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  • Review Article
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Exposure forecasting – ExpoCast – for data-poor chemicals in commerce and the environment

Journal of Exposure Science & Environmental Epidemiology volume 32, pages 783–793 (2022)Cite this article

Abstract

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA’s ExpoCast (“Exposure Forecasting”) project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA’s ToxCast (“Toxicity Forecasting”) project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

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Fig. 1: Timeline of ExpoCast research.

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References

  1. Zartarian V, Bahadori T, McKone T Adoption of an official ISEA glossary. Journal of Exposure Analysis & Environmental Epidemiology 15 2005.

  2. Mattingly CJ, McKone TE, Callahan MA, Blake JA, Cohen Hubal EA. Providing the missing link: the exposure science ontology ExO. Environ Sci Technol. 2012;46:3046–53.

    CAS  Google Scholar 

  3. Meyer DE, Bailin SC, Vallero D, Egeghy PP, Liu SV, Hubal EAC. Enhancing life cycle chemical exposure assessment through ontology modeling. Sci Total Environ. 2020;712:136263.

    CAS  Google Scholar 

  4. Nieuwenhuijsen M, Paustenbach D, Duarte-Davidson R. New developments in exposure assessment: the impact on the practice of health risk assessment and epidemiological studies. Environ Int. 2006;32:996–1009.

    CAS  Google Scholar 

  5. Rosenbaum RK, Bachmann TM, Gold LS, Huijbregts MA, Jolliet O, Juraske R, et al. USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. The. Int J Life Cycle Assess. 2008;13:532–46.

    CAS  Google Scholar 

  6. Hertzberg RP, Pope AJ. High-throughput screening: new technology for the 21st century. Curr Opin Chem Biol. 2000;4:445–51.

    CAS  Google Scholar 

  7. National Research Council. Toxicity testing in the 21st century: A vision and a strategy. National Academies Press, 2007.

  8. Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, et al. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch Toxicol. 2020;94:1–58.

    CAS  Google Scholar 

  9. Schmidt CW. TOX 21: new dimensions of toxicity testing. In: National Institute of Environmental Health Sciences, 2009.

  10. Dix DJ, Houck KA, Martin MT, Richard AM, Setzer RW, Kavlock RJ. The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicological Sci. 2006;95:5–12.

    Google Scholar 

  11. Cohen Hubal EA, Richard AM, Shah I, Gallagher J, Kavlock R, Blancato J, et al. Exposure science and the U.S. EPA National Center for Computational Toxicology. J Exposure Sci Environ Epidemiol. 2010;20:231–6.

    CAS  Google Scholar 

  12. Rager JE, Fry RC. Systems biology and environmental exposures. Network Biology: Theories, Methods and Applications (WJ Zhang, ed) 2013. 81–132.

  13. Kavlock RJ, Bahadori T, Barton-Maclaren TS, Gwinn MR, Rasenberg M, Thomas RS. Accelerating the pace of chemical risk assessment. Chem Res Toxicol. 2018;31:287–90.

    CAS  Google Scholar 

  14. Cohen Hubal EA, Richard A, Aylward L, Edwards S, Gallagher J, Goldsmith M-R, et al. Advancing exposure characterization for chemical evaluation and risk assessment. J Toxicol Environ Health, Part B. 2010;13:299–313.

    CAS  Google Scholar 

  15. Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, et al. The toxicity data landscape for environmental chemicals. Environ Health Perspect. 2009;117:685.

    CAS  Google Scholar 

  16. Egeghy PP, Judson R, Gangwal S, Mosher S, Smith D, Vail J, et al. The exposure data landscape for manufactured chemicals. Sci Total Environ. 2012;414:159–66.

    CAS  Google Scholar 

  17. Sheldon LS, Cohen Hubal EA. Exposure as part of a systems approach for assessing risk. Environ Health Perspect. 2009;117:1181–94.

    Google Scholar 

  18. National Research Council. Risk Assessment in the Federal Government: Managing the Process. In; https://doi.org/10.17226/317. National Academies Press: Washington (DC), 1983.

  19. World Health Organization Inter-Organization Programme for the Sound Management of Chemicals. IPCS risk assessment terminology. World Health Organization, 2004.

