vvEPA United States Environmental Protection Agency The Chemical Landscape of New Approach Methodologies for Exposure Kristin Center for Computational Office of Research and Dev Environmental Protection Agency APCRA Public Webinar March ------- SEPA United States Environmental Protection Agency Disclaimer The views expressed in this presentation are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA 2 of 24 Office of Research and Development ------- Exposure in the APCRA Initiative United States Environmental Protection Agency Identify available New Approach Methodologies (NAMs) for exposure-relevant domains Examine the landscape of exposure data (both traditional and NAMs) for an inventory of chemicals relevant to APCRA partners ¦ Identify key information or activities that would enable or enhance fit-for-purpose exposure estimates, predictions, or assessments and provide recommendations ¦ Provide exposure metrics to support the APCRA inventory and hazard-focused case study activities 3 of 24 Office of Research and Development ------- SEPA United States Environmental Protection Agency US EPA Office of Research and Development Center for Computational Toxicology and Exposure Kathie Dionisio Annette Guiseppi-Elie Kristin Isaacs Katherine Phillips Jon Sobus Elin Ulrich John Wambaugh Barbara Wetmore 4 of 24 Office of Research and Development Contributors Health Canada European Chemicals Agency Angelika Zidek Andreas Ahrens ------- SEPA United States Environmental Protection Agency 5 of 24 Office of Research and Development Risk is Multifaceted Regulatory bodies are tasked with evaluating risks associated with 1000s of chemicals in commerce ¦ For example, as of 2019 there were ~40,000 chemicals on EPA's TSCA Inventory Evaluating chemicals for risk to humans or the environment requires information on hazard and exposure potential Exposure potential quantifies the degree of contact between a chemical and a receptor , ¦ Toxicokinetic information is required to bridge hazard \ and exposure (what real-world exposure is required to produce an internal concentration consistent with a potential hazard?) ------- 4>EPA United States Environmental Protection Agency EPA's ExpoCast Project 6 of 24 Office of Research and Development Risk is Multifaceted mg/kg BW/day Potential Hazard from in vitro with Reverse Toxicokinetics Potential Exposure Lower Medium Higher Risk Risk Risk ------- 4>EPA United States Environmental Protection Agency Forecasting Exposure is a Systems Problem Forward Models n CHEMICAL USE and I ^ Consumer Products and Durable Goods Chemical Manufacturing Evaluation EXPOSURE ENVIRONMENTAL SURVEILLANCE and BIOMONITORING TOXICOKINETICS Critical Exposure-Relevant Domains Chemical use and release. Provides critical information for identifying chemical sources, exposure pathways, and relevant predictive models for a given chemical. Exposure Media Outdoor Air, Soil, Surface Ground Water ECOLOGICAL v Human Biomarkers of Exposure RECEPTORS n^\^vv Sampling Ecological Flora and Fauna Biomarkers of Exposure Media occurrence, environmental surveillance, andbiomonitoring. Provides exposure data for evaluating predictive models. Exposure estimates. Predictions of chemical intake in mg/kg/day that can be compared with hazard information to inform risk. Toxicokinetics. Provides real-world exposure context to in vitro high-throughput screening data and biological receptor monitoring information. 7 of 24 Office of Research and Development ------- 4>EPA United States Environmental Protection Agency Eight Classes of NAMs for Exposure Chemical descriptors that provide information on chemicals in an exposure context (e.g., how chemicals are used) Machine-learning approaches that use these descriptors to fill gaps in existing data High-throughput exposure models for various pathways High-throughput measurements to fill gaps in monitoring data High-throughput approaches for measuring and predicting chemical toxicokinetics New evaluation frameworks for integrating models and monitoring to provide consensus exposure predictions All these pieces together provide the tools for high- th roughput chemical prioritization JjyL ELSEVIER Current Opinion in Toxicology Volume 15, June 2019, Pages 76-92 New approach methodologies for exposure science John F. Wambaugh 1 A E3 Jane C. Bare 2, Courtney C. Carignan Kathie L. Dionisio 4 Robin E. Dodson 5, OlivierJollfet 6, Xiaoyu Liu David E. Meyer 2, Seth R. Newton 4, Katherine A. Phillips 4, Paul S. Price 4 Caroline L. Ring s, Hyeong-Moo Shin 9, Jon R. So'bus 4, Tamara Tal 10, El in M. UI rich 4, Daniel A. Vallero 4, Barbara A. Wetmore 4 Kristin K. Isaacs 4 0 Show more