New Approach Methods - What is That?
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The document outlines the role and activities of the U.S. EPA's Center for Computational Toxicology and Exposure, which focuses on integrating new approach methodologies (NAMs) for chemical hazard and risk assessments while avoiding the use of animals. It includes information about key initiatives like ToxCast and Tox21, describing various computational and in vitro methods, and highlights the Comptox Chemicals Dashboard as a resource for accessing toxicological data. The presentation discusses the use of machine learning and quantitative structure-activity relationship models to enhance the assessment of chemical properties and bioactivity.
![QUESTION 5
• How many bioactivity assays are associated with the ESR1
(estrogen receptor 1 [ Homo sapiens (human) ]) gene?
24 30 32 23](https://image.slidesharecdn.com/williamsslidedeck08182021v4-210821025028/85/New-Approach-Methods-What-is-That-64-320.jpg)
![QUESTION 5
• How many bioactivity assays are associated with the ESR1
(estrogen receptor 1 [ Homo sapiens (human) ]) gene?
24 30 32 23](https://image.slidesharecdn.com/williamsslidedeck08182021v4-210821025028/85/New-Approach-Methods-What-is-That-66-320.jpg)
New Approach Methods - What is That?
- 1. Center for Computational Toxicology and Exposure, US-EPA, RTP, NC http://www.orcid.org/0000-0002-2668-4821
- 4. Center for Computational Toxicology and Exposure, US-EPA, RTP, NC http://www.orcid.org/0000-0002-2668-4821 New Approach Methods What is That??? 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 Antony John Williams williams.antony@epa.gov Thanks to John Cowden, Katie Paul-Friedman, Grace Patlewicz and John Wambaugh
- 5. Announcements from the EPA 2019 – present
- 6. • National Center for Computational Toxicology established in 2005 to integrate: – High-throughput and high-content technologies – Modern molecular biology – Data mining and statistical modeling – Computational biology and chemistry • Staffed by ~60 employees as part of EPA’s Office of Research and Development • Home of ToxCast & ExpoCast research efforts • Key partner in U.S. Tox21 federal consortium The big jump was the National Center for Computational Toxicology
- 7. Now the Center for Computational Toxicology and Exposure
- 8. What’s a NAM? • NAM = New Approach Methodologies • Commonly defined to include in silico approaches, in vitro assays, as well as the inclusion of information from the exposure of chemicals in the context of hazard and exposure assessment. • Defined in the EPA’s TSCA Alternative Toxicity Strategy as: • a broadly descriptive reference to any technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. https://echa.europa.eu/documents/10162/22816069/scientific_ws_proceedings_en.pdf https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/alternative-test-methods-and- strategies-reduce Thanks: John Cowden
- 9. Examples of New Approach Methodologies • In silico (e.g. QSAR and Read-across) • Estimate effects and doses • Consensus exposure modeling • In vitro assays • Broad / screening (transcriptomics, cell painting) • Targeted (receptors, enzymes) • In vitro PODs, modes / mechanisms of action
- 10. Examples of New Approach Methodologies • In vitro Toxicokinetics • Allow conversion of an in vitro POD to in vivo (IVIVE) • High-throughput Exposure Measurements • To fill data gaps in monitoring data • Computer models • Hazard models to integrate multiple in silico and in vitro data streams • Exposure models to increase information on different pathways of exposure
- 11. The Dashboard and NAMS • Our overview of NAMS will specifically relate to the availability of data in the CompTox Chemicals Dashboard you can access • All data is accessible at http://comptox.epa.gov/dashboard • See the FIRST session for details
- 12. CompTox Chemicals Dashboard https://comptox.epa.gov/dashboard 12 SEARCH TOX DATA BIOACTIVITY SIMILARITY READ-ACROSS PUBMED BATCH SEARCH
- 13. The Dashboard and NAMS • I will touch on aspects of Sessions 5 and 6 but more will come..
