High-throughput PBTK models for in vitro to in vivo extrapolation

References

• Park YH, Lee K, Soltow QA, et al. High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring. Toxicology. 2012 May 16;295(1–3):47–55.
   PubMed Web of Science ®Google Scholar
• Rager JE, Strynar MJ, Liang S, et al. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ Int. 2016;88:269–280.
   PubMed Web of Science ®Google Scholar
• Egeghy PP, Judson R, Gangwal S, et al. The exposure data landscape for manufactured chemicals. SciTotal Environ. 2012 1; Jan(414): 159–166.
   Google Scholar
• Judson R, Richard A, Dix DJ, et al. The toxicity data landscape for environmental chemicals. Environ Health Perspect. 2009;117(5):685.
   PubMed Web of Science ®Google Scholar
• Breyer S. Breaking the vicious circle: toward effective risk regulation. Cambridge (MA): Harvard University Press; 2009.
   Google Scholar
• Bell SM, Chang X, Wambaugh JF, et al. In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicol in Vitro. 2018;47:213–227. 2018 March 01.
   PubMed Web of Science ®Google Scholar
• Bessems JG, Loizou G, Krishnan K, et al., PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment: recommendations from a joint EPAA–EURL ECVAM ADME workshop. Regul Toxicol Pharmacol. 68(1): 119–139. 2014.
   PubMed Web of Science ®Google Scholar
• Schmidt CW. TOX 21: new dimensions of toxicity testing. Research Triangle Park (NC): National Institute of Environmental Health Sciences; 2009.
   Google Scholar
• Dix DJ, Houck KA, Martin MT, et al. The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci. 2007;95(1):5–12.
   PubMed Web of Science ®Google Scholar
• Kavlock RJ, Bahadori T, Barton-Maclaren TS, et al. Accelerating the pace of chemical risk assessment. Chem Res Toxicol. 2018 [2018 May 21];31(5):287–290.
   PubMed Web of Science ®Google Scholar
• Coecke S, Pelkonen O, Leite SB, et al., Toxicokinetics as a key to the integrated toxicity risk assessment based primarily on non-animal approaches. Toxicol in Vitro. 27(5): 1570–1577. 2013.
   PubMed Web of Science ®Google Scholar
• Wetmore BA, Wambaugh JF, Allen B, et al. Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing. Toxicol Sci. 2015 Nov;148(1):121–136.
   PubMed Web of Science ®Google Scholar
• O’Flaherty EJ. Toxicants and drugs: kinetics and dynamics. Hoboken (NJ): John Wiley & Sons; 1981.
   Google Scholar
• Tan Y-M, Liao KH, Clewell HJ. Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling. J Expos Sci Envir Epidemiol. 2007;17(7):591–603.
   PubMed Web of Science ®Google Scholar
• Sobus JR, DeWoskin RS, Tan YM, et al. Uses of NHANES biomarker data for chemical risk assessment: trends, challenges, and opportunities. Environ Health Perspect. 2015 Oct;123(10):919–927.
   PubMed Web of Science ®Google Scholar
• Mage DT, Allen RH, Gondy G, et al. Estimating pesticide dose from urinary pesticide concentration data by creatinine correction in the Third National Health and Nutrition Examination Survey (NHANES-III). J Expos Sci Envir Epidemiol. 2004;14(6):457.
   Web of Science ®Google Scholar
• Lakind JS, Naiman DQ. Bisphenol A (BPA) daily intakes in the United States: estimates from the 2003–2004 NHANES urinary BPA data. J Expos Sci Envir Epidemiol. 2008;18(6):608.
   PubMed Web of Science ®Google Scholar
• Wetmore BA. Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment. Toxicology. 2015;332:94–101.
   PubMed Web of Science ®Google Scholar
• Hertzberg RP, Pope AJ. High-throughput screening: new technology for the 21st century. Curr Opin Chem Biol. 2000 Aug;4(4):445–451.
   PubMed Web of Science ®Google Scholar
• Tolonen A, Pelkonen O. Analytical challenges for conducting rapid metabolism characterization for QIVIVE. Toxicology. 2015. 2015 June 05. 332:20–29.
   PubMed Web of Science ®Google Scholar
• Armitage JM, Wania F, Arnot JA. Application of mass balance models and the chemical activity concept to facilitate the use of in vitro toxicity data for risk assessment. Environ Sci Technol. 2014;48(16):9770–9779.
