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Office of Research and Development National Center for Computational Toxicology Richard Judson Computational Toxicology UNC, November.

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Presentation on theme: "Office of Research and Development National Center for Computational Toxicology Richard Judson Computational Toxicology UNC, November."— Presentation transcript:

1 Office of Research and Development National Center for Computational Toxicology Richard Judson Computational Toxicology UNC, November 2012 The views expressed in this presentation are those of the author[s] and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

2 Big Ideas Understand chemical toxicity at a molecular level Understand using as few animal as possible Build predictive models Screening and prioritization Assess many chemicals – deal with the data gaps 1

3 Problem Statement 2 Too many chemicals to test with standard animal-based methods –Cost, time, animal welfare –Exposure is as important as hazard Too many chemicals to test with standard animal-based methods –Cost, time, animal welfare –Exposure is as important as hazard Need for better mechanistic data - Determine human relevance - What is the relevant Mode of Action (MOA) or Adverse Outcome Pathway (AOP)? Need for better mechanistic data - Determine human relevance - What is the relevant Mode of Action (MOA) or Adverse Outcome Pathway (AOP)?

4 Computational Toxicology 3 Benefits Less expensive More chemicals screened Fewer animals Solution oriented Innovative Multi-disciplinary Collaborative Catalytic Transparent Cancer ReproTox DevTox NeuroTox PulmonaryTox ImmunoTox in vitro testing Bioinformatics/ Machine Learning

5 Office of Research and Development National Center for Computational Toxicology Chemical Universe >100,000 Chemicals with likely exposure potential Mixtures HTS Chemical Library Chemicals w/o HTS or structural similarity Active chemicals and structural neighbors AOP / MOA Targeted High-throughput testing High, Medium, Low priority bins Inactive chemicals and structural neighbors Structural neighbors to HTS library Very Low priority bin Detailed Exposure and Toxicokinetics Evaluation Initial Exposure Evaluation: Use Categories Initial Objective: Risk-based Prioritization Structure Similarity Modeling

6 Hazard-based Approach Identify molecular targets or biological pathways linked to toxicity –MOA / AOP –Chemicals perturbing these can lead to adverse events Develop assays for these targets or pathways –Assays probe “Molecular Initiating Events” or “Key Events” [MIE / KE] Develop predictive models: in vitro → in vivo –“Toxicity Signature” –Extend to inform biomarkers or bioindicators for key events Use signatures: –Prioritize chemicals for targeted testing (“Too Many Chemicals” problem) –Suggest / distinguish possible AOP / MOA for chemicals AOP / MOA Targeted High-throughput testing

7 6 Toxicity Pathways Receptors / Enzymes / etc. Direct Molecular Interaction Pathway Regulation / Genomics Cellular Processes Tissue / Organ / Organism Tox Endpoint Chemical

8 AOP / MOA Development International workgroups developing frameworks and models –OECD – AOP –WHO – MOA Key Concepts –Molecular Initiating Events or Key Events – measureable in vitro –Causal evidence for downstream effects –AOP includes effects up to the population level 7 Ankley et al. 2010 AOP / MOA Targeted HTS Data AOP / MOA Targeted High-throughput testing

9 Office of Research and Development National Center for Computational Toxicology Knudsen and Kleinstreuer. Birth Def Res C. 2012 AOP / MOA Targeted HTS Data Proposed AOP: Embryonic Vascular Disruption AOP / MOA Targeted High-throughput testing

10 ToxCast Combine High-throughput screening with computer models

11 10 Key Research and Tools Toxicity Forecaster (ToxCast) 500 fast, automated chemical screens (in vitro) Builds statistical and computer models to forecast potential chemical toxicity Phase 1: Screened over 300 well characterized chemicals Phase 2: 700 more chemicals representing broad structures Multi-year, multi million dollar effort Tox21 collaboration utilizes ToxCast

12 Tox21 qHTS 10K Library NCGC –Drugs –Drug-like compounds –Active pharmaceutical ingredients EPA Pesticides actives and inerts Industrial chemicals Endocrine Disruptor Screening Program OECD Molecular Screening Working Group FDA Drug Induced Liver Injury Project Failed Drugs NTP NTP-studied compounds NTP nominations and related compounds NICEATM/ICCVAM validation reference compounds for regulatory tests External collaborators (e.g., Silent Spring Institute, U.S. Army Public Health Command) Formulated mixtures AOP / MOA Targeted HTS Data AOP / MOA Targeted High-throughput testing

