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Rowland Yeo K, Gil Berglund E, Chen Y. Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future. Clin Pharmacol Ther 2024; 116:563-576. [PMID: 38686708 DOI: 10.1002/cpt.3289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024]
Abstract
Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.
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Affiliation(s)
| | - Eva Gil Berglund
- Certara Clinical Drug Development Solutions, Oss, The Netherlands
| | - Yuan Chen
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA
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2
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The next frontier in ADME science: Predicting transporter-based drug disposition, tissue concentrations and drug-drug interactions in humans. Pharmacol Ther 2022; 238:108271. [DOI: 10.1016/j.pharmthera.2022.108271] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 12/25/2022]
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Kimoto E, Costales C, West MA, Bi YA, Vourvahis M, David Rodrigues A, Varma MVS. Biomarker-Informed Model-Based Risk Assessment of Organic Anion Transporting Polypeptide 1B Mediated Drug-Drug Interactions. Clin Pharmacol Ther 2021; 111:404-415. [PMID: 34605015 DOI: 10.1002/cpt.2434] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/15/2021] [Indexed: 11/08/2022]
Abstract
Quantitative prediction of drug-drug interactions (DDIs) involving organic anion transporting polypeptide (OATP)1B1/1B3 inhibition is limited by uncertainty in the translatability of experimentally determined in vitro inhibition potency (half-maximal inhibitory concentration (IC50 )). This study used an OATP1B endogenous biomarker-informed physiologically-based pharmacokinetic (PBPK) modeling approach to predict the effect of inhibitor drugs on the pharmacokinetics (PKs) of OATP1B substrates. Initial static analysis with about 42 inhibitor drugs, using in vitro IC50 values and unbound liver inlet concentrations (Iin,max,u ), suggested in vivo OATP1B inhibition risk for drugs with R-value (1+ Iin,max,u /IC50 ) above 1.5. A full-PBPK model accounting for transporter-mediated hepatic disposition was developed for coproporphyrin I (CP-I), an endogenous OATP1B biomarker. For several inhibitors (cyclosporine, diltiazem, fenebrutinib, GDC-0810, itraconazole, probenecid, and rifampicin at 3 different doses), PBPK models were developed and verified against available CP-I plasma exposure data to obtain in vivo OATP1B inhibition potency-which tend to be lower than the experimentally measured in vitro IC50 by about 2-fold (probenecid and rifampicin) to 37-fold (GDC-0810). Models verified with CP-I data are subsequently used to predict DDIs with OATP1B probe drugs, rosuvastatin and pitavastatin. The predicted and observed area under the plasma concentration-time curve ratios are within 20% error in 55% cases, and within 30% error in 89% cases. Collectively, this comprehensive study illustrates the adequacy and utility of endogenous biomarker-informed PBPK modeling in mechanistic understanding and quantitative predictions of OATP1B-mediated DDIs in drug development.
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Affiliation(s)
- Emi Kimoto
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
| | - Chester Costales
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
| | - Mark A West
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
| | - Yi-An Bi
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
| | - Manoli Vourvahis
- Clinical Pharmacology, Global Product Development, Pfizer Inc, New York, New York, USA
| | - A David Rodrigues
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
| | - Manthena V S Varma
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
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Abbiati RA, Wientjes MG, Au JLS. Is It Time to Use Modeling of Cellular Transporter Homeostasis to Inform Drug-Drug Interaction Studies: Theoretical Considerations. AAPS J 2021; 23:102. [PMID: 34435271 PMCID: PMC11048728 DOI: 10.1208/s12248-021-00635-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 08/06/2021] [Indexed: 11/30/2022] Open
Abstract
Mathematical modeling has been an important tool in pharmaceutical research for 50 + years and there is increased emphasis over the last decade on using modeling to improve the efficiency and effectiveness of drug development. In an earlier commentary, we applied a multiscale model linking 6 scales (whole body, tumor, vasculature, cell, spatial location, time), together with literature data on nanoparticle and tumor properties, to demonstrate the effects of nanoparticle particles on systemic disposition. The current commentary used a 4-scale model (cell membrane, intracellular organelles, spatial location, time) together with literature data on the intracellular processing of membrane receptors and transporters to demonstrate disruption of transporter homeostasis can lead to drug-drug interaction (DDI) between victim drug (VD) and perpetrator drug (PD), including changes in the area-under-concentration-time-curve of VD in cells that are considered significant by the US Food and Drug Administration (FDA). The model comprised 3 computational components: (a) intracellular transporter homeostasis, (b) pharmacokinetics of extracellular and intracellular VD/PD concentrations, and (c) pharmacodynamics of PD-induced stimulation or inhibition of an intracellular kinetic process. Model-based simulations showed that (a) among the five major endocytic processes, perturbation of transporter internalization or recycling led to the highest incidence and most extensive DDI, with minor DDI for perturbing transporter synthesis and early-to-late endosome and no DDI for perturbing transporter degradation and (b) three experimental conditions (spatial transporter distribution in cells, VD/PD co-incubation time, extracellular PD concentrations) were determinants of DDI detection. We propose modeling is a useful tool for hypothesis generation and for designing experiments to identify potential DDI; its application further aligns with the model-informed drug development paradigm advocated by FDA.