   PubMed Web of Science ®Google Scholar
• Honda GS, Pearce RG, Pham LL, et al., Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions. PLOS ONE. 14(5): e0217564. 2019.
   PubMed Web of Science ®Google Scholar
• Wetmore BA, Wambaugh JF, Ferguson SS, et al. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicol Sci. 2012 Jan;125(1):157–174.
   PubMed Web of Science ®Google Scholar
• Wambaugh JF, Hughes MF, Ring CL, et al., Evaluating in vitro-in vivo extrapolation of toxicokinetics. Toxicol Sci. 2018;163(1): 152–169.
   PubMed Web of Science ®Google Scholar
• Paul Friedman K, Gagne M, Loo L-H, et al., Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Toxicol Sci. 173(1): 202–225. 2020.
   PubMed Web of Science ®Google Scholar
• Jamei M, Marciniak S, Feng K, et al., The Simcyp® population-based ADME simulator. Expert Opin Drug Metab Toxicol. 5(2): 211–223. 2009.
   PubMed Web of Science ®Google Scholar
• Wang Y-H. 2010; Confidence assessment of the simcyp time-based approach and a static mathematical model in predicting clinical drug-drug interactions for mechanism-based CYP3A Inhibitors. Drug Metab Dispos.Jul 1. 38(7):1094–1104.
   PubMed Web of Science ®Google Scholar
• Chen Y, Jin JY, Mukadam S, et al. Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies. Biopharm Drug Dispos. 2012 Mar;33(2):85–98.
   PubMed Web of Science ®Google Scholar
• Rotroff DM, Wetmore BA, Dix DJ, et al., Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicol Sci. 117(2): 348–358. 2010.
   PubMed Web of Science ®Google Scholar
• Tonnelier A, Coecke S, Zaldívar J-M. Screening of chemicals for human bioaccumulative potential with a physiologically based toxicokinetic model. Arch Toxicol. 2012;86(3):393–403.
   PubMed Web of Science ®Google Scholar
• Wetmore BA, Wambaugh JF, Ferguson SS, et al. Relative impact of incorporating pharmacokinetics on predicting in vivo hazard and mode of action from high-throughput in vitro toxicity assays. Toxicol Sci. 2013 Apr;132(2):327–346.
   PubMed Web of Science ®Google Scholar
• Wetmore BA, Allen B, Clewell HJ 3rd, et al. Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing. Toxicol Sci. 2014 Nov;142(1):210–224.
   PubMed Web of Science ®Google Scholar
• Paini A, Cole T, Meinero M, et al. EURL ECVAM in vitro hepatocyte clearance and blood plasma protein binding dataset for 77 chemicals. Eur Comm Joint Res Centre (JRC). 2020. [cited 2021 Apr 1]. Available from: https://data.europa.eu/89h/a2ff867f-db80-4acf-8e5c-e45502713bee
   Google Scholar
• Ito K, Houston JB. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res. 2004 May;21(5):785–792.
   PubMed Web of Science ®Google Scholar
• Lau YY, Sapidou E, Cui X, et al. Development of a novel in vitro model to predict hepatic clearance using fresh, cryopreserved, and sandwich-cultured hepatocytes. Drug Metab Dispos. 2002 Dec;30(12):1446–1454.
   PubMed Web of Science ®Google Scholar
• McGinnity DF, Soars MG, Urbanowicz RA, et al. Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab Dispos. 2004 Nov;32(11):1247–1253.
   PubMed Web of Science ®Google Scholar
• Naritomi Y, Terashita S, Kagayama A, et al. Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab Dispos. 2003 May;31(5):580–588.
   PubMed Web of Science ®Google Scholar
• Shibata Y, Takahashi H, Chiba M, et al. Prediction of hepatic clearance and availability by cryopreserved human hepatocytes: an application of serum incubation method. Drug Metab Dispos. 2002;30(8):892–896.
   PubMed Web of Science ®Google Scholar
• Package ‘httk’. [cited 2021 Apr 1]. Available from: https://CRAN.R-project.org/package=httk
   Google Scholar
• Pearce RG, Setzer RW, Strope CL, et al., Httk: r package for high-throughput toxicokinetics. J Stat Softw. 79(1): 1–26. 2017.
   PubMedGoogle Scholar
• Health Canada. Science approach document - Bioactivity exposure ratio: application in priority setting and risk assessment. 2021.
   Google Scholar
• Espié P, Tytgat D, M-l S-M, et al. Physiologically based pharmacokinetics (PBPK). Drug Metab Rev. 2009;41(3):391–407.