- 14. BASIC Search • Type ahead search using Names, synonyms and CASRNs • Millions of identifiers • Substring search
- 15. Navigating data via the Left Hand Tabs
- 16. Different QS(X)R predictions • There are many different “QSAR-related” predictions available • QSPR: quantitative structure–property relationships • QSAR: quantitative structure–activity relationships • QSUR: quantitative structure-use relationships
- 17. Experimental and Predicted Data From the LAST SESSION • Physchem and Fate & Transport experimental and predicted data • Data can be downloaded as Excel, TSV and CSV files • Predictions: multiple algorithms • EPI Suite: Estimation Program Interface • ACD/Labs (commercial) • TEST: Toxicity Estimation Software Tool • OPERA: OPEn structure–activity/ property Relationship App
- 18. Data Curation Pipelines plus Manual Curation Processes
- 19. TEST and OPERA Predictions DESKTOP tools if you need them
- 20. Property and Fate and Transport Data ~25 MILLION pre-predicted values • We have built QSPR models based on tens of thousands of property data points curated over the past decade • We push our “QSAR-Ready” chemical structures through predictions to produce property predictions
- 22. OPERA Reports
- 23. Similar reports for TEST predictions
- 27. TEST Predictions ONLINE • Activity prediction and classification
- 28. BATCH access to data
- 29. What do we use predictions for? • Property predictions can be used for • Experimental design – what chemicals are too volatile to perform bioactivity screens on? • As inputs to other models – for example toxicokinetic models, exposure models (we cannot measure all the properties we need to build models) • To use as flags related to persistence and bioaccumulation
- 30. Machine Learning NAMS Chemical Structure and Property Descriptors humectant lubricating agent perfumer pH stabilizer oxidizer heat stabilizer photo- initiator masking agent hair dye organic pigment flavorant flame retardant film forming agent foam boosting agent foamer reducer rheology modifier skin protectant skin condi- tioner soluble dye catalyst chelator colorant crosslinker emollient emulsifier fragrance plasticizer monomer solvent antistatic agent anti- oxidant anti- microbial adhesion promoter additive for rubber additive for liquid system whitener wetting agent viscosity controlling agent vinyl UV absorber ubiquitous surfactant pre- servative oral care hair condi- tioner emulsion stabilizer buffer additive Probabilistic Predictions of Potential Chemical Uses Chemical Functional Use Database (FUSE) Phillips et al. (2017) Successful Model Failed Model Positive Examples Negative Examples
- 31. QSUR modeling
- 32. QUESTION 1 • How many “nearest neighbor” chemicals are in the Boiling Point OPERA model report for Bisphenol A 6 9 5 10
- 33. How to get there… • Search for the chemical… • Navigate to properties… • Find the property of interest… • Open the Predicted Properties and OPERA report…
- 34. QUESTION 1 • How many “nearest neighbor” chemicals are in the Boiling Point OPERA model report for Bisphenol A 6 9 5 10
- 35. Different QS(X)R predictions • What is the different between qsrr and qsar? How is DFT being utilized in this field? • QSPR: quantitative structure–property relationships • QSAR: quantitative structure–activity relationships • QSUR: quantitative structure-use relationships • QSRR: quantitative structure-(Chromatographic) retention relationships
- 36. QSRRs
- 37. ToxCast and Tox21 bioactivity data for hazard screening and prediction. 37 • ToxCast: more assays, fewer chemicals, EPA-driven • Tox21: fewer assays, mostly 1536, driven by consortium • All Tox21 data are analyzed by multiple partners • Tox21 data is available analyzed in the ToxCast Data Pipeline and other pipelines as well EPA’s ToxCast program at a glance Tox21 robot
- 38. ToxCast
- 39. ToxCast Chemicals and Assays
- 40. ToxCast/To21 HTS data 40 Estimate >10 million chemical-assay data points!!!