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Affiliation(s)
- Roberto A Abbiati
- Institute of Quantitative Systems Pharmacology, Carlsbad, California, 92008, USA
- Department of Pharmaceutical Sciences, University of Oklahoma, Oklahoma City, Oklahoma, 73117, USA
| | - M Guillaume Wientjes
- Institute of Quantitative Systems Pharmacology, Carlsbad, California, 92008, USA
- Optimum Therapeutics LLC, 1815 Aston Ave, Suite 107, Carlsbad, California, 92008, USA
| | - Jessie L-S Au
- Institute of Quantitative Systems Pharmacology, Carlsbad, California, 92008, USA.
- Department of Pharmaceutical Sciences, University of Oklahoma, Oklahoma City, Oklahoma, 73117, USA.
- Optimum Therapeutics LLC, 1815 Aston Ave, Suite 107, Carlsbad, California, 92008, USA.
- Taipei Medical University, Taipei, Taiwan, Republic of China.
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Jiang R, Hart A, Burgess L, Kim DS, Lai WG, Dixit V. Prediction of Transporter-Mediated Drug-Drug Interactions and Phenotyping of Hepatobiliary Transporters Involved in the Clearance of E7766, a Novel Macrocycle-Bridged Dinucleotide. Drug Metab Dispos 2020; 49:265-275. [DOI: 10.1124/dmd.120.000125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 12/10/2020] [Indexed: 01/08/2023] Open
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Sane R, Cheung KWK, Kovács P, Farasyn T, Li R, Bui A, Musib L, Kis E, Plise E, Gáborik Z. Calibrating the In Vitro–In Vivo Correlation for OATP-Mediated Drug-Drug Interactions with Rosuvastatin Using Static and PBPK Models. Drug Metab Dispos 2020; 48:1264-1270. [DOI: 10.1124/dmd.120.000149] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 09/28/2020] [Indexed: 12/16/2022] Open
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Lu C, Di L. In vitro
and
in vivo
methods to assess pharmacokinetic drug– drug interactions in drug discovery and development. Biopharm Drug Dispos 2020; 41:3-31. [DOI: 10.1002/bdd.2212] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 09/27/2019] [Accepted: 10/28/2019] [Indexed: 12/11/2022]
Affiliation(s)
- Chuang Lu
- Department of DMPKSanofi Company Waltham MA 02451
| | - Li Di
- Pharmacokinetics, Dynamics and MetabolismPfizer Worldwide Research & Development Groton CT 06340
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Peters SA, Dolgos H. Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them. Clin Pharmacokinet 2019; 58:1355-1371. [PMID: 31236775 PMCID: PMC6856026 DOI: 10.1007/s40262-019-00790-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.
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Affiliation(s)
| | - Hugues Dolgos
- Merck Healthcare KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany
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Cheung KWK, Yoshida K, Cheeti S, Chen B, Morley R, Chan IT, Sahasranaman S, Liu L. GDC-0810 Pharmacokinetics and Transporter-Mediated Drug Interaction Evaluation with an Endogenous Biomarker in the First-in-Human, Dose Escalation Study. Drug Metab Dispos 2019; 47:966-973. [DOI: 10.1124/dmd.119.087924] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 06/26/2019] [Indexed: 12/22/2022] Open
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