  20. Breyer S. Breaking the Vicious Circle: Toward Effective Risk Regulation. Harvard University Press, 2009.

  21. Anastas P, Teichman K, Hubal EC. Ensuring the safety of chemicals. J Exposure Sci Environ Epidemiol. 2010;20:395–6.

    Google Scholar 

  22. Nabholz JV. Environmental hazard and risk assessment under the United States toxic substances control act. Sci total Environ. 1991;109:649–65.

    Google Scholar 

  23. Walker JD, Carlsen L. QSARs for identifying and prioritizing substances with persistence and bioconcentration potential. SAR QSAR Environ Res. 2002;13:713–25.

    CAS  Google Scholar 

  24. Arnot JA, MacKay D, Webster E, Southwood JM. Screening level risk assessment model for chemical fate and effects in the environment. Environ Sci Technol. 2006;40:2316–23.

    CAS  Google Scholar 

  25. Bennett DH, McKone TE, Evans JS, Nazaroff WW, Margni MD, Jolliet O, et al. Defining intake fraction. Environ Sci Technol. 2002;36:207–16.

    Google Scholar 

  26. Jolliet O, Ernstoff AS, Csiszar SA, Fantke P. Defining product intake fraction to quantify and compare exposure to consumer products. Environ Sci Technol. 2015;49:8924–31.

    CAS  Google Scholar 

  27. Wallace LA. Comparison of risks from outdoor and indoor exposure to toxic chemicals. Environ Health Perspect. 1991;95:7–13.

    CAS  Google Scholar 

  28. McCurdy T, Glen G, Smith L, Lakkadi Y. The national exposure research laboratory’s consolidated human activity database. J Exposure Anal Environ Epidemiol. 2000;10:566–78.

    CAS  Google Scholar 

  29. Zartarian V, Glen G, Smith L, Xue J SHEDS-Multimedia model version 3 technical manual. US Environmental Protection Agency 2008.

  30. Robinson JP, Silvers A. Measuring potential exposure to environmental pollutants: time spent with soil and time spent outdoors. J Exposure Sci Environ Epidemiol. 2000;10:341–54.

    CAS  Google Scholar 

  31. Wambaugh JF, Bare JC, Carignan CC, Dionisio KL, Dodson RE, Jolliet O, et al. New approach methodologies for exposure science. Curr Opin in Toxicol. 2019;15:76–92.

  32. Egeghy PP, Vallero DA, Cohen Hubal EA. Exposure-based prioritization of chemicals for risk assessment. Environ Sci policy. 2011;14:950–64.

    CAS  Google Scholar 

  33. Mitchell J, Arnot JA, Jolliet O, Georgopoulos PG, Isukapalli S, Dasgupta S, et al. Comparison of modeling approaches to prioritize chemicals based on estimates of exposure and exposure potential. Sci Total Environ. 2013;458:555–67.

    Google Scholar 

  34. MacLeod M, Scheringer M, McKone TE, Hungerbuhler K. The state of multimedia mass-balance modeling in environmental science and decision-making. Environ Sci Technol. 2010;44:8360–4.

    CAS  Google Scholar 

  35. Huang L, Jolliet O. A parsimonious model for the release of volatile organic compounds (VOCs) encapsulated in products. Atmos Environ. 2016;127:223–35.

    CAS  Google Scholar 

  36. Cowan-Ellsberry CE, McLachlan MS, Arnot JA, MacLeod M, McKone TE, Wania F. Modeling exposure to persistent chemicals in hazard and risk assessment. Integr Environ Assess Manag. 2009;5:662–79.

    CAS  Google Scholar 

  37. Katritzky AR, Lobanov VS, Karelson M. QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem Soc Rev. 1995;24:279–87.

    CAS  Google Scholar 

  38. Mansouri K, Grulke CM, Judson RS, Williams AJ. OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminformatics. 2018;10:10.

    Google Scholar 

  39. Arnot JA, Brown TN, Wania F, Breivik K, McLachlan MS. Prioritizing chemicals and data requirements for screening-level exposure and risk assessment. Environ Health Perspect. 2012;120:1565–70.

    Google Scholar 

  40. Dudzina T, Delmaar CJ, Biesterbos JW, Bakker MI, Bokkers BG, Scheepers PT, et al. The probabilistic aggregate consumer exposure model (PACEM): validation and comparison to a lower-tier assessment for the cyclic siloxane D5. Environ Int. 2015;79:8–16.

