline:
14
9
2022
entrez:
4
12
2021
Statut:
ppublish
Résumé
The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.
Substances chimiques
Cytochrome P-450 Enzyme System
9035-51-2
Cytochrome P-450 CYP3A
EC 1.14.14.1
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
770-781Informations de copyright
© 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics.
Références
Zhou, S.F. Drugs behave as substrates, inhibitors and inducers of human cytochrome P450 3A4. Curr. Drug Metab. 9, 310-322 (2008).
Kenny, J.R. et al. Considerations from the innovation and quality induction working group in response to drug-drug interaction guidances from regulatory agencies: focus on CYP3A4 mRNA in vitro response thresholds, variability, and clinical relevance. Drug Metab. Dispos. 46, 1285-1303 (2018).
Burk, O. et al. The induction of cytochrome P450 3A5 (CYP3A5) in the human liver and intestine is mediated by the xenobiotic sensors pregnane X receptor (PXR) and constitutively activated receptor (CAR). J. Biol. Chem. 279, 38379-38385 (2004).
Pearson, J.T. et al. Differential time-dependent inactivation of P450 3A4 and P450 3A5 by raloxifene: a key role for C239 in quenching reactive intermediates. Chem. Res. Toxicol. 20, 1778-1786 (2007).
Hariparsad, N. et al. Considerations from the IQ induction working group in response to drug-drug interaction guidance from regulatory agencies: focus on downregulation, CYP2C induction, and CYP2B6 positive control. Drug Metab. Dispos. 45, 1049-1059 (2017).
Ramsden, D. et al. Perspectives from the Innovation and Quality Consortium Induction Working Group on factors impacting clinical drug-drug interactions resulting from induction: focus on cytochrome 3A substrates. Drug Metab. Dispos. 47, 1206-1221 (2019).
Wong, S.G. et al. Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidance from Regulatory Agencies: guidelines on model fitting and recommendations on time course for in vitro cytochrome P450 induction studies including impact on drug interaction risk assessment. Drug Metab. Dispos. 49(1), 94-110 (2021).
Harvey, R.D. et al. Modernizing clinical trial eligibility criteria: recommendations of the ASCO-friends of cancer research washout period and concomitant medication work group. Clin. Cancer Res. 27, 2400-2407 (2021).
Venkatakrishnan, K., Pickard, M.D. & von Moltke, L.L. A quantitative framework and strategies for management and evaluation of metabolic drug-drug interactions in oncology drug development: new molecular entities as object drugs. Clin. Pharmacokinet. 49, 703-727 (2010).
EMA. Guideline on the investigation of drug interactions 2012.
FDA. In vitro drug interaction studies - cytochrome P450 enzyme- and transporter- mediated drug interaction guidance for industry. 2020.
Pharmaceuticals and Medical Devices Agency (PMDA). Pharmaceuticals and Medical Devices Agency (Japan): drug interaction guideline for drug development and labelling recommendations. 2018.
Sudsakorn, S., Bahadduri, P., Fretland Jfer, F. & Lu, C. 2020 FDA drug-drug interaction guidance: a comparison analysis and action plan by pharmaceutical industrial scientists. Curr. Drug Metab. 21, 403-426 (2020).
M12 I. Final concept paper, M12: drug interaction studies. 2019.
Fahmi, O.A., Kish, M., Boldt, S. & Obach, R.S. Cytochrome P450 3A4 mRNA is a more reliable marker than CYP3A4 activity for detecting pregnane X receptor-activated induction of drug-metabolizing enzymes. Drug Metab. Dispos. 38, 1605-1611 (2010).
Fahmi, O.A., Boldt, S., Kish, M., Obach, R.S. & Tremaine, L.M. Prediction of drug-drug interactions from in vitro induction data: application of the relative induction score approach using cryopreserved human hepatocytes. Drug Metab. Dispos. 36, 1971-1974 (2008).
Samineni, D. et al. Physiologically based pharmacokinetic model-informed drug development for polatuzumab vedotin: label for drug-drug interactions without dedicated clinical trials. J. Clin. Pharmacol. 60(Suppl 1), S120-S131 (2020).
Chen, Y. et al. Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody-drug conjugates. Clin. Pharmacokinet. 54, 81-93 (2015).
Han, T.H. et al. CYP3A-mediated drug-drug interaction potential and excretion of brentuximab vedotin, an antibody-drug conjugate, in patients with CD30-positive hematologic malignancies. J. Clin. Pharmacol. 53, 866-877 (2013).
Genentech Inc. Polivy® (polatuzumab vedotin) Prescribing Information <https://www.accessdata.fda.gov/drugsatfda_docs/label/2019> (2019). Accessed May 1, 2021.
Roche Pharma AG. Polivy (polatuzumab vedotin) Summary of Product Characteristics <https://www.ema.europa.eu/en/documents/product-information/polivy-eparproduct-information_en.pdf> (2020). Accessed May 1, 2021.
Bolleddula, J., Ke, A., Yang, H. & Prakash, C. PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction. CPT Pharmacometr. Syst. Pharmacol. 10, 577-588 (2021).
Prakash, C., Fan, B., Ke, A., Le, K. & Yang, H. Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia. Cancer Chemother. Pharmacol. 86, 619-632 (2020).