https://doi.Org/10.1016/j.cotox.2019.07.001 Get rights and content 8 of 24 Office of Research and Development ------- g pp/\ Characterizing the Chemical Landscape for United States Environmental Protection Agency Exposure NAMs ¦ "APCRA inventory" - case study chemical list • 6621 chemical substances compiled by APCRA partners for potential use in retrospective or prospective case studies • Compiled from regulatory lists from EPA, Health Canada, ECHA, EFSA, NICNAS ¦ Investigated the coverage of this inventory ¦ "Traditional" exposure data • Regulatory reporting • Targeted monitoring data • Regulatory exposure assessments • In-vivotoxicokinetic information ¦ Exposure NAMs across all four domains Office of Research and Development ------- 4>EPA United States Environmental Protection Agency Traditional and NAM Exposure Datasets NAM dataset Voluntary Regulatory or agency data reporting of chemical use vvEPA New quantitative and qualitative chemical use descriptors from EPA's Chemicals and Products Database (CPDat, Dionisio et al., 2018) Machine learning models for chemical function (Phillips et al. 2017) Chemical Environments r Use Surveillance and and Release Biomonitoring Toxicokinetics Exposure J Estimates / IVIVE 10 of 24 Office of Research and Development ------- 4>EPA United States Environmental Protection Agency Traditional and NAM Exposure Datasets NAM dataset Voluntary Regulatory or agency data reporting of chemical use vvEPAi 1USGS science for a changing world New quantitative and qualitative chemical use descriptors from EPA's Chemicals and Products Database (CPDat, Dionisio et al., 2018) Machine learning models for chemical function (Phillips et al. 2017) Traditional (targeted) monitoring data for various environmental media from publicly available monitoring databases Non-Targeted analysis studies for various environmental media from EPA and the EU (Newton et al. 2018, Rager et al. 2016, Sjerps et al. 2016, Phillips et al. 2018) Machine learning models for media occurrence Office of Research and Development Toxicokinetics X IVIVE Exposure Estimates ------- 4>EPA United States Environmental Protection Agency Traditional and NAM Exposure Datasets NAM dataset 1USGS science for a changing world Traditional (targeted) monitoring data for various environmental media from publicly available monitoring databases Non-Targeted analysis studies for various environmental media from EPA and the EU (Newton et al. 2018, Rager et al. 2016, Sjerps et al, 2016, Phillips et al. 2018) Machine learning models for media occurrence Cumulative Estimated Daily Intakes Chemicals Management Plan Environmental and Consumer Assessments Publicly Available Traditional Assessments from Regulatory Bodies ena- •to"™™*"*-'* Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways Caroline L Ring/'8'00 Jon A. Amot,"1" Deborah H. Bennett,1' L Peter P. Egeghy,' Peter Fantkc,®® 1 Lei Huang,* Kristin K. Isaacs, Olivier Jolliet,*'- Katherine A. Phillips,''" Paul S. Price, Hyeong-Moo Shin,'1 - John N. Wcstgate,1 R. Woodrow Sctzer,' and John F. Wambaugh* '® ere yj IS A Danmarks l| ij m l#l Tekniske » ~ Universitet High-Throughput Models for Various Pathways and Consensus Predictions from a Collaborative Modeling Study (Ring et al., 2019) 12 of 24 Office of Research and Development ------- 4>EPA United States Environmental Protection Agency Traditional and NAM Exposure Datasets NAM dataset Voluntary Regulatory or agency data reporting of chemical use vvEPAi 1USGS science for a changing world New quantitative and qualitative chemical use descriptors from EPA's Chemicals and Products Database (CPDat, Dionisio et al., 2018) Machine learning models for chemical function (Phillips et al. 2017) In-silico machine learning models for protein binding and clearance (Sipes et al, 2017, Ingle et al. 2018) In-vitro protein binding and clearance (Wetmore et al. 2015, Pearce et al. 2017, Wambaugh et al 2019a.) Traditional (targeted) monitoring data for various environmental media from publicly available monitoring databases Non-Targeted analysis studies for various environmental media from EPA and the EU (Newton et al. 2018, Rager et al. 2016, Sjerps et al. 2016, Phillips et al. 2018) Machine learning models for media occurrence In-vivo toxicokinetic parameters collected from the literature (Sayre et al., 2019) Cumulative Estimated Daily Intakes Chemicals Management Plan Environmental and Consumer Assessments Publicly Available Traditional Assessments from Regulatory Bodies High-Throughput Models for Various Pathways and Consensus Predictions from a Collaborative Modeling Study (Ring et al., 2019) ///// * -* /// 13 of 24 Office of Research and Development ------- oEPA