   PubMed Web of Science ®Google Scholar
• Grech A, Brochot C, Dorne J-L, et al. Toxicokinetic models and related tools in environmental risk assessment of chemicals. SciTotal Environ. 2017;578:1–15.
   Google Scholar
• U.S. EPA. Approaches for the application of physiologically based pharmacokinetic (PBPK) models and supporting data in risk assessment. Washington, DC: National Center for Environmental Assessment; 2006.
   Google Scholar
• Andersen ME. Development of physiologically based pharmacokinetic and physiologically based pharmacodymamic models for applications in toxicology and risk assessment. Toxicol Lett. 1995;79(1):35–44.
   PubMed Web of Science ®Google Scholar
• Campbell JL, Clewell RA, Gentry PR, et al. Physiologically based pharmacokinetic/toxicokinetic modeling. Computational Toxicology: Volume I. 2012:439–499.
   Google Scholar
• Pearce RG, Setzer RW, Davis JL, et al. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn. 2017 [2017 December 01];44(6):549–565.
   PubMed Web of Science ®Google Scholar
• Barton HA, Chiu WA, Setzer RW, et al., Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation. Toxicol Sci. 99(2): 395–402. 2007.
   PubMed Web of Science ®Google Scholar
• Rostami‐Hodjegan A. Physiologically based pharmacokinetics joined with in vitro–in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther. 2012;92(1):50–61.
   PubMed Web of Science ®Google Scholar
• Clewell III HJ, Andersen ME. Dose, species, and route extrapolation using physiologically based pharmacokinetic models. Drink Water Health Vol 8: Pharmacokinet Risk Assess. 1987;8:159.
   Google Scholar
• Clewell III HJ, Gentry PR, Gearhart JM, et al. Development of a physiologically based pharmacokinetic model of isopropanol and its metabolite acetone. Toxicol Sci. 2001;63(2):160–172.
   PubMed Web of Science ®Google Scholar
• Sweeney LM, Kirman CR, Gannon SA, et al. Development of a physiologically based pharmacokinetic (PBPK) model for methyl iodide in rats, rabbits, and humans. Inhal Toxicol. 2009;21(6):552–582.
   PubMed Web of Science ®Google Scholar
• Jones HM, Barton HA, Lai Y, et al. Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos. 2012;40(5):1007–1017.
   PubMed Web of Science ®Google Scholar
• Parham F, Kohn M, Matthews H, et al., Using structural information to create physiologically based pharmacokinetic models for all polychlorinated biphenyls: i. Tissue: blood partition coefficients. Toxicol Appl Pharmacol. 144(2): 340–347. 1997.
   PubMed Web of Science ®Google Scholar
• Clewell HJ, Gearhart JM, Gentry PR, et al. Evaluation of the uncertainty in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics. Risk Anal. 1999;19(4):547–558.
   PubMed Web of Science ®Google Scholar
• Ring CL, Pearce RG, Setzer RW, et al. Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability. Environ Int. 2017;106:105–118. 2017 September 01.
   PubMed Web of Science ®Google Scholar
• Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov. 2007;6(2):140–148.
   PubMed Web of Science ®Google Scholar
• Thomas RS, Bigelow PL, Keefe TJ, et al. Variability in biological exposure indices using physiologically based pharmacokinetic modeling and Monte Carlo simulation. Am Indust Hyg Assoc J. 57(1): 23–32. 1996.
   PubMed Web of Science ®Google Scholar
• Liao KH, Tan YM, Conolly RB, et al. Bayesian Estimation Of Pharmacokinetic And Pharmacodynamic Parameters In A Mode‐Of‐Action‐Based Cancer Risk Assessment For Chloroform. Risk Anal. 2007;27(6):1535–1551.
   PubMed Web of Science ®Google Scholar
• Luecke RH, Wosilait WD, Pearce BA, et al. A physiologically based pharmacokinetic computer model for human pregnancy. Teratology. 1994;49(2):90–103.
   PubMedGoogle Scholar
• World Health Organization. Characterization and application of physiologically based pharmacokinetic models in risk assessment. Geneva, Switzerland: World Health Organization, International Programme on Chemical Safety; 2010.
   Google Scholar
• Corley RA, Bartels M, Carney E, et al. Development of a physiologically based pharmacokinetic model for ethylene glycol and its metabolite, glycolic acid, in rats and humans. Toxicol Sci. 2005;85(1):476–490.