    CAS  Google Scholar 

  41. Phillips L, Moya J. The evolution of EPA’s Exposure Factors Handbook and its future as an exposure assessment resource. J Exposure Sci Environ Epidemiol. 2013;23:13–21.

    Google Scholar 

  42. Hall B, Tozer S, Safford B, Coroama M, Steiling W, Leneveu-Duchemin MC, et al. European consumer exposure to cosmetic products, a framework for conducting population exposure assessments. Food Chem Toxicol. 2007;45:2097–108.

    CAS  Google Scholar 

  43. Comiskey D, Api A, Barrett C, Ellis G, McNamara C, O’Mahony C, et al. Integrating habits and practices data for soaps, cosmetics and air care products into an existing aggregate exposure model. Regulatory Toxicol Pharmacol. 2017;88:144–56.

    CAS  Google Scholar 

  44. Safford B, Api A, Barratt C, Comiskey D, Ellis G, McNamara C, et al. Application of the expanded Creme RIFM consumer exposure model to fragrance ingredients in cosmetic, personal care and air care products. Regulatory Toxicol Pharmacol. 2017;86:148–56.

    CAS  Google Scholar 

  45. Brandon N, Dionisio KL, Isaacs K, Tornero-Velez R, Kapraun D, Setzer RW, et al. Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence. J Exposure Sci Environ Epidemiol. 2020;30:184–93.

    Google Scholar 

  46. Brandon N, Price PS. Calibrating an agent-based model of longitudinal human activity patterns using the Consolidated Human Activity Database. J Exposure Sci Environ Epidemiol. 2020;30:194–204.

    Google Scholar 

  47. Shin HM, Ernstoff A, Arnot JA, Wetmore BA, Csiszar SA, Fantke P, et al. Risk-based high-throughput chemical screening and prioritization using exposure models and in vitro bioactivity assays. Environ Sci Technol. 2015;49:6760–71.

    CAS  Google Scholar 

  48. Oreskes N. Evaluation (not validation) of quantitative models. Environ Health Perspect. 1998;106:1453.

    Google Scholar 

  49. McKone TE, Castorina R, Harnly ME, Kuwabara Y, Eskenazi B, Bradman A. Merging models and biomonitoring data to characterize sources and pathways of human exposure to organophosphorus pesticides in the Salinas Valley of California. Environ Sci Technol. 2007;41:3233–40.

    CAS  Google Scholar 

  50. Xue J, Zartarian V, Tornero-Velez R, Tulve NS. EPA’s SHEDS-multimedia model: Children’s cumulative pyrethroid exposure estimates and evaluation against NHANES biomarker data. Environ Int. 2014;73:304–11.

    CAS  Google Scholar 

  51. U.S. Centers for Disease Control and Prevention. NHANES Fourth national report on human exposure to environmental chemicals. In: Department of Health and Human Services Centers for Disease Control and Prevention Atlanta, Georgia, 2009.

  52. Wambaugh JF, Setzer RW, Reif DM, Gangwal S, Mitchell-Blackwood J, Arnot JA, et al. High-throughput models for exposure-based chemical prioritization in the ExpoCast project. Environ Sci Technol. 2013;47:8479–88.

    CAS  Google Scholar 

  53. Wambaugh JF, Wang A, Dionisio KL, Frame A, Egeghy P, Judson R, et al. High throughput heuristics for prioritizing human exposure to environmental chemicals. Environ Sci Technol. 2014;48:12760–7.

    CAS  Google Scholar 

  54. Ring CL, Arnot JA, Bennett DH, Egeghy PP, Fantke P, Huang L, et al. Consensus modeling of median chemical intake for the US population based on predictions of exposure pathways. Environ Sci Technol. 2018;53:719–32.

    Google Scholar 

  55. Dionisio KL, Frame AM, Goldsmith M-R, Wambaugh JF, Liddell A, Cathey T, et al. Exploring consumer exposure pathways and patterns of use for chemicals in the environment. Toxicol Rep. 2015;2:228–37.

