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Gonnabathula P, Choi MK, Li M, Kabadi SV, Fairman K. Utility of life stage-specific chemical risk assessments based on New Approach Methodologies (NAMs). Food Chem Toxicol 2024; 190:114789. [PMID: 38844066 DOI: 10.1016/j.fct.2024.114789] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 05/17/2024] [Accepted: 06/03/2024] [Indexed: 06/17/2024]
Abstract
The safety assessments for chemicals targeted for use or expected to be exposed to specific life stages, including infancy, childhood, pregnancy and lactation, and geriatrics, need to account for extrapolation of data from healthy adults to these populations to assess their human health risk. However, often adequate and relevant toxicity or pharmacokinetic (PK) data of chemicals in specific life stages are not available. For such chemicals, New Approach Methodologies (NAMs), such as physiologically based pharmacokinetic (PBPK) modeling, biologically based dose response (BBDR) modeling, in vitro to in vivo extrapolation (IVIVE), etc. can be used to understand the variability of exposure and effects of chemicals in specific life stages and assess their associated risk. A life stage specific PBPK model incorporates the physiological and biochemical changes associated with each life stage and simulates their impact on the absorption, distribution, metabolism, and elimination (ADME) of these chemicals. In our review, we summarize the parameterization of life stage models based on New Approach Methodologies (NAMs) and discuss case studies that highlight the utility of a life stage based PBPK modeling for risk assessment. In addition, we discuss the utility of artificial intelligence (AI)/machine learning (ML) and other computational models, such as those based on in vitro data, as tools for estimation of relevant physiological or physicochemical parameters and selection of model. We also discuss existing gaps in the available toxicological datasets and current challenges that need to be overcome to expand the utility of NAMs for life stage-specific chemical risk assessment.
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Affiliation(s)
- Pavani Gonnabathula
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - Me-Kyoung Choi
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - Miao Li
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - Shruti V Kabadi
- Center for Food Safety and Applied Nutrition (CFSAN), US Food and Drug Administration (FDA), College Park, MD, 20740, USA
| | - Kiara Fairman
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA.
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2
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Bagga AD, Johnson BP, Zhang Q. A minimal human physiologically based kinetic model of thyroid hormones and chemical disruption of plasma thyroid hormone binding proteins. Front Endocrinol (Lausanne) 2023; 14:1168663. [PMID: 37305053 PMCID: PMC10248451 DOI: 10.3389/fendo.2023.1168663] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/11/2023] [Indexed: 06/13/2023] Open
Abstract
The thyroid hormones (THs), thyroxine (T4) and triiodothyronine (T3), are under homeostatic control by the hypothalamic-pituitary-thyroid axis and plasma TH binding proteins (THBPs), including thyroxine-binding globulin (TBG), transthyretin (TTR), and albumin (ALB). THBPs buffer free THs against transient perturbations and distribute THs to tissues. TH binding to THBPs can be perturbed by structurally similar endocrine-disrupting chemicals (EDCs), yet their impact on circulating THs and health risks are unclear. In the present study, we constructed a human physiologically based kinetic (PBK) model of THs and explored the potential effects of THBP-binding EDCs. The model describes the production, distribution, and metabolism of T4 and T3 in the Body Blood, Thyroid, Liver, and Rest-of-Body (RB) compartments, with explicit consideration of the reversible binding between plasma THs and THBPs. Rigorously parameterized based on literature data, the model recapitulates key quantitative TH kinetic characteristics, including free, THBP-bound, and total T4 and T3 concentrations, TH productions, distributions, metabolisms, clearance, and half-lives. Moreover, the model produces several novel findings. (1) The blood-tissue TH exchanges are fast and nearly at equilibrium especially for T4, providing intrinsic robustness against local metabolic perturbations. (2) Tissue influx is limiting for transient tissue uptake of THs when THBPs are present. (3) Continuous exposure to THBP-binding EDCs does not alter the steady-state levels of THs, while intermittent daily exposure to rapidly metabolized TBG-binding EDCs can cause much greater disruptions to plasma and tissue THs. In summary, the PBK model provides novel insights into TH kinetics and the homeostatic roles of THBPs against thyroid disrupting chemicals.