- 41. ToxCast covers a lot of biology but not all ToxCast is growing over time. Invitrodb version 3.3 (released August 2020) contained 17 different assay sources, covering (at least) 491 unique gene-related targets with 1600 unique assay endpoints. 41 Assay source Long name Truncated assay source description Some rough notes on the biology covered ACEA ACEA Biosciences real-time, label-free, cell growth assay system based on a microelectronic impedance readout Endocrine (ER-induced proliferation) APR Apredica CellCiphr High Content Imaging system Hepatic cells (HepG2) ATG Attagene multiplexed pathway profiling platform Nuclear receptor and stress response profile BSK Bioseek BioMAP system providing uniquely informative biological activity profiles in complex human primary co-culture systems Immune/inflammation responses NVS Novascreen large diverse suite of cell-free binding and biochemical assays. Receptor binding; transporter protein binding; ion channels; enzyme inhibition; many targets OT Odyssey Thera novel protein:protein interaction assays using protein-fragment complementation technology Endocrine (ER and AR) TOX21 Tox21/NCGC Tox21 is an interagency agreement between the NIH, NTP, FDA and EPA. NIH Chemical Genomics Center (NCGC) is the primary screening facility running ultra high-throughput screening assays across a large interagency-developed chemical library Many – with many nuclear receptors CEETOX Ceetox/OpAns HT-H295R assay Endocrine (steroidogenesis) CLD CellzDirect Formerly CellzDirect, this Contract Research Organization (CRO) is now part of the Invitrogen brand of Thermo Fisher providing cell-based in vitro assay screening services using primary hepatocytes. Liver (Phase I/Phase II/ Phase III expression) NHEERL_PADILLA NHEERL Padilla Lab The Padilla laboratory at the EPA National Health and Environmental Effects Research Laboratory focuses on the development and screening of zebrafish assays. Zebrafish terata NCCT NCCT Simmons Lab The Simmons Lab at the EPA National Center for Computational Toxicology focuses on developing and implementing in vitro methods to identify potential environmental toxicants. Endocrine (thyroid - thyroperoxidase inhibition) TANGUAY Tanguay Lab The Tanguay Lab, based at the Oregon State University Sinnhuber Aquatic Research Laboratory, uses zebrafish as a systems toxicology model. Zebrafish terata/phenotypes NHEERL_NIS NHEERL Stoker & Laws The Stoker and Laws laboratories at the EPA National Health and Environmental Effects Research Laboratory work on the development and implementation of high-throughput assays, particularly related to the sodium-iodide cotransporter (NIS). Endocrine (thyroid - NIS inhibition) UPITT University of Pittsburgh The Johnston Lab at the University of Pittsburgh ran androgen receptor nuclear translocation assays under a Material Transfer Agreement (MTA) for the ToxCast Phase 1, Phase 2, and E1K chemicals. Endocrine (AR related)
- 42. ToxCast Assays
- 43. QUESTION 2 • How many chemicals are in the Tox21 screening library on the dashboard? 9847 8947 4987 7894
- 44. The power of the Lists Page
- 45. QUESTION 2 • How many chemicals are in the Tox21 screening library on the dashboard? 9847 8947 4987 7894
- 46. The Tox21 Screening Library
- 47. Learning more about the assay endpoints and biology Download summary information here: https://www.epa.gov/chemical-research/exploring-toxcast-data-downloadable-data Assay endpoint Assay component Assay CEETOX_H295R ESTRADIOL ESTRADIOL_up ESTRADIOL_dn TESTOSTERONE TESTOSTERONE_up TESTOSTERONE_dn https://comptox.epa.gov/dashboard/assay_endpoints/ Example assay annotation hierarchy • Many assay endpoints mapped to a gene, if applicable • Assay endpoints now cover >1000 unique gene targets in invitrodb 3.3 • Intended target family helps understand biological target(s) • Apolipoprotein • Apoptosis • Background measurement • Catalase • Cell adhesion • Cell cycle • Cell morphology • CYP • Cytokine • Deiodinase • DNA binding • Esterase • Filaments • GPCR • Growth factor • Histones • Hydrolase • Ion channel • Kinase • Ligase • Lyase • Malformation (zebrafish) • Membrane protein • Mitochondria • Methyltransferase • microRNA • Mutagenicity response • Nuclear receptor • Oxidoreductase • Phosphatase • Protease/inhibitor • Steroid hormone • Transferase • Transporter
- 48. What can be done with ToxCast data? • (for example) Does this substance have endocrine or liver-mediated bioactivity? • Is there support for one or more adverse outcome pathways based on these data, or does the substance appear “non-selective?” 48 • What is the relative priority of this substance for additional evaluation? • Can a protective bioactivity- based point-of-departure be calculated? Answering biological questions Answering risk-related questions
- 49. Lets look at the data
- 50. Rich data tables – full transparency
- 51. QUESTION 3 • How many assays does the chemical Bisphenol A show an ACTIVE hit response in? 1152 351 452 217
- 52. Bioactivity Data (ToxCast/Tox21) Data below for Bisphenol A 52
- 53. QUESTION 3 • How many assays does the chemical Bisphenol A show an ACTIVE hit response in? 1152 351 452 217
- 54. #Actives for a chemical
- 55. Cytotoxicity Threshold 55 This is the cytotoxicity threshold or “burst” based on the method described in Judson et al. 2016. It is the lower bound on the estimate of a cytotoxicity threshold.