    CAS  Google Scholar 

  56. Isaacs KK, Glen WG, Egeghy P, Goldsmith M-R, Smith L, Vallero D, et al. SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources. Environ Sci Technol. 2014;48:12750–9.

    CAS  Google Scholar 

  57. Li L, Westgate JN, Hughes L, Zhang X, Givehchi B, Toose L et al. A model for risk-based screening and prioritization of human exposure to chemicals from near-field sources. Environmental Science & Technology 2018.

  58. Fantke P, Ernstoff AS, Huang L, Csiszar SA, Jolliet O. Coupled near-field and far-field exposure assessment framework for chemicals in consumer products. Environ Int. 2016;94:508–18.

    CAS  Google Scholar 

  59. Silver N. The signal and the noise: why so many predictions fail--but some don’t. Penguin, 2012.

  60. Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith M-R, Wambaugh JF, et al. Computational exposure science: an emerging discipline to support 21st-century risk assessment. Environ Health Perspect. 2016;124:697.

    CAS  Google Scholar 

  61. Weaver S, Gleeson MP. The importance of the domain of applicability in QSAR modeling. J Mol Graph Model. 2008;26:1315–26.

    CAS  Google Scholar 

  62. Stanfield Z, Setzer RW, Hull V, Sayre RR, Isaacs KK, Wambaugh JF Bayesian inference of chemical exposures from NHANES urine biomonitoring data. Journal of Exposure Science & Environmental Epidemiology; https://doi.org/10.1038/s41370-022-00459-0 2022.

  63. Tan Y-M, Liao KH, Conolly RB, Blount BC, Mason AM, Clewell HJ. Use of a physiologically based pharmacokinetic model to identify exposures consistent with human biomonitoring data for chloroform. J Toxicol Environ Health, Part A. 2006;69:1727–56.

    CAS  Google Scholar 

  64. Lakind JS, Naiman DQ. Bisphenol A (BPA) daily intakes in the United States: estimates from the 2003–2004 NHANES urinary BPA data. J Exposure Sci Environ Epidemiol. 2008;18:608.

    CAS  Google Scholar 

  65. Mage DT, Allen RH, Gondy G, Smith W, Barr DB, Needham LL. Estimating pesticide dose from urinary pesticide concentration data by creatinine correction in the Third National Health and Nutrition Examination Survey (NHANES-III). J Exposure Sci Environ Epidemiol. 2004;14:457.

    CAS  Google Scholar 

  66. Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: a tutorial. Stat Sci. 1999;14:382–401.

  67. Biryol D, Nicolas CI, Wambaugh J, Phillips K, Isaacs K. High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization. Environ Int. 2017;108:185–94.

    CAS  Google Scholar 

  68. Ernstoff AS, Fantke P, Huang L, Jolliet O. High-throughput migration modelling for estimating exposure to chemicals in food packaging in screening and prioritization tools. Food Chem Toxicol. 2017;109:428–38.

    CAS  Google Scholar 

  69. Csiszar SA, Ernstoff AS, Fantke P, Meyer DE, Jolliet O. High-throughput exposure modeling to support prioritization of chemicals in personal care products. Chemosphere. 2016;163:490–8.

    CAS  Google Scholar 

  70. Blackburn K, Stickney JA, Carlson-Lynch HL, McGinnis PM, Chappell L, Felter SP. Application of the threshold of toxicological concern approach to ingredients in personal and household care products. Regulatory Toxicol Pharmacol. 2005;43:249–59.

    CAS  Google Scholar 

  71. Scruggs CE. Reducing hazardous chemicals in consumer products: proactive company strategies. J Clean Prod. 2013;44:105–14.

    CAS  Google Scholar 

  72. Goldsmith MR, Grulke CM, Brooks RD, Transue TR, Tan YM, Frame A, et al. Development of a consumer product ingredient database for chemical exposure screening and prioritization. Food Chem Toxicol. 2014;65:269–79.

    CAS  Google Scholar 

  73. Dionisio K, Phillips K, Price P, Grulke C, Williams A, Biryol D et al. The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products. Scientific Data 2018.

  74. Isaacs KK, Dionisio K, Phillips K, Bevington C, Egeghy P, Price PS. Establishing a system of consumer product use categories to support rapid modeling of human exposure. J Exposure Sci Environ Epidemiol. 2020;30:171–83.