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Affiliation(s)
- Anish D. Bagga
- Emory College of Arts and Sciences, Emory University, Atlanta, GA, United States
| | - Brian P. Johnson
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, United States
| | - Qiang Zhang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, GA, Atlanta, United States
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Bajard L, Adamovsky O, Audouze K, Baken K, Barouki R, Beltman JB, Beronius A, Bonefeld-Jørgensen EC, Cano-Sancho G, de Baat ML, Di Tillio F, Fernández MF, FitzGerald RE, Gundacker C, Hernández AF, Hilscherova K, Karakitsios S, Kuchovska E, Long M, Luijten M, Majid S, Marx-Stoelting P, Mustieles V, Negi CK, Sarigiannis D, Scholz S, Sovadinova I, Stierum R, Tanabe S, Tollefsen KE, van den Brand AD, Vogs C, Wielsøe M, Wittwehr C, Blaha L. Application of AOPs to assist regulatory assessment of chemical risks - Case studies, needs and recommendations. ENVIRONMENTAL RESEARCH 2023; 217:114650. [PMID: 36309218 PMCID: PMC9850416 DOI: 10.1016/j.envres.2022.114650] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 05/06/2023]
Abstract
While human regulatory risk assessment (RA) still largely relies on animal studies, new approach methodologies (NAMs) based on in vitro, in silico or non-mammalian alternative models are increasingly used to evaluate chemical hazards. Moreover, human epidemiological studies with biomarkers of effect (BoE) also play an invaluable role in identifying health effects associated with chemical exposures. To move towards the next generation risk assessment (NGRA), it is therefore crucial to establish bridges between NAMs and standard approaches, and to establish processes for increasing mechanistically-based biological plausibility in human studies. The Adverse Outcome Pathway (AOP) framework constitutes an important tool to address these needs but, despite a significant increase in knowledge and awareness, the use of AOPs in chemical RA remains limited. The objective of this paper is to address issues related to using AOPs in a regulatory context from various perspectives as it was discussed in a workshop organized within the European Union partnerships HBM4EU and PARC in spring 2022. The paper presents examples where the AOP framework has been proven useful for the human RA process, particularly in hazard prioritization and characterization, in integrated approaches to testing and assessment (IATA), and in the identification and validation of BoE in epidemiological studies. Nevertheless, several limitations were identified that hinder the optimal usability and acceptance of AOPs by the regulatory community including the lack of quantitative information on response-response relationships and of efficient ways to map chemical data (exposure and toxicity) onto AOPs. The paper summarizes suggestions, ongoing initiatives and third-party tools that may help to overcome these obstacles and thus assure better implementation of AOPs in the NGRA.