United States Environmental Protection Agency Traditional Release Reporting Information Traditional Use Reporting Information Machine-Learning QSUR Models for Function Chemical Use and Release Chemical Use Descriptors Developed Using Informatics Approaches APCRA Inventory 6621 Inventory Chemicals iC NAM Office of Research and Development The number of chemicals for which release data are available is still limited ------- oEPA Chemical Use and Release United States Environmental Protection Agency Traditional Release Reporting Information Traditional Use Reporting Information Machine-Learning QSUR Models for Function Chemical Use Descriptors Developed Using Informatics Approaches APCRA Inventory ML Models for function allow for extrapolation to data poor chemicals iC NAM Office of Research and Development 6621 Inventory Chemicals The number of chemicals for which release data are available is still limited ------- 4>EPA United States Environmental Protection Agency Media Occurrence, Environmental Surveillance, and Biomonitoring Traditional Targeted Monitoring Data Non-Targeted Studies in _J Several Media jr=J Positive Prediction of Occurrence in Different. Media from Machine Learning Models J|i bi^ii ¦ ¦ i In ¦¦. mi I III I II ? V !¦ I'.lJ J r~jt'i mi ii i ill ii mil il ¦ mi APCRA Inventory llll I II mini 1 ¦¦ Uir.'i il i ¦ i ¦ n jj 11 ii ¦ i i 11 ^ 11 in in i1 r i . .ii vr . . 6621 Inventory Chemicals 16 of 24 ¦ Traditional monitoring very limited Office of Research and Development ------- 4>EPA United States Environmental Protection Agency Media Occurrence, Environmental Surveillance, and Biomonitoring Traditional Targeted Monitoring Data Non-Targeted Studies in _J Several Media *¦— Positive Prediction of Occurrence in Different. Media from Machine Learning Models 111 ^1^11 ¦ y r ii ii •j'ldffr'1 ,M i—tt'i nli n i iii ii inn i APCRA Inventory A limited number of non- targeted studies in media have provided data for many additional chemicals 6621 Inventory Chemicals 17 of 24 ¦ Traditional monitoring very limited Office of Research and Development ------- oEPA Exposure Predictions United States Environmental Protection Agency r— Traditional Assessments HT Exposure Models for Pathways (Ring et al. 2019) Consensus Predictions -t (Ring et a I. 2019) Positive Prediction for Various Exposure Pathways (Ring et al, 2019) APCRA Inventory •k NAM Office of Research and Development 18 of 24 6621 Inventory Chemicals High-throughput exposure models covering different exposure pathway classes have generated exposure estimates for large numbers of chemicals compared to traditional assessments. ------- 4>EPA Exposure Predictions United States Environmental Protection Agency Traditional Assessments HT Exposure Models for Pathways (Ring et al. 2019) Consensus Predictions -t (Ring et a I. 2019) Positive Prediction for Various Exposure Pathways (Ring et al, 2019) APCRA Inventory •k NAM Office of Research and Development 6621 Inventory Chemicals High-throughput exposure models covering different exposure pathway classes have generated exposure estimates for large numbers of chemicals compared to traditional assessments. ------- SEPA Toxicokinetics United States Environmental Protection Agency In Vivo TK Data High-Throughput In Vitro TK Data In Silico (QSAR) TK Parameters Tox21 ToxCost APCRA Inventory 6621 Inventory Chemicals •kNAM ^^9 Office of Research and Development 20 of 24 High throughput in vitro measurement of toxicokinetics has expanded the quantity and domain of chemicals with data, allowing for the development or refinement of in silico models ------- oEPA Toxicokinetics United States Environmental Protection Agency In Vivo TK Data High-Throughput In Vitro TK Data In Silico (QSAR) TK Parameters Tox21 ToxCost APCRA Inventory In silico approaches have expanded the availability of HTTK parameters to nearly all chemicals tested for in vitro bioactivity (96% of Tox21 and 89% ofToxCast) allowing for in vitro to in vivo extrapolation of 6621 Inventory Chemicals High throughput in vitro measurement of toxicokinetics has expanded the quantity and domain of chemicals with data, allowing for the development or ic NAM refinement of in silico models 21 of 24 Office of Research and Development ------- SEPA United States Environmental Protection Agency Summary In all exposure-relevant domains, high-throughput NAMs have substantially increased the number of chemicals for which data are available and improved coverage of chemical inventories. Methods for estimating chemical releases (quantitative estimates of emission into different environmental compartments) are needed; predictions for releases can reduce uncertainty in HT exposure models that currently rely on production volume as surrogates for emission rates. Methods should be developed for addressing mixtures or UVCBs. Approaches are needed for identifying representative compositions or structures for multicomponent substances, and for making use of this information in in silico modeling (i.e., QSAR) frameworks. Measurement NAMs (i.e., non-targeted approaches) have the potential to substantially increase the scope of evaluation datasets for predictive exposure models. Continuing to develop and refine NAMs for exposure and toxicokinetic domains will improve the quality of and expand the scope of risk-based metrics available for chemical prioritization. 