   PubMed Web of Science ®Google Scholar
• Fisher JW, Twaddle NC, Vanlandingham M, et al. Pharmacokinetic modeling: prediction and evaluation of route dependent dosimetry of bisphenol A in monkeys with extrapolation to humans. Toxicol Appl Pharmacol. 2011 Nov 15;257(1):122–136.
   PubMed Web of Science ®Google Scholar
• Docci L, Umehara K, Krähenbühl S, et al. Construction and verification of physiologically based pharmacokinetic models for four drugs majorly cleared by Glucuronidation: lorazepam, Oxazepam, Naloxone, and Zidovudine. Aaps J. 2020 Oct 8;22(6):128.
   PubMed Web of Science ®Google Scholar
• Cohen Hubal EA, Wetmore B, Wambaugh J, et al. Advancing internal exposure and physiologically-based toxicokinetic modeling for 21st-century risk assessments. J Expos Sci Envir Epidemiol. 2018.
   PubMed Web of Science ®Google Scholar
• McLanahan ED, El-Masri HA, Sweeney LM, et al., Physiologically based pharmacokinetic model use in risk assessment—why being published is not enough. Toxicol Sci. 126(1): 5–15. 2012.
   PubMed Web of Science ®Google Scholar
• Chiu WA, Barton HA, DeWoskin RS, et al. Evaluation of physiologically based pharmacokinetic models for use in risk assessment. J Appl Toxicol. 2007;27(3):218–237.
   PubMed Web of Science ®Google Scholar
• Tan YM, Worley RR, Leonard JA, et al. Challenges associated with applying physiologically based pharmacokinetic modeling for public health decision-making. Toxicol Sci. 2018 Apr 1;162(2):341–348.
   PubMed Web of Science ®Google Scholar
• Ioannidis JPA. Reproducible pharmacokinetics. J Pharmacokinet Pharmacodyn. 2019 Apr;46(2):111–116.
   PubMed Web of Science ®Google Scholar
• Kirouac DC, Cicali B, Schmidt S. Reproducibility of Quantitative Systems Pharmacology Models: current Challenges and Future Opportunities. CPT Pharmacometrics Syst Pharmacol. 2019 Apr;8(4):205–210.
   PubMedGoogle Scholar
• Clark LH, Setzer RW, Barton HA. Framework for evaluation of physiologically‐based pharmacokinetic models for use in safety or risk assessment. Risk Anal. 2004;24(6):1697–1717.
   PubMed Web of Science ®Google Scholar
• Loizou G, Spendiff M, Barton HA, et al. Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: the first steps. Regul Toxicol Pharmacol. 2008;50(3):400–411.
   PubMed Web of Science ®Google Scholar
• Madden JC, Pawar G, Cronin MTD, et al. In silico resources to assist in the development and evaluation of physiologically-based kinetic models. Comput Toxicol. 2019 [2019 August 01];11:33–49.
   Google Scholar
• Ruiz P, Ray M, Fisher J, et al. Development of a human physiologically based pharmacokinetic (PBPK) toolkit for environmental pollutants. Int J Mol Sci. 2011;12(11):7469–7480.
   PubMed Web of Science ®Google Scholar
• Tan Y-M, Chan M, Chukwudebe A, et al. PBPK model reporting template for chemical risk assessment applications. Regulatory Toxicology and Pharmacology. 2020; 115:Article 104691.
   Google Scholar
• Lu J, Goldsmith M-R, Grulke CM, et al. Developing a physiologically-based pharmacokinetic model knowledgebase in support of provisional model construction. PLoS Comput Biol. 2016;12(2):e1004495.
   PubMed Web of Science ®Google Scholar
• Van Den Berg M, De Jongh J, Poiger H, et al. The toxicokinetics and metabolism of Polychlorinated Dibenzo-p-Dioxins (PCDDs) and Dibenzofurans (PCDFs) and their relevance for toxicity. Crit Rev Toxicol. 1994 [1994 January 01];24(1):1–74.
   PubMed Web of Science ®Google Scholar
• Geyer HJ, Schramm KW, Feicht EA, et al. Half-lives of tetra-, penta-, hexa-, hepta-, and octachlorodibenzo-p-dioxin in rats, monkeys, and humans–a critical review. Chemosphere. 2002 Aug;48(6):631–644.