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Affiliation(s)
- Lola Bajard
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Ondrej Adamovsky
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Karine Audouze
- Université Paris Cité, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Kirsten Baken
- Unit Health, Flemish Institute for Technological Research (VITO NV), Boeretang 200, 2400 Mol, Belgium
| | - Robert Barouki
- Université Paris Cité, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Joost B Beltman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - Anna Beronius
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Solna, Sweden
| | - Eva Cecilie Bonefeld-Jørgensen
- Centre for Arctic Health & Molecular Epidemiology, Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark; Greenland Centre for Health Research, University of Greenland, Manutooq 1, 3905 Nuussuaq, Greenland
| | | | - Milo L de Baat
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Filippo Di Tillio
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - Mariana F Fernández
- Center for Biomedical Research (CIBM) & School of Medicine, University of Granada, 18016 Granada, Spain; Instituto de Investigación Biosanitaria (ibs. GRANADA), 18012, Granada, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Rex E FitzGerald
- Swiss Centre for Applied Human Toxicology SCAHT, University of Basel, Missionsstrasse 64, CH-4055 Basel, Switzerland
| | - Claudia Gundacker
- Institute of Medical Genetics, Center for Pathobiochemistry and Genetics, Medical University of Vienna, 1090 Vienna, Austria
| | - Antonio F Hernández
- Instituto de Investigación Biosanitaria (ibs. GRANADA), 18012, Granada, Spain; Department of Legal Medicine and Toxicology, University of Granada School of Medicine, Avda. de la Investigación, 11, 18016, Granada, Spain; Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, Madrid, Spain
| | - Klara Hilscherova
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece; HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Thessaloniki, Greece
| | - Eliska Kuchovska
- IUF-Leibniz Research Institute for Environmental Medicine, Auf'm Hennekamp 50, 40225, Duesseldorf, Germany
| | - Manhai Long
- Centre for Arctic Health & Molecular Epidemiology, Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark
| | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Centre for Health Protection, Bilthoven, the Netherlands
| | - Sanah Majid
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Philip Marx-Stoelting
- German Federal Institute for Risk Assessment, Dept. Pesticides Safety, Berlin, Germany
| | - Vicente Mustieles
- Center for Biomedical Research (CIBM) & School of Medicine, University of Granada, 18016 Granada, Spain; Instituto de Investigación Biosanitaria (ibs. GRANADA), 18012, Granada, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Chander K Negi
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Dimosthenis Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece; HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Thessaloniki, Greece
| | - Stefan Scholz
- UFZ Helmholtz Center for Environmental Research, Dept Bioanalyt Ecotoxicol, D-04318 Leipzig, Germany
| | - Iva Sovadinova
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Rob Stierum
- Netherlands Organisation for Applied Scientific Research, Risk Analysis for Products in Development, Utrecht, the Netherlands
| | - Shihori Tanabe
- Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki, Japan
| | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA), Section of Ecotoxicology and Risk Assessment, Gaustadalléen, Oslo, Norway; Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Norway
| | - Annick D van den Brand
- Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, 3720 BA Bilthoven, the Netherlands
| | - Carolina Vogs
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Solna, Sweden; Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, SE-75007 Uppsala, Sweden
| | - Maria Wielsøe
- Centre for Arctic Health & Molecular Epidemiology, Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark
| | | | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic.
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McNally K, Sams C, Loizou G. Development, testing, parameterisation, and calibration of a human PBK model for the plasticiser, di (2-ethylhexyl) adipate (DEHA) using in silico, in vitro and human biomonitoring data. Front Pharmacol 2023; 14:1165770. [PMID: 37033641 PMCID: PMC10076754 DOI: 10.3389/fphar.2023.1165770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/15/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction: A physiologically based biokinetic model for di (2-ethylhexyl) adipate (DEHA) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHA following a single oral dosage of 50 mg to two male and two female volunteers. Methods: The model was parameterized using in vitro and in silico methods such as, measured intrinsic hepatic clearance scaled from in vitro to in vivo and algorithmically predicted parameters such as plasma unbound fraction and tissue:blood partition coefficients (PCs). Calibration of the DEHA model was achieved using concentrations of specific downstream metabolites of DEHA excreted in urine. The total fractions of ingested DEHA eliminated as specific metabolites were estimated and were sufficient for interpreting the human biomonitoring data. Results: The specific metabolites of DEHA, mono-2-ethyl-5-hydroxyhexyl adipate (5OH-MEHA), mono-2-ethyl-5-oxohexyl adipate (5oxo-MEHA), mono-5-carboxy-2-ethylpentyl adipate (5cx-MEPA) only accounted for ∼0.45% of the ingested DEHA. Importantly, the measurements of adipic acid, a non-specific metabolite of DEHA, proved to be important in model calibration. Discussion: The very prominent trends in the urinary excretion of the metabolites, 5cx-MEPA and 5OH-MEHA allowed the important absorption mechanisms of DEHA to be modelled. The model should be useful for the study of exposure to DEHA of the general human population.