- 56. What to make of the data • Bisphenol A clearly has some in vitro nuclear receptor activity at concentrations that may be below or near cytotoxicity. • It has moderate ToxCast ER agonist and AR antagonist scores. • The cytotoxicity threshold or “burst” seems to support selectivity of some nuclear receptor responses. • Diving a little deeper into the intended target family supports this analysis.
- 57. QUESTION 4 • How many assay endpoints are associated with the ACEA vendor family of assays? 16 11 6 21
- 58. Let’s look at the assay table ACEA_ER
- 59. High-Level Visualizations of Data
- 60. QUESTION 4 • How many assay endpoints are associated with the ACEA vendor family of assays 16 11 6 21
- 61. Filtering by vendor….or Gene Symbol
- 62. Use Models Derived from the Data
- 63. For Endocrine (AR and ER) better to use summary models Positive ToxCast ER pathway agonist and ToxCast AR antagonist scores. CERAPP = consensus ER QSAR (from 17 groups) COMPARA = consensus AR QSAR ToxCast Pathway Model AUC ER = full ER model (18 assays) ToxCast Pathway Model AUC AR = full AR model (11 assays)
- 64. QUESTION 5 • How many bioactivity assays are associated with the ESR1 (estrogen receptor 1 [ Homo sapiens (human) ]) gene? 24 30 32 23
- 65. Where do we look for assay details? • How do we search the 100s of genes mapped against assays • Home page: Assay/Gene Search
- 66. QUESTION 5 • How many bioactivity assays are associated with the ESR1 (estrogen receptor 1 [ Homo sapiens (human) ]) gene? 24 30 32 23
- 67. A note on ToxCast versioning • Data change: curve-fitting, addition of new data • Models change: improvements, more data, etc. • The CompTox Chemicals Dashboard release from July 2020 is now using ToxCast invitrodb version 3.3: https://doi.org/10.23645/epacomptox.6062479.v5 • All ToxCast data and endocrine models (CERAPP, COMPARA, ER, AR, steroidogenesis) can currently be accessed from within invitrodb. • Data downloads for NCCT: https://www.epa.gov/chemical- research/exploring-toxcast-data-downloadable-data • We anticipate a new ToxCast release in 2021. 67
- 68. QUESTION 6 • How many Assay Endpoints are reported through the Dashboard? 1759 1659 1569 569
- 69. With each release, more assay endpoints and more chemical x endpoint data are released Invitrodb version 3.3 (released August 2020) contained 17 different assay sources, covering (at least) 491 unique gene- related targets with 1600 unique assay endpoints. Varying amounts of data are available for 9949 unique substances. These assay endpoints were notable additions in invitrodb version 3.3. 69 Assay source Long name Truncated assay source description Some rough notes on the biology covered NCCT_MITO NCCT (now Center for Computational Toxicology and Exposure) Mitochondrial toxicity Respirometric assay that measure mitochondrial function in HepG2 cells Multiple assay endpoints to evaluate mitochondrial function https://doi.org/10.1093/toxsci/kfaa059. NHEERL_MED NHEERL Mid- Continent Ecology Division The EPA Mid-Continent Ecology Division of the National Health and Environmental Effects Research Laboratory screened the ToxCast Phase 1 chemical library for hDIO1 (deiodinase 1) inhibition as part of an ecotoxicology effort. Endocrine (thyroid – hDIO1,2,3 inhibition) https://doi.org/10.1093/toxsci/kfy302 STM Stemina Stem cell-based metabolomic indicator of developmental toxicity for screening. Developmental toxicity screening – multiple assay endpoints https://doi.org/10.1093/toxsci/kfaa014 LTEA Life Tech Expression Analysis Gene expression measured in HepaRG cells following 48 hr exposure Liver toxicity model via transcription factor regulated- metabolism and markers of oxidative/cell stress; multiple assay endpoints
- 70. QUESTION 6 • How many Assay Endpoints are reported through the Dashboard? 1759 1659 1569 569
- 71. Check out the List of Assays
- 72. QUESTION 7 • How many physicochemical properties can be predicted using real time predictions on the Dashboard? 18 9 27 19
- 73. Remember where you find TEST?