   PubMed Web of Science ®Google Scholar
• Fernández-González R, Yebra-Pimentel I, Martínez-Carballo E, et al. A Critical Review about Human Exposure to Polychlorinated Dibenzo-p-Dioxins (PCDDs), Polychlorinated Dibenzofurans (PCDFs) and Polychlorinated Biphenyls (PCBs) through foods. Crit Rev Food Sci Nutr. 2015;55(11):1590–1617.
   PubMed Web of Science ®Google Scholar
• Barbosa F Jr., Tanus-Santos JE, Gerlach RF, et al. A critical review of biomarkers used for monitoring human exposure to lead: advantages, limitations, and future needs. Environ Health Perspect. 2005 Dec;113(12):1669–1674.
   PubMed Web of Science ®Google Scholar
• Mushak P. New directions in the toxicokinetics of human lead exposure. Neurotoxicology. 1993;14(2–3):29–42. Summer-Fall
   PubMed Web of Science ®Google Scholar
• Philip AT, Gerson B. Lead poisoning-part i: incidence, etiology, and toxicokinetics. Clin Lab Med. 1994 [1994 June 01];14(2):423–444.
   PubMed Web of Science ®Google Scholar
• Chiu WA, Jinot J, Scott CS, et al. Human health effects of trichloroethylene: key findings and scientific issues. Environ Health Perspect. 2013 Mar;121(3):303–311.
   PubMed Web of Science ®Google Scholar
• Chiu WA, Micallef S, Monster AC, et al. Toxicokinetics of inhaled trichloroethylene and tetrachloroethylene in humans at 1 ppm: empirical results and comparisons with previous studies. Toxicol Sci. 2007;95(1):23–36.
   PubMed Web of Science ®Google Scholar
• Haddad S, Tardif GC, Tardif R. Development of physiologically based toxicokinetic models for improving the human indoor exposure assessment to water contaminants: trichloroethylene and trihalomethanes. J Toxicol Environ Health A. 2006 Dec;69(23):2095–2136.
   PubMed Web of Science ®Google Scholar
• Van Der Molen G, Kooijman S, Slob W. A generic toxicokinetic model for persistent lipophilic compounds in humans: an application to TCDD. Fundament applToxicol. 1996;31(1):83–94.
   PubMedGoogle Scholar
• Sager JE, Yu J, Ragueneau-Majlessi I, et al. Physiologically Based Pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos. 2015 Nov;43(11):1823–1837.
   PubMed Web of Science ®Google Scholar
• Miller NA, Reddy MB, Heikkinen AT, et al. Physiologically based pharmacokinetic modelling for first-in-human predictions: an updated model building strategy illustrated with challenging industry case studies. Clin Pharmacokinet. 2019 [2019 June 01];58(6):727–746.
   PubMed Web of Science ®Google Scholar
• Brightman FA, Leahy DE, Searle GE, et al. Application of a generic physiologically based pharmacokinetic model to the estimation of xenobiotic levels in human plasma. Drug Metab Dispos. 2006;34(1):94–101.
   PubMed Web of Science ®Google Scholar
• Cahill TM, Cousins I, Mackay D. Development and application of a generalized physiologically based pharmacokinetic model for multiple environmental contaminants. Environ Toxicol Chem Int J. 2003;22(1):26–34.
   PubMed Web of Science ®Google Scholar
• Edginton AN, Schmitt W, Willmann S. Development and evaluation of a generic physiologically based pharmacokinetic model for children. Clin Pharmacokinet. 2006;45(10):1013–1034.
   PubMed Web of Science ®Google Scholar
• Lukacova V, Woltosz WS, Bolger MB. Prediction of modified release pharmacokinetics and pharmacodynamics from in vitro, immediate release, and intravenous data. AAPS J. 2009;11(2):323–334.
   PubMed Web of Science ®Google Scholar
• Eissing T, Kuepfer L, Becker C, et al. A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol. 2011;2:4.
   PubMed Web of Science ®Google Scholar
• Jongeneelen FJ, Berge WFT. A generic, cross-chemical predictive PBTK model with multiple entry routes running as application in MS Excel; design of the model and comparison of predictions with experimental results. Ann Occupat Hyg. 2011;55(8):841–864.
   PubMedGoogle Scholar
• Punt A, Pinckaers N, Peijnenburg A, et al. Development of a web-based toolbox to support Quantitative In-Vitro-to-In-Vivo Extrapolations (QIVIVE) within Nonanimal Testing Strategies. Chem Res Toxicol. 2021 Feb 15;34(2):460–472.