Agios Pharmaceuticals. Tibsovo (Ivosidenib tablets) US Prescribing Information. 2018.
Yee, K.L. et al. Physiologically based pharmacokinetic modeling of doravirine and its major metabolite to support dose adjustment with rifabutin. J. Clin. Pharmacol. 61, 394-405 (2021).
Côté, B. et al. Discovery of MK-1439, an orally bioavailable non-nucleoside reverse transcriptase inhibitor potent against a wide range of resistant mutant HIV viruses. Bioorg. Med. Chem. Lett. 24, 917-922 (2014).
Sanchez, R.I. et al. Characterisation of the absorption, distribution, metabolism, excretion and mass balance of doravirine, a non-nucleoside reverse transcriptase inhibitor in humans. Xenobiotica 49, 422-432 (2019).
Khalilieh, S.G. et al. Multiple doses of rifabutin reduce exposure of doravirine in healthy subjects. J. Clin. Pharmacol. 58, 1044-1052 (2018).
Gu, H. et al. Simultaneous Physiologically Based Pharmacokinetic (PBPK) modeling of parent and active metabolites to investigate complex CYP3A4 drug-drug interaction potential: a case example of midostaurin. Drug Metab. Dispos. 46, 109-121 (2018).
Van den Bergh, A. et al. Pharmacokinetic drug-drug interaction of apalutamide, part 2: investigating interaction potential using a physiologically based pharmacokinetic model. Clin. Pharmacokinet. 59, 1149-1160 (2020).
Glaxo SmithKline. Dectova (Zanimivir) Summary of Product Characteristics <https://www.ema.europa.eu/en/documents/product-information/dectova-epar-product-information_en.pdf> (2020). Accessed May 1, 2021.
Yang, J., Jamei, M., Yeo, K.R., Tucker, G.T. & Rostami-Hodjegan, A. Prediction of intestinal first-pass drug metabolism. Curr. Drug Metab. 8, 676-684 (2007).
Chen, Y. et al. Recommendations for the design of clinical drug-drug interaction studies with itraconazole using a mechanistic physiologically-based pharmacokinetic model. CPT Pharmacometr. Syst. Pharmacol. 8, 685-695 (2019).
Heiskanen, T., Backman, J.T., Neuvonen, M., Kontinen, V.K., Neuvonen, P.J. & Kalso, E. Itraconazole, a potent inhibitor of P-glycoprotein, moderately increases plasma concentrations of oral morphine. Acta Anaesthesiol. Scand. 52, 1319-1326 (2008).
Varma, M.V. & El-Kattan, A.F. Transporter-enzyme interplay: deconvoluting effects of hepatic transporters and enzymes on drug disposition using static and dynamic mechanistic models. J. Clin. Pharmacol. 56(Suppl 7), S99-S109 (2016).
Einolf, H.J. et al. Physiologically Based Pharmacokinetic Model Predictions of Panobinostat (LBH589) as a victim and perpetrator of drug-drug interactions. Drug Metab. Dispos. 45, 1304-1316 (2017).
Einolf, H.J. et al. Evaluation of various static and dynamic modeling methods to predict clinical CYP3A induction using in vitro CYP3A4 mRNA induction data. Clin. Pharmacol. Ther. 95, 179-188 (2014).
Otsuka, Y., Choules, M.P., Bonate, P.L. & Komatsu, K. Physiologically-based pharmacokinetic modeling for the prediction of a drug-drug interaction of combined effects on p-glycoprotein and cytochrome P450 3A. CPT Pharmacometr. Syst. Pharmacol. 9, 659-669 (2020).
US Food and Drug Administration (FDA) <https://www.fda.gov/drugs/drug-safety-and-availability/laboratory-analysis-rifampinrifapentine-products> (2020).
Rodrigues, A.D. et al. Exploring the use of serum-derived small extracellular vesicles as liquid biopsy to study the induction of hepatic cytochromes P450 and organic anion transporting polypeptides. Clin. Pharmacol. Ther. 110, 248-258 (2021).
US Food and Drug Administration (FDA). Clinical drug interaction studies with combined oral contraceptives - guidance for industry. 2020.
Ezuruike, U., Humphries, H., Dickins, M., Neuhoff, S., Gardner, I. & Rowland, Y.K. Risk-benefit assessment of ethinylestradiol using a physiologically based pharmacokinetic modeling approach. Clin. Pharmacol. Ther. 104, 1229-1239 (2018).
Kanacher, T. et al. A Physiologically-Based Pharmacokinetic (PBPK) model network for the prediction of CYP1A2 and CYP2C19 drug-drug-gene interactions with fluvoxamine, and caffeine. Pharmaceutics 12, 1191 (2020).
Leil, T.A., Kasichayanula, S., Boulton, D.W. & LaCreta, F. Evaluation of 4beta-hydroxycholesterol as a clinical biomarker of CYP3A4 drug interactions using a Bayesian mechanism-based pharmacometric model. CPT Pharmacometr. Syst. Pharmacol. 3, e120 (2014).
Mangold, J.B., Wu, F. & Rebello, S. Compelling relationship of CYP3A induction to levels of the putative biomarker 4beta-hydroxycholesterol and changes in midazolam exposure. Clin. Pharmacol. Drug Dev. 5, 245-249 (2016).