    CAS  Google Scholar 

  75. Jolliet O, Huang L, Hou P, Fantke P. High throughput risk and impact screening of chemicals in consumer products. Risk Anal. 2021;41:627–44.

    Google Scholar 

  76. Li L, Sangion A, Wania F, Armitage JM, Toose L, Hughes L, et al. Development and evaluation of a holistic and mechanistic modeling framework for chemical emissions, fate, exposure, and risk. Environ Health Perspect. 2021;129:127006.

    Google Scholar 

  77. U.S. Environmental Protection Agency. SOL-NC-13-00017: “Exposure Screening Tools for Accelerated Chemical Prioritization (ExpoCast”. In. FedBizOpps, 2013.

  78. Judson RS, Houck KA, Kavlock RJ, Knudsen TB, Martin MT, Mortensen HM, et al. In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project. Environ health Perspect. 2010;118:485–92.

    CAS  Google Scholar 

  79. Park YH, Lee K, Soltow QA, Strobel FH, Brigham KL, Parker RE, et al. High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring. Toxicology. 2012;295:47–55.

    CAS  Google Scholar 

  80. Kerns EH. High throughput physicochemical profiling for drug discovery. J Pharm Sci. 2001;90:1838–58.

    CAS  Google Scholar 

  81. Sobus JR, Wambaugh JF, Isaacs KK, Williams AJ, McEachran AD, Richard AM, et al. Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA. J Exposure Sci Environ Epidemiol. 2018;28:411.

    CAS  Google Scholar 

  82. Phillips K, Yau AY, Favela KA, Isaacs KK, McEachran A, Grulke CM, et al. Suspect screening analysis of chemicals in consumer products. Environ Sci Technol. 2018;52:3125–35.

    CAS  Google Scholar 

  83. Lowe CN, Phillips KA, Favela KA, Yau AY, Wambaugh JF, Sobus JR, et al. Chemical characterization of recycled consumer products using suspect screening analysis. Environ Sci Technol. 2021;55:11375–87.

    CAS  Google Scholar 

  84. Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol, Biomark Prev. 2005;14:1847–50.

    CAS  Google Scholar 

  85. Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A. The blood exposome and its role in discovering causes of disease. Environ Health Perspect. 2014;122:769.

    Google Scholar 

  86. Bessonneau V, Pawliszyn J, Rappaport SM. The Saliva Exposome for Monitoring of Individuals’ Health Trajectories. Environ Health Perspect. 2017;125:077014.

    Google Scholar 

  87. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, et al. The human urine metabolome. PloS one. 2013;8:e73076.

    CAS  Google Scholar 

  88. Andra SS, Austin C, Arora M. The tooth exposome in children’s health research. Curr Opin Pediatrics. 2016;28:221–7.

    Google Scholar 

  89. Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM, Grulke CM, et al. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ Int. 2016;88:269–80.

    CAS  Google Scholar 

  90. Newton SR, McMahen RL, Sobus JR, Mansouri K, Williams AJ, McEachran AD, et al. Suspect screening and non-targeted analysis of drinking water using point-of-use filters. Environ Pollut. 2018;234:297–306.

    CAS  Google Scholar 

  91. Williams AJ, Grulke CM, Edwards J, McEachran AD, Mansouri K, Baker NC, et al. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J Cheminformatics. 2017;9:61.

    Google Scholar 

  92. McEachran AD, Sobus JR, Williams AJ. Identifying known unknowns using the US EPA’s CompTox Chemistry Dashboard. Anal Bioanal Chem. 2017;409:1729–35.

    CAS  Google Scholar 

  93. Johnson P, Favela K. Chemicals of Concern in Personal Care Products Used by Women of Color. J of Exposure Sci & Environ Epidemiol. 2022;32.

  94. Hsiao Y-C, Liu C-W, Robinette C, Knight N, Lu K, Rebuli ME Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics. Journal of Exposure Science & Environmental Epidemiology 2022; https://doi.org/10.1038/s41370-022-00448-3.

  95. Houston JB, Galetin A. Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr Drug Metab. 2008;9:940–51.

    CAS  Google Scholar 

  96. Wang Y-H. 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 Disposition. 2010;38:1094–104.

    CAS  Google Scholar 

  97. Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, et al. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicological Sci. 2010;117:348–58.