   PubMed Web of Science ®Google Scholar
• PK-Sim. [cited 2021 Apr 1]. Available from: http://www.open-systems-pharmacology.org/
   Google Scholar
• IndusChemFate. [cited 2021 Apr 1]. Available from: http://cefic-lri.org/toolbox/induschemfate/
   Google Scholar
• Package “pbktool”. [cited 2021 Apr 1]. Available from: https://github.com/wfsrqivive/pbktool
   Google Scholar
• EXPOSURE AND SAFETY ESTIMATION (EAS-E) SUITE. [cited 2021 Apr 1]. Available from: https://arnotresearch.com/eas-e-suite/
   Google Scholar
• Kisitu J, Hollert H, Fisher C, et al. Chemical concentrations in cell culture compartments (C5) - free concentrations. Altex. 2020;37(4):693–708.
   PubMedGoogle Scholar
• Comprehensive R Archive Network (CRAN). [cited 2021 Apr 1]. Available from: https://cloud.r-project.org/
   Google Scholar
• RStudio. [cited 2021 Apr 1]. Available from: https://www.rstudio.com/
   Google Scholar
• Pendse SN, Efremenko A, Hack CE, et al. Population Life-course exposure to health effects model (PLETHEM): an R package for PBPK modeling. Comput Toxicol. 2020 2020 Feb 01;13:100115.
   Google Scholar
• Bell S, Abedini J, Ceger P, et al. An integrated chemical environment with tools for chemical safety testing. Toxicol in Vitro. 2020 [2020 September 01];67:104916.
   PubMed Web of Science ®Google Scholar
• R-Shiny. [cited 2021 Apr 1]. Available from: https://shiny.rstudio.com/
   Google Scholar
• Berthold MR, Cebron N, Dill F, et al. KNIME-the Konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explorations Newsl. 2009;11(1):26–31.
   Google Scholar
• KNIME. Available from: https://www.knime.com/nodeguide/scripting/r
   Google Scholar
• Linakis MW, Sayre RR, Pearce RG, et al. Development and evaluation of a high throughput inhalation model for organic chemicals. J Expo Sci Environ Epidemiol. 2020;30(5):866–877.
   Web of Science ®Google Scholar
• Sayre RR, Wambaugh JF, Grulke CM. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Sci Data. 2020Apr20;7(1):122.
   PubMed Web of Science ®Google Scholar
• Waters NJ, Jones R, Williams G, et al. Validation of a rapid equilibrium dialysis approach for the measurement of plasma protein binding. J Pharm Sci. 2008;97(10):4586–4595.
   PubMed Web of Science ®Google Scholar
• Kilford PJ, Gertz M, Houston JB, et al. Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data. Drug Metab Dispos. 2008 Jul;36(7):1194–1197.
   PubMed Web of Science ®Google Scholar
• Wambaugh JF, Wetmore BA, Ring CL, et al. Assessing toxicokinetic uncertainty and variability in risk prioritization. Toxicol Sci. 2019 Dec 1;172(2):235–251.
   PubMed Web of Science ®Google Scholar
• Wilkinson GR, Shand DG. Commentary: a physiological approach to hepatic drug clearance. Clin Pharmacol Ther. 1975;18(4):377–390.
   PubMed Web of Science ®Google Scholar
• Hubatsch I, Ragnarsson EG, Artursson P. Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers. Nat Protoc. 2007;2(9):2111.
   PubMed Web of Science ®Google Scholar
• Artursson P. Epithelial transport of drugs in cell culture. I: a model for studying the passive diffusion of drugs over intestinal absorptive (Caco-2) cells. J Pharm Sci. 1990 Jun;79(6):476–482.
   PubMed Web of Science ®Google Scholar
• Artursson P, Magnusson C. Epithelial transport of drugs in cell culture. II: effect of extracellular calcium concentration on the paracellular transport of drugs of different lipophilicities across monolayers of intestinal epithelial (Caco-2) cells. J Pharm Sci. 1990 Jul;79(7):595–600.
   PubMed Web of Science ®Google Scholar
• Mandagere AK, Thompson TN, Hwang KK. Graphical model for estimating oral bioavailability of drugs in humans and other species from their Caco-2 permeability and in vitro liver enzyme metabolic stability rates. J Med Chem. 2002 Jan 17;45(2):304–311.
   PubMed Web of Science ®Google Scholar
• Davies B, Morris T. Physiological parameters in laboratory animals and humans. Pharm Res. 1993 Jul;10(7):1093–1095.