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5
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Dela A, Shtylla B, Pillis L. Multi-method Global Sensitivity Analysis of Mathematical Models. J Theor Biol 2022; 546:111159. [DOI: 10.1016/j.jtbi.2022.111159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/10/2022] [Accepted: 05/03/2022] [Indexed: 10/18/2022]
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Le A, Wearing HJ, Li D. Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs. CPT Pharmacometrics Syst Pharmacol 2022; 11:409-424. [PMID: 35045205 PMCID: PMC9007599 DOI: 10.1002/psp4.12762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 11/19/2021] [Accepted: 01/11/2022] [Indexed: 12/13/2022] Open
Abstract
Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.
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Affiliation(s)
- Anh‐Dung Le
- Nanoscience & Microsystems Engineering University of New Mexico Albuquerque New Mexico USA
| | - Helen J. Wearing
- Department of Biology Department of Mathematics & Statistics University of New Mexico Albuquerque New Mexico USA
| | - Dingsheng Li
- School of Community Health Sciences University of Nevada Reno Nevada USA
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Fairman K, Li M, Kabadi SV, Lumen A. Physiologically based pharmacokinetic modeling: A promising tool for translational research and regulatory toxicology. CURRENT OPINION IN TOXICOLOGY 2020. [DOI: 10.1016/j.cotox.2020.03.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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8
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Kazma JM, van den Anker J, Allegaert K, Dallmann A, Ahmadzia HK. Anatomical and physiological alterations of pregnancy. J Pharmacokinet Pharmacodyn 2020; 47:271-285. [PMID: 32026239 PMCID: PMC7416543 DOI: 10.1007/s10928-020-09677-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 01/28/2020] [Indexed: 02/07/2023]
Abstract
The extensive metabolic demands of pregnancy require specific physiological and anatomical changes. These changes affect almost all organ systems, including the cardiovascular, respiratory, renal, gastrointestinal, and hematologic system. The placenta adds another layer of complexity. These changes make it challenging for clinicians to understand presenting signs and symptoms, or to interpret laboratory and radiological tests. Furthermore, these physiological alterations can affect the pharmacokinetics and pharmacodynamics of drugs. Drug safety in lactation is only supported by limited evidence. In addition, the teratogenic effects of medications are often extrapolated from animals, which further adds uncertainties. Unfortunately, pregnant women are only rarely included in clinical drug trials, while doses, regimens, and side effects are often extrapolated from studies conducted in non-pregnant populations. In this comprehensive review, we present the changes occurring in each system with its effects on the pharmacokinetic variables. Understanding these physiological changes throughout normal pregnancy helps clinicians to optimize the health of pregnant women and their fetuses. Furthermore, the information on pregnancy-related physiology is also critical to guide study design in this vulnerable 'orphan' population, and provides a framework to explore pregnancy-related pathophysiology such as pre-eclampsia.
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Affiliation(s)
- Jamil M Kazma
- Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Karel Allegaert
- Department of Development and Regeneration, and Department of Pharmaceutical and Pharmacological Sciences, Leuven, Belgium
- Department of Clinical Pharmacy, Erasmus MC, Rotterdam, The Netherlands
| | - André Dallmann
- Clinical Pharmacometrics, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Homa K Ahmadzia
- Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
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Liu D, Li L, Rostami-Hodjegan A, Bois FY, Jamei M. Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models. AAPS JOURNAL 2020; 22:93. [PMID: 32681207 PMCID: PMC7367914 DOI: 10.1208/s12248-020-00480-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 07/07/2020] [Indexed: 02/06/2023]
Abstract
Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (Cmax) time at which Cmax is reached (Tmax), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (Vss) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (Vliver) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models. Graphical abstract ![]()
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Affiliation(s)
- Dan Liu
- Simcyp Division, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK.