- 74. QUESTION 7 • How many physicochemical properties can be predicted using realtime predictions on the Dashboard? 18 9 27 19
- 75. How are the bioactivity data used??? In Vitro-In Vivo Extrapolation (IVIVE) Translation of in vitro high throughput screening requires chemical-specific toxicokinetic models Needed for anywhere from dozens to thousands of chemicals Exposure in vitro bioactive concentration Toxicokinetic model: Absorption Distribution Metabolism Excretion Internal concentration Iin vivo TK data Concentration Response In vitro Bioactivity Assay
- 76. IVIVE-based reverse toxicokinetics Reverse dosimetry can be leveraged in IVIVE to estimate the exposure that would produce the plasma concentration corresponding to bioactivity High-throughput toxicokinetic (HTTK) approaches make it possible to predict doses corresponding to in vitro bioactivity for thousands of chemicals. 2012 A subset of the papers describing the development of a high- throughput toxicokinetic approach 2017 2017 2017 2014 2015 2019 2014 76
- 77. QUESTION 8 • How many HTTK related lists are available on the Dashboard? 0 6 3 1
- 78. QUESTION 8 • How many HTTK related lists are available on the Dashboard? 0 6 3 1
- 79. Bioactivity:exposure ratio requires exposure 79 Comparison to exposure predictions for a bioactivity:exposure ratio
- 81. Definitions: Read-Across • Known information on the property of a substance (source) is used to make a prediction of the same property for another substance (target) that is considered “similar” 81 Source chemical Target chemical Property Reliable data Missing data Predicted to be harmful Known to be harmful Acute fish toxicity?
- 82. GenRA (Generalised Read-Across) • Predicting toxicity as a similarity-weighted activity of nearest neighbors based on chemistry and/or bioactivity descriptors • Goal: to systematically evaluate read-across performance and uncertainty using available data • The approach enabled a performance baseline for read-across predictions of toxicity effects within specific study outcomes to be established 82
- 84. Read-across workflow in GenRA 84 Decision Context Screening level assessment of hazard based on toxicity effects from ToxRefDB Analogue identification Similarity context is based on structural characteristics Data gap analysis for target and source analogues Analogue evaluation Evaluate consistency and concordance of experimental data of source analogues across and between endpoints Read-across Similarity weighted average – many to one read-across Uncertainty assessment Assess prediction and uncertainty using AUC and p value metrics
- 86. GenRA (Generalised Read-Across) Structure Similarity Select and Review Analogs
- 87. GenRA (Generalised Read-Across) Review Available Data Fingerprint indicating available data Select and Review Analogs
- 88. GenRA (Generalised Read-Across) 88 Run GenRA Target Source analogues Red : Toxicity effects. Blue: No Toxicity effects Grey : Absence of data
- 89. QUESTION 9 • In the GenRA module what is the chemical most similar to Fluconazole? Flusizazole Isthatahole Hexaconazole Metaconazole
- 91. QUESTION 9 • In the GenRA module what is the chemical most similar to Fluconazole? Flusizazole Isthatahole Hexaconazole Metaconazole
- 92. Session 5 will focus on Exposure
- 94. QUESTION 10 LAST QUESTION… • How many chemicals are in the “National Health and Nutrition Examination Survey” list 412 214 124 >500
- 95. Where would you look for a list?