    CAS  Google Scholar 

  98. Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, et al. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicological Sci. 2012;125:157–74.

    CAS  Google Scholar 

  99. Wetmore BA, Allen B, Clewell HJ 3rd, Parker T, Wambaugh JF, Almond LM, et al. Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing. Toxicological Sci. 2014;142:210–24.

    CAS  Google Scholar 

  100. Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, et al. Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing. Toxicological Sci. 2015;148:121–36.

    CAS  Google Scholar 

  101. Wambaugh JF, Wetmore BA, Ring CL, Nicolas CI, Pearce R, Honda G, et al. Assessing toxicokinetic uncertainty and variability in risk prioritization. Toxicological Sci. 2019;172:235–51.

  102. Wambaugh JF, Wetmore BA, Pearce R, Strope C, Goldsmith R, Sluka JP, et al. Toxicokinetic triage for environmental chemicals. Toxicological Sci. 2015;147:55–67.

    CAS  Google Scholar 

  103. Wambaugh JF, Hughes MF, Ring CL, MacMillan DK, Ford J, Fennell TR, et al. Evaluating in vitro-in vivo extrapolation of toxicokinetics. Toxicological Sci. 2018;163:152–69.

    CAS  Google Scholar 

  104. Linakis MW, Sayre RR, Pearce RG, Sfeir MA, Sipes NS, Pangburn HA, et al. Development and evaluation of a high throughput inhalation model for organic chemicals. J of Exp Sci & Environ Epidemiol. 2020;30:866–77.

  105. Breen M, Ring CL, Kreutz A, Goldsmith M-R, Wambaugh JF. High-throughput PBTK models for in vitro to in vivo extrapolation. Exp Opin on Drug Metab & Toxicol. 2021;17:903–21.

  106. Wetmore BA. Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment. Toxicology. 2015;332:94–101.

    CAS  Google Scholar 

  107. Bell SM, Chang X, Wambaugh JF, Allen DG, Bartels M, Brouwer KLR, et al. In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicol Vitr. 2018;47:213–27.

    CAS  Google Scholar 

  108. Chang X, Tan Y-M, Allen DG, Bell S, Brown PC, Browning L, et al. IVIVE: facilitating the use of in vitro toxicity data in risk assessment and decision making. Toxics. 2022;10:232.

    Google Scholar 

  109. Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, et al. Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions. PLOS ONE. 2019;14:e0217564.

    CAS  Google Scholar 

  110. Ring C, Sipes NS, Hsieh J-H, Carberry C, Koval LE, Klaren WD, et al. Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics. Comput Toxicol. 2021;18:100166.

    CAS  Google Scholar 

  111. Klaren WD, Ring C, Harris MA, Thompson CM, Borghoff S, Sipes NS, et al. Identifying attributes that influence in vitro-to-in vivo concordance by comparing in vitro tox21 bioactivity versus in vivo drugmatrix transcriptomic responses across 130 chemicals. Toxicological Sci: Off J Soc Toxicol. 2019;167:157–71.

    CAS  Google Scholar 

  112. Paul Friedman K, Gagne M, Loo L-H, Karamertzanis P, Netzeva T, Sobanski T, et al. Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Toxicological Sci. 2020;173:202–25.

    Google Scholar 

  113. Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF. Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability. Environ Int. 2017;106:105–18.

    CAS  Google Scholar 

  114. Koman PD, Singla V, Lam J, Woodruff TJ. Population susceptibility: a vital consideration in chemical risk evaluation under the Lautenberg Toxic Substances Control Act. PLoS Biol. 2019;17:e3000372.

    CAS  Google Scholar 

  115. Pearce RG, Setzer RW, Strope CL, Sipes NS, Wambaugh JF. Httk: R package for high-throughput toxicokinetics. J Stat Softw. 2017;79:1–26.

    Google Scholar 

  116. Breen M, Wambaugh JF, Bernstein A, Sfeir M, Ring CL. Simulating Toxicokinetic Variability to Identify Susceptible and Highly Exposed Populations. J of Exp Sci & Environ Epidemiol. 2022;32.

  117. Cohen Hubal EA. Biologically relevant exposure science for 21st century toxicity testing. Toxicological Sci. 2009;111:226–32.