| | - Linzhong Li
- Simcyp Division, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Amin Rostami-Hodjegan
- Simcyp Division, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Frederic Y Bois
- Simcyp Division, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Masoud Jamei
- Simcyp Division, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
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An evaluation of the USEPA Proposed Approaches for applying a biologically based dose-response model in a risk assessment for perchlorate in drinking water. Regul Toxicol Pharmacol 2019; 103:237-252. [DOI: 10.1016/j.yrtph.2019.01.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/18/2019] [Accepted: 01/20/2019] [Indexed: 12/18/2022]
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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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Li M, Gehring R, Riviere JE, Lin Z. Probabilistic Physiologically Based Pharmacokinetic Model for Penicillin G in Milk From Dairy Cows Following Intramammary or Intramuscular Administrations. Toxicol Sci 2018; 164:85-100. [DOI: 10.1093/toxsci/kfy067] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Miao Li
- Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506
| | - Ronette Gehring
- Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506
| | - Jim E Riviere
- Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506
| | - Zhoumeng Lin
- Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506
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13
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Scherholz ML, Forder J, Androulakis IP. A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models. J Pharmacokinet Pharmacodyn 2018; 45:309-327. [DOI: 10.1007/s10928-018-9573-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 01/19/2018] [Indexed: 12/12/2022]
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Dietrich JW, Midgley JEM, Hoermann R. Editorial: "Homeostasis and Allostasis of Thyroid Function". Front Endocrinol (Lausanne) 2018; 9:287. [PMID: 29922229 PMCID: PMC5996081 DOI: 10.3389/fendo.2018.00287] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 05/15/2018] [Indexed: 12/14/2022] Open
Affiliation(s)
- Johannes W. Dietrich
- Medical Department 1, Endocrinology and Diabetology, Bergmannsheil University Hospitals, Ruhr University of Bochum, Bochum, North Rhine-Westphalia, Germany
- Ruhr Centre of Rare Diseases (CeSER), Ruhr University of Bochum, Bochum, North Rhine-Westphalia, Germany
- Ruhr Centre of Rare Diseases (CeSER), Witten/Herdecke University, Bochum, North Rhine-Westphalia, Germany
- *Correspondence: Johannes W. Dietrich,
| | | | - Rudolf Hoermann
- Private Consultancy, Research and Development, Yandina, QLD, Australia
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15
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Bell SM, Chang X, Wambaugh JF, Allen DG, Bartels M, Brouwer KLR, Casey WM, Choksi N, Ferguson SS, Fraczkiewicz G, Jarabek AM, Ke A, Lumen A, Lynn SG, Paini A, Price PS, Ring C, Simon TW, Sipes NS, Sprankle CS, Strickland J, Troutman J, Wetmore BA, Kleinstreuer NC. In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicol In Vitro 2017; 47:213-227. [PMID: 29203341 DOI: 10.1016/j.tiv.2017.11.016] [Citation(s) in RCA: 153] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 11/28/2017] [Accepted: 11/30/2017] [Indexed: 01/10/2023]
Abstract
In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.
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Affiliation(s)
- Shannon M Bell
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John F Wambaugh
- U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA.
| | - David G Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | | | - Kim L R Brouwer
- UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Campus Box 7569, Chapel Hill, NC 27599, USA.
| | - Warren M Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Neepa Choksi
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Stephen S Ferguson
- National Toxicology Program, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | | | - Annie M Jarabek
- U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA.
| | - Alice Ke
- Simcyp Limited (a Certara company), John Street, Sheffield, S2 4SU, United Kingdom.
| | - Annie Lumen
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Scott G Lynn
- U.S. Environmental Protection Agency, William Jefferson Clinton Building, 1200 Pennsylvania Ave. NW, Washington, DC 20460, USA.
| | - Alicia Paini
- European Commission, Joint Research Centre, Directorate Health, Consumers and Reference Materials, Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Via E. Fermi 2749, Ispra, Varese 20127, Italy.
