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Tsiros P, Minadakis V, Li D, Sarimveis H. Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms. Toxicol Sci 2024; 200:31-46. [PMID: 38637946 PMCID: PMC11199918 DOI: 10.1093/toxsci/kfae051] [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] [Indexed: 04/20/2024] Open
Abstract
Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.
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Affiliation(s)
- Periklis Tsiros
- School of Chemical Engineering, National Technical University of Athens, Attiki 15772, Greece
| | - Vasileios Minadakis
- School of Chemical Engineering, National Technical University of Athens, Attiki 15772, Greece
| | - Dingsheng Li
- School of Public Health, University of Nevada, Reno, Nevada 89557-0274, USA
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Attiki 15772, Greece
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2
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Arora S, Clarke J, Tsakalozou E, Ghosh P, Alam K, Grice JE, Roberts MS, Jamei M, Polak S. Mechanistic Modeling of In Vitro Skin Permeation and Extrapolation to In Vivo for Topically Applied Metronidazole Drug Products Using a Physiologically Based Pharmacokinetic Model. Mol Pharm 2022; 19:3139-3152. [PMID: 35969125 DOI: 10.1021/acs.molpharmaceut.2c00229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling has increasingly been employed in dermal drug development and regulatory assessment, providing a framework to integrate relevant information including drug and drug product attributes, skin physiology parameters, and population variability. The current study aimed to develop a stepwise modeling workflow with knowledge gained from modeling in vitro skin permeation testing (IVPT) to describe in vivo exposure of metronidazole locally in the stratum corneum following topical application of complex semisolid drug products. The initial PBPK model of metronidazole in vitro skin permeation was developed using infinite and finite dose aqueous metronidazole solution. Parameters such as stratum corneum lipid-water partition coefficient (Ksclip/water) and stratum corneum lipid diffusion coefficient (Dsclip) of metronidazole were optimized using IVPT data from simple aqueous solutions (infinite) and MetroGel (10 mg/cm2 dose application), respectively. The optimized model, when parameterized with physical and structural characteristics of the drug products, was able to accurately predict the mean cumulative amount permeated (cm2/h) and flux (μg/cm2/h) profiles of metronidazole following application of different doses of MetroGel and MetroCream. Thus, the model was able to capture the impact of differences in drug product microstructure and metamorphosis of the dosage form on in vitro metronidazole permeation. The PBPK model informed by IVPT study data was able to predict the metronidazole amount in the stratum corneum as reported in clinical studies. In summary, the proposed model provides an enhanced understanding of the potential impact of drug product attributes in influencing in vitro skin permeation of metronidazole. Key kinetic parameters derived from modeling the metronidazole IVPT data improved the predictions of the developed PBPK model of in vivo local metronidazole concentrations in the stratum corneum. Overall, this work improves our confidence in the proposed workflow that accounts for drug product attributes and utilizes IVPT data toward improving predictions from advanced modeling and simulation tools.
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Affiliation(s)
- Sumit Arora
- Certara UK Ltd, Simcyp Division, Sheffield S1 2BJ, U.K
| | - James Clarke
- Certara UK Ltd, Simcyp Division, Sheffield S1 2BJ, U.K
| | - Eleftheria Tsakalozou
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Priyanka Ghosh
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Khondoker Alam
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Jeffery E Grice
- Therapeutics Research Centre, Diamantina Institute, University of Queensland, Brisbane 4102, Australia
| | - Michael S Roberts
- Therapeutics Research Centre, Diamantina Institute, University of Queensland, Brisbane 4102, Australia.,Clinical and Health Sciences, University of South Australia, Adelaide 5005, Australia
| | - Masoud Jamei
- Certara UK Ltd, Simcyp Division, Sheffield S1 2BJ, U.K
| | - Sebastian Polak
- Certara UK Ltd, Simcyp Division, Sheffield S1 2BJ, U.K.,Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
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Dalaijamts C, Cichocki JA, Luo YS, Rusyn I, Chiu WA. Quantitative Characterization of Population-Wide Tissue- and Metabolite-Specific Variability in Perchloroethylene Toxicokinetics in Male Mice. Toxicol Sci 2021; 182:168-182. [PMID: 33988684 DOI: 10.1093/toxsci/kfab057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.