   PubMed Web of Science ®Google Scholar
• Carlile DJ, Zomorodi K, Houston JB. Scaling factors to relate drug metabolic clearance in hepatic microsomes, isolated hepatocytes, and the intact liver: studies with induced livers involving diazepam. Drug Metab Dispos. 1997 Aug;25(8):903–911.
   PubMed Web of Science ®Google Scholar
• International Commission on Radiological Protection. Report of the task group on reference man. Vol. 23. Pergamon, Oxford. 1975.
   Google Scholar
• Mansouri K, Grulke CM, Judson RS, et al., OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminform. 10(1): 10. 2018.
   PubMedGoogle Scholar
• Ingle BL, Veber BC, Nichols JW, et al. Informing the human plasma protein binding of environmental chemicals by machine learning in the pharmaceutical space: applicability domain and limits of predictability. J Chem Inf Model. 2016;56(11):2243–2252.
   PubMed Web of Science ®Google Scholar
• Yin Y, Chang D, Grulke C, et al. Essential set of molecular descriptors for ADME prediction in drug and environmental chemical space. Research. 2014;1:996.
   Google Scholar
• Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Top Med Chem. 2008;8(18):1555–1572.
   PubMed Web of Science ®Google Scholar
• Schmitt W. General approach for the calculation of tissue to plasma partition coefficients. Toxicol in Vitro. 2008;22(2):457–467.
   PubMed Web of Science ®Google Scholar
• Peyret T, Poulin P, Krishnan K. A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals. Toxicol Appl Pharmacol. 2010;249(3):197–207.
   PubMed Web of Science ®Google Scholar
• Metropolis N, Ulam S. The Monte Carlo method. J Am Stat Assoc. 1949 Sep;44(247):335–341.
   PubMed Web of Science ®Google Scholar
• Thomas RS, Lytle WE, Keefe TJ, et al. Incorporating Monte Carlo simulation into physiologically based pharmacokinetic models using advanced continuous simulation language (ACSL): a computational method. Toxicol Sci. 1996;31(1):19–28.
   Google Scholar
• Hoffman FO, Hammonds JS. Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Anal. 1994;14(5):707–712.
   PubMed Web of Science ®Google Scholar
• Bois FY. Analysis of PBPK models for risk characterization. Ann N Y Acad Sci. 1999;895(1):317–337.
   PubMedGoogle Scholar
• Kramer NI, Krismartina M, Rico-Rico A, et al. Quantifying processes determining the free concentration of phenanthrene in Basal cytotoxicity assays. Chem Res Toxicol. 2012 Feb 20;25(2):436–445.
   PubMed Web of Science ®Google Scholar
• Henneberger L, Mühlenbrink M, Heinrich DJ, et al. Experimental validation of mass balance models for in vitro cell-based bioassays. Environ Sci Technol. 2020 [2020 January 21];54(2):1120–1127.
   PubMed Web of Science ®Google Scholar
• Groothuis FA, Heringa MB, Nicol B, et al. Dose metric considerations in in vitro assays to improve quantitative in vitro–in vivo dose extrapolations. Toxicology. 2015;332:30–40.
   PubMed Web of Science ®Google Scholar
• Groothuis FA, Timmer N, Opsahl E, et al. Influence of in vitro assay setup on the apparent cytotoxic potency of benzalkonium chlorides. Chem Res Toxicol. 2019 [2019 June 17];32(6):1103–1114.
   PubMed Web of Science ®Google Scholar
• Proença S, Escher BI, Fischer FC, et al. Effective exposure of chemicals in in vitro cell systems: a review of chemical distribution models. Toxicol In Vitro. 2021 Jun;73:105133.
   PubMed Web of Science ®Google Scholar
• Fischer FC, Henneberger L, König M, et al. Modeling exposure in the Tox21 in vitro bioassays. Chem Res Toxicol. 2017 [2017 May 15];30(5):1197–1208.
   PubMed Web of Science ®Google Scholar
• Fischer FC, Henneberger L, König M, et al. Modeling exposure in the Tox21 in vitro bioassays. Chem Res Toxicol. 2017;30(5):1197–1208.
   PubMed Web of Science ®Google Scholar
• CompTox Chemicals Dashboard. [cited 2021 Apr 1]. Available from: https://comptox.epa.gov/dashboard
   Google Scholar
• Oreskes N.Evaluation ON. (not validation) of quantitative models. Environ Health Perspect. 1998;106(Suppl 6):1453.