| | - Paul S Price
- U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA.
| | - Caroline Ring
- Oak Ridge Institute for Science and Education, P.O. Box 2008, Oak Ridge, TN 37831, USA.
| | - Ted W Simon
- Ted Simon LLC, 4184 Johnston Road, Winston, GA 30187, USA.
| | - Nisha S Sipes
- National Toxicology Program, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Catherine S Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Judy Strickland
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John Troutman
- Central Product Safety, The Procter & Gamble Company, Cincinnati, OH 45202, USA.
| | - Barbara A Wetmore
- ScitoVation LLC, 6 Davis Drive, Research Triangle Park, NC 27709, USA.
| | - Nicole C Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
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16
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Wittwehr C, Aladjov H, Ankley G, Byrne HJ, de Knecht J, Heinzle E, Klambauer G, Landesmann B, Luijten M, MacKay C, Maxwell G, Meek MEB, Paini A, Perkins E, Sobanski T, Villeneuve D, Waters KM, Whelan M. How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology. Toxicol Sci 2017; 155:326-336. [PMID: 27994170 PMCID: PMC5340205 DOI: 10.1093/toxsci/kfw207] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.
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Affiliation(s)
| | | | - Gerald Ankley
- US Environmental Protection Agency, Duluth, Minnesota 55804
| | | | - Joop de Knecht
- National Institute for Public Health and the Environment (RIVM), Bilthoven, MA 3721, The Netherlands
| | - Elmar Heinzle
- Universität des Saarlandes, 66123 Saarbrücken, Germany
| | | | | | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Bilthoven, MA 3721, The Netherlands
| | - Cameron MacKay
- Unilever Safety and Environmenta Assurance Centre, Sharnbrook, MK44 1LQ, UK
| | - Gavin Maxwell
- Unilever Safety and Environmenta Assurance Centre, Sharnbrook, MK44 1LQ, UK
| | | | - Alicia Paini
- European Commission, Joint Research Centre, Ispra 21027, Italy
| | - Edward Perkins
- US Army Engineer Research and Development Center, Vicksburg, Mississippi 39180
| | | | - Dan Villeneuve
- US Environmental Protection Agency, Duluth, Minnesota 55804
| | - Katrina M Waters
- Pacific Northwest National Laboratory, Richland, Washington 99352
| | - Maurice Whelan
- European Commission, Joint Research Centre, Ispra 21027, Italy
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17
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Lumen A, George NI. Estimation of iodine nutrition and thyroid function status in late-gestation pregnant women in the United States: Development and application of a population-based pregnancy model. Toxicol Appl Pharmacol 2016; 314:24-38. [PMID: 27818216 DOI: 10.1016/j.taap.2016.10.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 10/27/2016] [Accepted: 10/30/2016] [Indexed: 12/15/2022]
Abstract
Previously, a deterministic biologically-based dose-response (BBDR) pregnancy model was developed to evaluate moderate thyroid axis disturbances with and without thyroid-active chemical exposure in a near-term pregnant woman and fetus. In the current study, the existing BBDR model was adapted to include a wider functional range of iodine nutrition, including more severe iodine deficiency conditions, and to incorporate empirically the effects of homeostatic mechanisms. The extended model was further developed into a population-based model and was constructed using a Monte Carlo-based probabilistic framework. In order to characterize total (T4) and free (fT4) thyroxine levels for a given iodine status at the population-level, the distribution of iodine intake for late-gestation pregnant women in the U.S was reconstructed using various reverse dosimetry methods and available biomonitoring data. The range of median (mean) iodine intake values resulting from three different methods of reverse dosimetry tested was 196.5-219.9μg of iodine/day (228.2-392.9μg of iodine/day). There was minimal variation in model-predicted maternal serum T4 and ft4 thyroxine levels from use of the three reconstructed distributions of iodine intake; the range of geometric mean for T4 and fT4, was 138-151.7nmol/L and 7.9-8.7pmol/L, respectively. The average value of the ratio of the 97.5th percentile to the 2.5th percentile equaled 3.1 and agreed well with similar estimates from recent observations in third-trimester pregnant women in the U.S. In addition, the reconstructed distributions of iodine intake allowed us to estimate nutrient inadequacy for late-gestation pregnant women in the U.S. via the probability approach. The prevalence of iodine inadequacy for third-trimester pregnant women in the U.S. was estimated to be between 21% and 44%. Taken together, the current work provides an improved tool for evaluating iodine nutritional status and the corresponding thyroid function status in pregnant women in the U.S. This model enables future assessments of the relevant risk of thyroid hormone level perturbations due to exposure to thyroid-active chemicals at the population-level.