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Affiliation(s)
- Chimeddulam Dalaijamts
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843-4458, USA.,Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843-4458, USA
| | - Joseph A Cichocki
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843-4458, USA.,Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843-4458, USA
| | - Yu-Syuan Luo
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843-4458, USA.,Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843-4458, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843-4458, USA.,Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843-4458, USA
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843-4458, USA.,Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843-4458, USA
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4
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Tsakalozou E, Alam K, Babiskin A, Zhao L. Physiologically-Based Pharmacokinetic Modeling to Support Determination of Bioequivalence for Dermatological Drug Products: Scientific and Regulatory Considerations. Clin Pharmacol Ther 2021; 111:1036-1049. [PMID: 34231211 DOI: 10.1002/cpt.2356] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/11/2021] [Indexed: 12/30/2022]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling and simulation provides mechanism-based predictions of the pharmacokinetics of an active ingredient following its administration in humans. Dermal PBPK models describe the skin permeation and disposition of the active ingredient following the application of a dermatological product on the skin of virtual healthy and diseased human subjects. These models take into account information on product quality attributes, physicochemical properties of the active ingredient and skin (patho)physiology, and their interplay with each other. Regulatory and product development decision makers can leverage these quantitative tools to identify factors impacting local and systemic exposure. In the realm of generic drug products, the number of US Food and Drug Administratioin (FDA) interactions that use dermal PBPK modeling to support alternative bioequivalence (BE) approaches is increasing. In this report, we share scientific considerations on the development, verification and validation (V&V), and application of PBPK models within the context of a virtual BE assessment for dermatological drug products. We discuss the challenges associated with model V&V for these drug products stemming from the fact that target-site active ingredient concentrations are typically not measurable. Additionally, there are no established relationships between local and systemic PK profiles, when the latter are quantifiable. To that end, we detail a multilevel model V&V approach involving validation for the model of the drug product of interest coupled with the overall assessment of the modeling platform in use while leveraging in vitro and in vivo data related to local and systemic bioavailability.
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Affiliation(s)
- Eleftheria Tsakalozou
- Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Khondoker Alam
- Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Andrew Babiskin
- Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Liang Zhao
- Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
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5
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Hsieh NH, Reisfeld B, Chiu WA. pksensi: An R package to apply global sensitivity analysis in physiologically based kinetic modeling. SOFTWAREX 2020; 12:100609. [PMID: 33426260 PMCID: PMC7790364 DOI: 10.1016/j.softx.2020.100609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Sensitivity analysis (SA) is an essential tool for modelers to understand the influence of model parameters on model outputs. It is also increasingly used in developing and assessing physiologically based kinetic (PBK) models. For instance, several studies have applied global SA to reduce the computational burden in the Bayesian Markov chain Monte Carlo-based calibration process PBK models. Although several SA algorithms and software packages are available, no comprehensive software package exists that allows users to seamlessly solve differential equations in a PBK model, conduct and visualize SA results, and discriminate between the non-influential model parameters that can be fixed and those that need calibration. Therefore, we developed an R package, named pksensi, to make global SA more accessible in PBK modeling. This package can investigate both uncertainty and sensitivity in PBK models, including those with multivariate model outputs. It also includes functions to check the convergence of the global SA results. Overall, pksensi improves the user experience of performing global SA and can create robust and reproducible results for decision making in PBK model calibration.
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Affiliation(s)
- Nan-Hung Hsieh
- Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Brad Reisfeld
- Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort Collins, CO, USA
| | - Weihsueh A. Chiu
- Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
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Tan YM, Chan M, Chukwudebe A, Domoradzki J, Fisher J, Hack CE, Hinderliter P, Hirasawa K, Leonard J, Lumen A, Paini A, Qian H, Ruiz P, Wambaugh J, Zhang F, Embry M. PBPK model reporting template for chemical risk assessment applications. Regul Toxicol Pharmacol 2020; 115:104691. [PMID: 32502513 PMCID: PMC8188465 DOI: 10.1016/j.yrtph.2020.104691] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/18/2020] [Accepted: 05/28/2020] [Indexed: 12/04/2022]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals. This harmonized reporting template can be adopted and customized by public health agencies receiving PBPK model submission, and it can also serve as general guidance for submitting PBPK-related studies for publication in journals or other modeling sharing purposes. The current effort represents one of several ongoing collaborations among the PBPK modeling and risk assessment communities to promote, when appropriate, incorporating PBPK modeling to characterize the influence of pharmacokinetics on safety decisions made by regulatory agencies.