   PubMedGoogle Scholar
• Wambaugh JF, Wetmore BA, Pearce R, et al., Toxicokinetic triage for environmental chemicals. Toxicol Sci. 147(1): 55–67. 2015.
   PubMed Web of Science ®Google Scholar
• Armitage JM, Hughes L, Sangion A, et al. Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: implications for model selection and tiered modeling frameworks. Environ Int. 2021 Apr;23(154):106557.
   Google Scholar
• Yoon M, Efremenko A, Blaauboer BJ, et al., Evaluation of simple in vitro to in vivo extrapolation approaches for environmental compounds. Toxicol in Vitro. 28(2): 164–170. 2014.
   PubMed Web of Science ®Google Scholar
• Casey WM, Chang X, Allen DG, et al. Evaluation and optimization of pharmacokinetic models for in vitro to in vivo extrapolation of estrogenic activity for environmental chemicals. Environ Health Perspect. 2018;126(9):097001.
   PubMed Web of Science ®Google Scholar
• Kamiya Y, Otsuka S, Miura T, et al. Physiologically based pharmacokinetic models predicting renal and hepatic concentrations of industrial chemicals after virtual oral doses in rats. Chemical research in toxicology. 2020;33(7)1736–1751.
   Google Scholar
• Paini A, Leonard JA, Joossens E, et al. Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. Comput Toxicol. 2019 [2019 Feb 01];9:61–72.
   PubMedGoogle Scholar
• Zavala J, Freedman AN, Szilagyi JT, et al. New approach methods to evaluate health risks of air pollutants: critical design considerations for in vitro exposure testing. Int J Environ Res Public Health. 2020;17(6):2124.
   Web of Science ®Google Scholar
• National Research Council. Risk assessment in the federal government: managing the process. Washington (DC): National Academies Press; 1983.
   Google Scholar
• Hack C, Efremenko A, Pendse S, et al. Physiologically based pharmacokinetic modeling software. In: Physiologically Based Pharmacokinetic (PBPK) Modeling. Cambridge (MA): Academic Press. 2020. p. 81–126.
   Google Scholar
• Yoon M, Ring C, Van Landingham CB, et al. Assessing children’s exposure to manganese in drinking water using a PBPK model. Toxicol Appl Pharmacol. 2019 Oct;1(380):114695.
   Google Scholar
• Bois FY. GNU MCSim: bayesian statistical inference for SBML-coded systems biology models. Bioinformatics. 2009Jun1;25(11):1453–1454.
   PubMed Web of Science ®Google Scholar
• Sipes NS, Wambaugh JF, Pearce R, et al. An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library. Environ Sci Technol. 2017;51(18):10786–10796. 2017 September 19.
   PubMed Web of Science ®Google Scholar
• Pradeep P, Patlewicz G, Pearce R, et al. Using chemical structure information to develop predictive models for in vitro toxicokinetic parameters to inform high-throughput risk-assessment. Comput Toxicol. 2020 [2020 november 01];16:100136.
   Google Scholar
• Yun YE, Tornero-Velez R, Purucker ST, et al. Evaluation of quantitative structure property relationship algorithms for predicting plasma protein binding in humans. Computational Toxicology. 2020;17: Article 100142.
   Google Scholar
• Kirman CR, Aylward LL, Wetmore BA, et al. Quantitative property–property relationship for screening-level prediction of intrinsic clearance: a tool for exposure modeling for high-throughput toxicity screening data. Appl In Vitro Toxicol. 2015;1(2):140–146.
   Google Scholar
• Dawson DE, Ingle BL, Phillips KA, et al. Designing QSARs for parameters of high-throughput toxicokinetic models using open-source descriptors. Environ Sci Technol. 2021 May 4;55(9):6505–6517.
   PubMed Web of Science ®Google Scholar
• Cohen Hubal EA, Richard A, Aylward L, et al. Advancing exposure characterization for chemical evaluation and risk assessment. J Toxicol Environ Health B. 2010;13(2–4):299–313.
   PubMed Web of Science ®Google Scholar
• Williams AJ, Grulke CM, Edwards J, et al. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J Cheminform. 2017;9(1):61.
   PubMedGoogle Scholar
• Schrodinger. [cited 2021 Apr 1]. Available from: https://www.schrodinger.com/drug-discovery
   Google Scholar
• Ankley GT, Bennett RS, Erickson RJ, et al. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem. 2010;29(3):730–741.
   PubMed Web of Science ®Google Scholar