    Google Scholar 

  118. Thomas RS, Bahadori T, Buckley TJ, Cowden J, Deisenroth C, Dionisio KL, et al. The next generation blueprint of computational toxicology at the US Environmental Protection Agency. Toxicol Sci. 2019;169:317–32.

  119. Judson RS, Kavlock RJ, Setzer RW, Cohen Hubal EA, Martin MT, Knudsen TB, et al. Estimating toxicity-related biological pathway altering doses for high-throughput chemical risk assessment. Chem Res Toxicol. 2011;24:451–62.

    CAS  Google Scholar 

  120. Heinemeyer G. Concepts of exposure analysis for consumer risk assessment. Exp Toxicol Pathol. 2008;60:207–12.

    Google Scholar 

  121. Sayre RR, Setzer RW, Serre ML, Wambaugh JF Determining screening-level ecological risk prioritizations for hundreds of organic chemicals in United States surface waters. Journal of Exposure Science & Environmental Epidemiology 2022.

  122. Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA, Hsieh J-H, et al. An intuitive approach for predicting potential human health risk with the Tox21 10k library. Environ Sci Technol. 2017;51:10786–96.

    CAS  Google Scholar 

  123. Fantke P, Chiu WA, Aylward L, Judson R, Huang L, Jang S, et al. Exposure and toxicity characterization of chemical emissions and chemicals in products: global recommendations and implementation in USEtox. The. Int J Life Cycle Assess. 2021;26:899–915.

    CAS  Google Scholar 

  124. Patlewicz G, Wambaugh JF, Felter SP, Simon TW, Becker RA. Utilizing threshold of toxicological concern (TTC) with high throughput exposure predictions (HTE) as a risk-based prioritization approach for thousands of chemicals. Computational. Toxicology. 2018;7:58–67.

    Google Scholar 

  125. National Academies of Sciences E, and Medicine,. Using 21st Century Science to Improve Risk-Related Evaluations. The National Academies Press: Washington, DC, 2017.

  126. Schmidt CW. TSCA 2.0: A new era in chemical risk management. In: National Institute of Environmental Health Sciences, 2016.

  127. U.S. Environmental Protection Agency. U.S. EPA. A Proof-of-Concept Case Study Integrating Publicly Available Information to Screen Candidates for Chemical Prioritization under TSCA. In. Washington, DC, 2021.

  128. Health Canada. Bioactivity Exposure Ratio: Application in Priority Setting and Risk Assessment. In. Ottawa, ON, Canada, 2021.

  129. Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature. 2015;521:452–9.

    CAS  Google Scholar 

  130. Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.

    Google Scholar 

  131. Liaw A, Wiener M. Classification and regression by randomForest. R N. 2002;2:18–22.

    Google Scholar 

  132. Phillips KA, Wambaugh JF, Grulke CM, Dionisio KL, Isaacs KK. High-throughput screening of chemicals as functional substitutes using structure-based classification models. Green Chem. 2017;19:1063–74.

    CAS  Google Scholar 

  133. Isaacs KK, Goldsmith M-R, Egeghy P, Phillips K, Brooks R, Hong T, et al. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicol Rep. 2016;3:723–32.

    CAS  Google Scholar 

  134. McEachran AD, Mansouri K, Newton SR, Beverly BE, Sobus JR, Williams AJ. A comparison of three liquid chromatography (LC) retention time prediction models. Talanta. 2018;182:371–9.

    CAS  Google Scholar 

  135. Lowe CN, Isaacs KK, McEachran A, Grulke CM, Sobus JR, Ulrich EM, et al. Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis. Anal Bioanal Chem. 2021;413:7495–508.

    CAS  Google Scholar 

  136. Abrahamsson D. Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS). J of Exp Sci & Environ Epidemiol. 2022;32.

  137. Li Estimation of fine particulate matter in an arid area from visibility based on machine learning. J of Exp Sci & Environ Epidemiol. 2022.

  138. Borgelt C. Frequent item set mining. Wiley Interdiscip Rev: Data Min Knowl Discov. 2012;2:437–56.

    Google Scholar 

  139. Kapraun DF, Wambaugh JF, Ring CL, Tornero-Velez R, Setzer RW. A method for identifying prevalent chemical combinations in the US population. Environ Health Perspect. 2017;125:087017.