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Affiliation(s)
- Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Health Effects Division, 109 TW Alexander Dr, Research Triangle Park, NC, 27709, USA.
| | - Melissa Chan
- Corteva Agriscience, Haskell R&D Center, 1090 Elkton Road, Newark, DE, 19714, USA.
| | - Amechi Chukwudebe
- BASF Corporation, 26 Davis Drive, Research Triangle Park, NC, 27709, USA.
| | - Jeanne Domoradzki
- Corteva Agriscience, Haskell R&D Center, 1090 Elkton Road, Newark, DE, 19714, USA
| | - Jeffrey Fisher
- National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA.
| | - C Eric Hack
- ScitoVation, 100 Capitola Drive, Durham, NC, 27713, USA.
| | - Paul Hinderliter
- Syngenta Crop Protection, LLC, 410 Swing Rd, Greensboro, NC, 27409, USA.
| | - Kota Hirasawa
- Sumitomo Chemical Co, Ltd, 1-98, Kasugadenaka 3-chome, Konohana-ku, Osaka, 554-8558, Japan.
| | - Jeremy Leonard
- Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN, 37830, USA.
| | - Annie Lumen
- National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA.
| | - Alicia Paini
- European Commission Joint Research Centre, Via E. Fermi 2749, Ispra I, 21027, Italy.
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc, 1545 US Hwy 22 East, Annandale, NJ, 08801, USA.
| | - Patricia Ruiz
- CDC-ATSDR, 4770 Buford Hwy, Mailstop S102-1, Chamblee, GA, 3034, USA.
| | - John Wambaugh
- US Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr, Research Triangle Park, NC, 27711, USA.
| | - Fagen Zhang
- The Dow Chemical Company, 1803 Building, Midland, MI, 48674, USA.
| | - Michelle Embry
- Health and Environmental Sciences Institute, 740 15th Street, NW, Suite 600, Washington, DC, 20005, USA.
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Wambaugh JF, Wetmore BA, Ring CL, Nicolas CI, Pearce R, Honda G, Dinallo R, Angus D, Gilbert J, Sierra T, Badrinarayanan A, Snodgrass B, Brockman A, Strock C, Setzer W, Thomas RS. Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization. Toxicol Sci 2019; 172:235-251. [PMID: 31532498 PMCID: PMC8136471 DOI: 10.1093/toxsci/kfz205] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.
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Affiliation(s)
- John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
| | - Barbara A. Wetmore
- National Exposure Research Laboratory, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
| | - Caroline L. Ring
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Chantel I. Nicolas
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
- Office of Pollution Prevention and Toxics, U.S. EPA, Washington, D.C. 20460
| | - Robert Pearce
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Gregory Honda
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | | | | | | | | | | | | | | | | | - Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
| | - Russell S. Thomas
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
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Polak S, Tylutki Z, Holbrook M, Wiśniowska B. Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development. Drug Discov Today 2019; 24:1344-1354. [PMID: 31132414 DOI: 10.1016/j.drudis.2019.05.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 03/04/2019] [Accepted: 05/21/2019] [Indexed: 12/15/2022]
Abstract
Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.
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Affiliation(s)
- Sebastian Polak
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland; Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK.
| | - Zofia Tylutki
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland; Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Mark Holbrook
- Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Barbara Wiśniowska
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland
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Tsiros P, Bois FY, Dokoumetzidis A, Tsiliki G, Sarimveis H. Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim. J Pharmacokinet Pharmacodyn 2019; 46:173-192. [PMID: 30949914 DOI: 10.1007/s10928-019-09630-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 03/25/2019] [Indexed: 11/29/2022]
Abstract
The aim of this study is to benchmark two Bayesian software tools, namely Stan and GNU MCSim, that use different Markov chain Monte Carlo (MCMC) methods for the estimation of physiologically based pharmacokinetic (PBPK) model parameters. The software tools were applied and compared on the problem of updating the parameters of a Diazepam PBPK model, using time-concentration human data. Both tools produced very good fits at the individual and population levels, despite the fact that GNU MCSim is not able to consider multivariate distributions. Stan outperformed GNU MCSim in sampling efficiency, due to its almost uncorrelated sampling. However, GNU MCSim exhibited much faster convergence and performed better in terms of effective samples produced per unit of time.