- 96. QUESTION 10 • How many chemicals are in the “National Health and Nutrition Examination Survey” list 412 214 124 >500
- 97. Conclusions • NAMs are increasingly accepted as the path forward to move away from animal testing • EPA-CCTE (previously NCCT) has been at the forefront of NAMs development for over 15 years – in vitro bioactivity measurements and modeling, exposure modeling, in vitro to in vivo extrapolation, QSAR/QSUR/QSPR prediction etc. • The Dashboard offers a path in to source relevant data and models generated from our work 97
- 100. You want to know more… • Lots of resources available • Presentations: https://tinyurl.com/w5hqs55 • Communities of Practice Videos: https://rb.gy/qsbno1 • Manual: https://rb.gy/4fgydc • Latest News: https://comptox.epa.gov/dashboard/news_info 100
- 101. Acknowledgments • Contact: Williams.Antony@epa.gov • Thanks to John Cowden, Katie Paul-Friedman, Grace Patlewicz and John Wambaugh for slides • Feedback and follow-up is welcomed! Your questions help • The dashboard is based on the efforts of many more team members than us. Many collaborators provide data also. 101 EPA’s Center for Computational Toxicology and Exposure
- 102. Questions from the Audience • “Is there a unified database of CYP450 and drug transporter interactions parameters (Km, Ki, EC50, IC50, etc.) with drugs and environmental chemicals” • Transportal: https://transportal.compbio.ucsf.edu/
- 103. Other databases of interest • PDSP Ki https://pdsp.unc.edu/databases/kidb.php • BindingDB https://www.bindingdb.org/
- 105. 1. What is the best way to move beyond using many NAMs just for prioritization? Prioritizing chemicals for what? Moving into animal experiments we are trying to eliminate using? 2. How can we use information from NAMs to support experiments using traditional in-vivo bioassays? 3. I am quite interested in applying various NAMs endpoints in risk assessment, specifically how said data could be used in toxicity factor derivation (e.g., the identification of a PODhec). Do we see the data as an informant of MOA/key events or dose-response or both? How do we get the risk assessment community to rely more upon these types of data? How do we demonstrate that these methods are as good or better than traditional models that have been relied upon for toxicity factor derivation up until this point? What new uncertainties need to be accounted for using this type of data? 4. How to obtain sufficient toxicological data in short time consuming and how to interpret the data based on figures and numbers and ask critical questions?? Thank you! 5. if you can have extra resources for NAMS explanations not presented - breakdown of what needs in NAMS tables. For instance, physiochemical explanations to discount aspiration hazard not in NAMs? Only modeling? In vitro studies? Etc.
Editor's Notes
- #9: May also include a variety of new testing tools, such as “high-throughput screening” and “high-content methods” e.g. genomics, proteomics, metabolomics; as well as some “conventional” methods that aim to improve understanding of toxic effects, either through improving toxicokinetic or toxicodynamic knowledge for substances.
- #10: May also include a variety of new testing tools, such as “high-throughput screening” and “high-content methods” e.g. genomics, proteomics, metabolomics; as well as some “conventional” methods that aim to improve understanding of toxic effects, either through improving toxicokinetic or toxicodynamic knowledge for substances. Definitions: In silico – computationally based In chemico – chemical reactions, generally abiotic In vitro – taking place outside a living organism In vivo – taking place inside a living organism Toxicokinetics – how a substance gets into the body and what happens to it there POD – point of departure – point on a dose-response curve that marks the no effect or low effect level
- #11: May also include a variety of new testing tools, such as “high-throughput screening” and “high-content methods” e.g. genomics, proteomics, metabolomics; as well as some “conventional” methods that aim to improve understanding of toxic effects, either through improving toxicokinetic or toxicodynamic knowledge for substances. Definitions: In silico – computationally based In chemico – chemical reactions, generally abiotic In vitro – taking place outside a living organism In vivo – taking place inside a living organism Toxicokinetics – how a substance gets into the body and what happens to it there POD – point of departure – point on a dose-response curve that marks the no effect or low effect level
- #80: BER50 is about 280