22 of 24 Office of Research and Development ------- g pp/\ Ongoing Exposure NAM Evaluation Activities United States Environmental Protection Agency Will aid in assessing fit-for-use of exposure NAMs in various regulatory contexts (classification and labelling, prioritization, first-tier versus full assessments) Comparison of Quantitative Use Relationship (QSUR) models for chemical function with industry reported data • EPA's Chemical Data Reporting for Industrial Uses (Public) • ECHA Plastics Additives Initiative (PLASI) • Health Canada Chemicals Management Plan Information Gathering Comparison of traditional exposure assessments (Health Canada Chemicals Management Plan) to high-throughput model predictions • Consumer Assessments • Environmental media (i.e., ambient/far-field) 23 of 24 Office of Research and Development ------- SEPA United States Environmental Protection Agency References 1.Dionisio KL, Phillips K, Price PS, Grulke CM, Williams A, Biryol D, Hong T, Isaacs KK. The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products. Sci Data. 2018 Jul 10;5:180125. 2.Ingle BL, Veber BC, Nichols JW, Tornero-Velez R. 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 Nov 28;56(11):2243-2252. 3.Newton SR, McMahen RL, Sobus JR, Mansouri K, Williams AJ, McEachran AD, Strynar MJ. Suspect screening and non-targeted analysis of drinking water using point-of-use filters. Environ Pollut. 2018 Mar;234:297-306. doi: 10.1016/j.envpol.2017.11.033. 4.Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS. httk: R Package for High-Throughput Toxicokinetics. J StatSoftw. 2017 Jul 17;79(4):1-26. 5.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(4):1063- 1074. 6.Phillips KA, Yau A, Favela KA, Isaacs KK, McEachran A, Grulke C, Richard AM, Williams AJ, Sobus JR, Thomas RS, Wambaugh JF. Suspect Screening Analysis of Chemicals in Consumer Products. Environ Sci Technol. 2018 Mar 6;52(5):3125-3135. 7.Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM, Grulke CM, Wambaugh JF, Isaacs KK, Judson R, Williams AJ, Sobus JR. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ Int. 2016 Mar;88:269-280. 8.Ring CL, Arnot JA, Bennett DH, Egeghy PP, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips KA, Price PS, Shin HM, Westgate JN, Setzer RW, Wambaugh JF. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. Environ Sci Technol. 2019 Jan 15;53(2):719-732. 9.Sayre R, Wambaugh J, and Grulke C. Database of Pharmacokinetic Time-Series Data and Parameters for Evaluating the Safety of Environmental Chemicals. Presented at American Chemical Society Spring Meeting, Orlando, FL, March 31 - April 04, 2019. 10.Sjerps RMA, Vughs D, van Leerdam JA, Ter Laak TL, van Wezel AP. Data-driven prioritization of chemicals for various water types using suspect screening LC-HRMS. Water Res. 2016 Apr 15;93:254- 264 11.Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA, Hsieh JH, Shapiro AJ, Svoboda D, DeVito MJ, Ferguson SS. An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library. Environ Sci Technol. 2017 Sep 19;51 (18): 10786-10796. 12.Wambaugh JF, Wetmore BA, Ring CL, Nicolas CI, Pearce RG, Honda GS, Dinallo R, Angus D, Gilbert J, Sierra T, Badrinarayanan A, Snodgrass B, Brockman A, Strock C, Setzer RW, Thomas RS. Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization. Toxicol Sci. 2019a Dec 1;172(2):235- 251. 13.Wambaugh JF, Bare JC, Carignan CC, Dionisio KL, Dodson RE, Jolliet O, Liu X, Meyer D, Newton S, Phillips KA, Price PS, Ring CL, Shin H, Sobus JR, Tal T, Ulrich E, Vallero D, Wetmore BA, Isaacs KK. New approach methodologies for exposure science, Current Opinion in Toxicology, Volume 15, 2019b, Pages 76- 92. 14.Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, LeCluyse E, Clewell HJ, Thomas RS, Andersen ME. Incorporating High- Throughput Exposure Predictions With Dosimetry- Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing. Toxicol Sci. 2015 Nov; 148(1): 121-36. 24 of 24 Office of Research and Development ------- |