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Affiliation(s)
- Periklis Tsiros
- School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780, Athens, Greece
| | - Frederic Y Bois
- Unit Modles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Parc ALATA BP2, 60550, Verneuil en Halatte, France
| | - Aristides Dokoumetzidis
- Department of Pharmacy, University of Athens, Panepistimiopolis Zografou, 15784, Athens, Greece
| | - Georgia Tsiliki
- ATHENA Research and Innovation Centre, Artemidos 6 & Epidavrou, Marousi, Athens, 15125, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780, Athens, Greece.
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10
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Wambaugh JF. Comment on: Dong et al. (2017) "Issues raised by the reference doses for perfluorooctonate sulfonate and perfluorooctanoic acid.". ENVIRONMENT INTERNATIONAL 2018; 121:1372-1374. [PMID: 30420130 DOI: 10.1016/j.envint.2018.10.048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 10/23/2018] [Indexed: 05/20/2023]
Affiliation(s)
- John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr., NC 27711, USA.
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11
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Hsieh NH, Reisfeld B, Bois FY, Chiu WA. Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling. Front Pharmacol 2018; 9:588. [PMID: 29937730 PMCID: PMC6002508 DOI: 10.3389/fphar.2018.00588] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/16/2018] [Indexed: 11/13/2022] Open
Abstract
Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.
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Affiliation(s)
- Nan-Hung Hsieh
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Brad Reisfeld
- Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
| | | | - Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
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12
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Wambaugh JF, Hughes MF, Ring CL, MacMillan DK, Ford J, Fennell TR, Black SR, Snyder RW, Sipes NS, Wetmore BA, Westerhout J, Setzer RW, Pearce RG, Simmons JE, Thomas RS. Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics. Toxicol Sci 2018; 163:152-169. [PMID: 29385628 PMCID: PMC5920326 DOI: 10.1093/toxsci/kfy020] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks.
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Affiliation(s)
| | - Michael F Hughes
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Caroline L Ring
- National Center for Computational Toxicology
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Denise K MacMillan
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Jermaine Ford
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | | | - Sherry R Black
- RTI International, Research Triangle Park, North Carolina
| | | | - Nisha S Sipes
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27717
| | - Barbara A Wetmore
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Joost Westerhout
- The Netherlands Organisation for Applied Scientific Research (TNO), AJ Zeist 3700, The Netherlands
| | | | - Robert G Pearce
- National Center for Computational Toxicology
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Jane Ellen Simmons
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
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13
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Shebley M, Sandhu P, Emami Riedmaier A, Jamei M, Narayanan R, Patel A, Peters SA, Reddy VP, Zheng M, de Zwart L, Beneton M, Bouzom F, Chen J, Chen Y, Cleary Y, Collins C, Dickinson GL, Djebli N, Einolf HJ, Gardner I, Huth F, Kazmi F, Khalil F, Lin J, Odinecs A, Patel C, Rong H, Schuck E, Sharma P, Wu SP, Xu Y, Yamazaki S, Yoshida K, Rowland M. Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective. Clin Pharmacol Ther 2018; 104:88-110. [PMID: 29315504 PMCID: PMC6032820 DOI: 10.1002/cpt.1013] [Citation(s) in RCA: 223] [Impact Index Per Article: 37.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/05/2017] [Accepted: 01/03/2018] [Indexed: 12/15/2022]
Abstract
This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Ming Zheng
- Bristol-Myers Squibb, Princeton, NJ, USA
| | | | | | | | - Jun Chen
- Sanofi, Région de Montpellier, France
| | | | | | | | | | | | | | | | | | | | | | - Jing Lin
- Sunovion Pharmaceuticals Inc., Marlborough, MA, USA
| | | | - Chirag Patel
- Takeda Pharmaceuticals International Co., Cambridge, MA, USA
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14
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Pearce RG, Setzer RW, Davis JL, Wambaugh JF. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 2017; 44:549-565. [PMID: 29032447 DOI: 10.1007/s10928-017-9548-7.evaluation] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/30/2017] [Indexed: 05/27/2023]
Abstract
Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.