    Google Scholar 

  140. Yun YE, Tornero-Velez R, Purucker ST, Chang DT, Edginton AN. Evaluation of quantitative structure property relationship algorithms for predicting plasma protein binding in humans. Comput Toxicol. 2020;17:100142.

  141. Stanfield Z, Addington CK, Dionisio KL, Lyons D, Tornero-Velez R, Phillips KA, et al. Mining of consumer product ingredient and purchasing data to identify potential chemical coexposures. Environ Health Perspect. 2021;129:067006.

    CAS  Google Scholar 

  142. Zota AR, Aschengrau A, Rudel RA, Brody JG. Self-reported chemicals exposure, beliefs about disease causation, and risk of breast cancer in the Cape Cod Breast Cancer and Environment Study: a case-control study. Environ Health. 2010;9:40.

    Google Scholar 

  143. Bremmer H, Prud’Homme de Lodder L, Van Engelen J Cosmetics Fact Sheet. To assess the risks for the consumer. Updated version for ConsExpo 4. 2006.

  144. Grulke CM, Williams AJ, Thillanadarajah I, Richard AM. EPA’s DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research. Computational. Toxicology. 2019;12:100096.

    Google Scholar 

  145. Abdallah MA-E, Harrad S. Dermal contact with furniture fabrics is a significant pathway of human exposure to brominated flame retardants. Environ Int. 2018;118:26–33.

    CAS  Google Scholar 

  146. Koval LE, Dionisio KL, Friedman KP, Isaacs KK, Rager JE. Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics. Journal of Exposure Science & Environmental Epidemiology; https://doi.org/10.1038/s41370-022-00451-8 2022.

  147. Kavlock R, Dix D. Computational toxicology as implemented by the U.S. EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk. J Toxicol Environ Health, Part B. 2010;13:197–217.

    CAS  Google Scholar 

  148. Isaacs KK. The Chemical Landscape of High-Throughput New Approacj Methodologies for Exposure. J of Exp Sci & Environ Epidemiol. 2022;32.

  149. Edwards SW, Preston RJ. Systems biology and mode of action based risk assessment. Toxicological Sci. 2008;106:312–8.

    CAS  Google Scholar 

  150. Bell SM, Edwards SW. Identification and prioritization of relationships between environmental stressors and adverse human health impacts. Environ Health Perspect. 2015;123:1193.

    Google Scholar 

  151. Patel CJ, Pho N, McDuffie M, Easton-Marks J, Kothari C, Kohane IS, et al. A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data. 2016;3:160096.

    CAS  Google Scholar 

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Acknowledgements

The research described in this manuscript has been reviewed by the Center for Computational Toxicology and Exposure, in the Office of Research and Development of the U.S. EPA and approved for publication. Approval does not signify those contents necessarily reflect the views and policies of the agency, nor does the mention of trade names or commercial products constitute endorsement or recommendation for use. The authors would like to especially thank Dr. Elaine Cohen Hubal, Dr. Kristin Isaacs, and Dr. Peter Egeghy for their leadership, vision, and tremendous impact on the development and success of the ExpoCast Project, as well as acknowledge the contributions of dozens of researchers, funders, and administrative staff within the U.S. EPA and beyond. It has been a great honor to work with all of you. We appreciate Dr. Kristin Isaacs, Dr. Dan Vallero, Dr. Peter Egeghy, and Dr. Michael Devito for providing U.S. EPA internal technical and clearance reviews of this manuscript. This manuscript was supported by the Intramural Research Program of the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, and the National Institutes of Health (NIH) from the National Institute of Environmental Health Sciences (P42ES031007). Additional support was provided by the Institute for Environmental Health Solutions at the Gillings School of Global Public Health.

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  1. Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, USA

    John F. Wambaugh

  2. Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    John F. Wambaugh & Julia E. Rager

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JFW and JER jointly organized, drafted, and revised the manuscript.

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Wambaugh, J.F., Rager, J.E. Exposure forecasting – ExpoCast – for data-poor chemicals in commerce and the environment. J Expo Sci Environ Epidemiol 32, 783–793 (2022). https://doi.org/10.1038/s41370-022-00492-z

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