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Affiliation(s)
- A Lumen
- Division of Biochemical Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - N I George
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
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18
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Willemin ME, Lumen A. Development of a PBPK model of thiocyanate in rats with an extrapolation to humans: A computational study to quantify the mechanism of action of thiocyanate kinetics in thyroid. Toxicol Appl Pharmacol 2016; 307:19-34. [DOI: 10.1016/j.taap.2016.07.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 07/07/2016] [Accepted: 07/15/2016] [Indexed: 12/13/2022]
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Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans. Antimicrob Agents Chemother 2016; 60:4860-8. [PMID: 27270284 DOI: 10.1128/aac.00031-16] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 05/25/2016] [Indexed: 01/21/2023] Open
Abstract
Rifapentine (RPT) is a rifamycin antimycobacterial and, as part of a combination therapy, is indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis Although the results from a number of studies indicate that rifapentine has the potential to shorten treatment duration and enhance completion rates compared to other rifamycin agents utilized in antituberculosis drug regimens (i.e., regimens 1 to 4), its optimal dose and exposure in humans are unknown. To help inform such an optimization, a physiologically based pharmacokinetic (PBPK) model was developed to predict time course, tissue-specific concentrations of RPT and its active metabolite, 25-desacetyl rifapentine (dRPT), in humans after specified administration schedules for RPT. Starting with the development and verification of a PBPK model for rats, the model was extrapolated and then tested using human pharmacokinetic data. Testing and verification of the models included comparisons of predictions to experimental data in several rat tissues and time course RPT and dRPT plasma concentrations in humans from several single- and repeated-dosing studies. Finally, the model was used to predict RPT concentrations in the lung during the intensive and continuation phases of a current recommended TB treatment regimen. Based on these results, it is anticipated that the PBPK model developed in this study will be useful in evaluating dosing regimens for RPT and for characterizing tissue-level doses that could be predictors of problems related to efficacy or safety.
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20
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Boas SEM, Navarro Jimenez MI, Merks RMH, Blom JG. A global sensitivity analysis approach for morphogenesis models. BMC SYSTEMS BIOLOGY 2015; 9:85. [PMID: 26589144 PMCID: PMC4654849 DOI: 10.1186/s12918-015-0222-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/26/2015] [Indexed: 02/03/2023]
Abstract
BACKGROUND Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such 'black-box' models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. RESULTS To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. CONCLUSIONS We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all 'black-box' models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.
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Affiliation(s)
- Sonja E M Boas
- Life Sciences, CWI, Science Park 123, Amsterdam, 1098XG, The Netherlands.
- Mathematical Institute, University of Leiden, Niels Bohrweg 1, Leiden, 2333CA, The Netherlands.
| | - Maria I Navarro Jimenez
- CEMSE Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
| | - Roeland M H Merks
- Life Sciences, CWI, Science Park 123, Amsterdam, 1098XG, The Netherlands.
- Mathematical Institute, University of Leiden, Niels Bohrweg 1, Leiden, 2333CA, The Netherlands.
| | - Joke G Blom
- Life Sciences, CWI, Science Park 123, Amsterdam, 1098XG, The Netherlands.
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