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Affiliation(s)
- Robert G Pearce
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
| | - Jimena L Davis
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Syngenta, Research Triangle Park, NC, 27709, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA.
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15
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Pearce RG, Setzer RW, Davis JL, Wambaugh JF. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 2017; 44:549-565. [PMID: 29032447 PMCID: PMC6186149 DOI: 10.1007/s10928-017-9548-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/30/2017] [Indexed: 12/25/2022]
Abstract
Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.
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Affiliation(s)
- Robert G Pearce
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
| | - Jimena L Davis
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Syngenta, Research Triangle Park, NC, 27709, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA.
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16
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Lavielle M, Aarons L. What do we mean by identifiability in mixed effects models? J Pharmacokinet Pharmacodyn 2015; 43:111-22. [PMID: 26660913 DOI: 10.1007/s10928-015-9459-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Accepted: 11/26/2015] [Indexed: 12/28/2022]
Abstract
We discuss the question of model identifiability within the context of nonlinear mixed effects models. Although there has been extensive research in the area of fixed effects models, much less attention has been paid to random effects models. In this context we distinguish between theoretical identifiability, in which different parameter values lead to non-identical probability distributions, structural identifiability which concerns the algebraic properties of the structural model, and practical identifiability, whereby the model may be theoretically identifiable but the design of the experiment may make parameter estimation difficult and imprecise. We explore a number of pharmacokinetic models which are known to be non-identifiable at an individual level but can become identifiable at the population level if a number of specific assumptions on the probabilistic model hold. Essentially if the probabilistic models are different, even though the structural models are non-identifiable, then they will lead to different likelihoods. The findings are supported through simulations.
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17
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Wendling T, Tsamandouras N, Dumitras S, Pigeolet E, Ogungbenro K, Aarons L. Reduction of a Whole-Body Physiologically Based Pharmacokinetic Model to Stabilise the Bayesian Analysis of Clinical Data. AAPS JOURNAL 2015; 18:196-209. [PMID: 26538125 DOI: 10.1208/s12248-015-9840-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 10/15/2015] [Indexed: 12/27/2022]
Abstract
Whole-body physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development for their ability to predict drug concentrations in clinically relevant tissues and to extrapolate across species, experimental conditions and sub-populations. A whole-body PBPK model can be fitted to clinical data using a Bayesian population approach. However, the analysis might be time consuming and numerically unstable if prior information on the model parameters is too vague given the complexity of the system. We suggest an approach where (i) a whole-body PBPK model is formally reduced using a Bayesian proper lumping method to retain the mechanistic interpretation of the system and account for parameter uncertainty, (ii) the simplified model is fitted to clinical data using Markov Chain Monte Carlo techniques and (iii) the optimised reduced PBPK model is used for extrapolation. A previously developed 16-compartment whole-body PBPK model for mavoglurant was reduced to 7 compartments while preserving plasma concentration-time profiles (median and variance) and giving emphasis to the brain (target site) and the liver (elimination site). The reduced model was numerically more stable than the whole-body model for the Bayesian analysis of mavoglurant pharmacokinetic data in healthy adult volunteers. Finally, the reduced yet mechanistic model could easily be scaled from adults to children and predict mavoglurant pharmacokinetics in children aged from 3 to 11 years with similar performance compared with the whole-body model. This study is a first example of the practicality of formal reduction of complex mechanistic models for Bayesian inference in drug development.
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Affiliation(s)
- Thierry Wendling
- Manchester Pharmacy School, The University of Manchester, Manchester, UK. .,Drug Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research, Basel, Switzerland.
| | | | - Swati Dumitras
- Drug Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Kayode Ogungbenro
- Manchester Pharmacy School, The University of Manchester, Manchester, UK
| | - Leon Aarons
- Manchester Pharmacy School, The University of Manchester, Manchester, UK
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