13 12 Human Relevance/ Cost/Complexity Throughput/ Simplicity High-Throughput Screening Assays 10s-100s/yr 10s-100s/day 1000s/day 10,000s- 100,000s/day LTSHTSMTSuHTS batch testing of chemicals for pharmacological/toxicological endpoints using automated liquid handling, detectors, and data acquisition Gene-expression

14 13 High Throughput Screening 101 96-, 384-, 1536 Well Plates Assay Target Biology (e.g., Estrogen Receptor) HTS Robotic Platform Pathway Chemical Exposure Cell Population HTS: High Throughput Screening

15 14 Biochemical Assays Protein super-families –GPCR –Kinase –Phosphatase –Protease –Ion channel –Nuclear receptor –Other enzyme –CYP P450 inhibition Various formats: –Radioligand receptor binding –Fluorescent receptor binding –Fluorescent enzyme substrate- intensity quench –Fluorescent enzyme substrate- mobility shift Initial screening: –25  M in duplicate –10  M in duplicate (CYPs) Normalize data to assay window –% of control activity (central reference – scalar reference)

16 15 What do biochemical assays measure? Mainly direct effects of chemical on target protein –Enzyme activity –Ligand binding False positives: –Fluorescent compounds—fluorescing and quenching –Reactive compounds/covalent modification of target –Physical effects—colloid aggregation of target –Operational False negatives: –Solubility –Inappropriate assay conditions –Operational –Target protein not physiological –Lack of biotransformation

17 16 Biochemical Concentration-Response Testing Retest actives: –Median absolute deviation (MAD) median Ιx-xmedΙ two MADs or 30% activity –8 conc/3-fold serial dilutions 50  M high conc 25  M high conc for CYPs Normalize to assay window Fit % Activity data to 3- or 4- parameter Hill function –Sometimes had to fix top or bottom of curve –Did not extrapolate beyond testing range –Manual or automated removal of obvious outliers

18 17 Example Curve Fits hCYP 2C9 hER  rAdrRa2B hLynA Activator hM1 hKATPase

19 18 Real Time Cell Growth Kinetics Cytotoxicity with potential mechanistic interpretation Human A549 lung carcinoma cell line –ACEA experience with line –Reference compound effects Concentration-response testing –8 conc/3-fold serial dilutions –Duplicate wells Real-time measuremens during exposure (0-72 hr) IC50 and LELs calculated

20 19 Example Plots: Data examples Replicate Analysis:

21 20 Multiplexed Transcription Factor Assays Modulation of TF activity in human hepatoma HepG2 cells Multiplexed reporter gene assay –cis 52 assays (response element driving reporter) –trans 29 assays (GAL4-NR_LBD driving reporter) “ligand detection” IC50 for cytotoxicity measured first in HepG2 High concentration either 100  M or 1/3 calculated IC50 for cytotoxicity Seven concentrations, 3-fold serial dilutions, 24 hr exposure Cells harvested, RNA isolated, processed for reporter gene quantitation LEL provided in data set

22 21 Multiplexed Reporter Gene Technology Cis: AhR

23 22 trans: ERa cis: ERE Bisphenol AHPTE Corresponding cis and trans assays

24 23 BioSeek: BioMAP® Technology Platform Assays Human primary cells Disease-like culture conditions LPS BF4T SM3C Profile Database Informatics Biological responses to drugs and stored in the database Specialized informatics tools are used to mine and analyze biological data Primary Human Cell-Based Assay Platform for Human Pharmacology

25 24 BioSeek Assays Tested

26 25 High-Content Screening of Cellular Phenotypic Toxicity Parameters Technology: automated fluorescent microscopy Objective: Determine effects of chemicals on toxicity biomarkers in a cell culture of HepG2 and primary rat hepatocytes Cell Cycle CSK Integrity DNA Damage Oxidative Stress Stress Pathway Activation Organelle Functions Panel 1 design*: Multiple mechanisms of toxicity Acute, early & chronic exposure 384-well capacity HepG2

27 26 Cell Loss Mitochondrial Membrane Potential DNA Damage Data Examples

28 27 XME Gene Expression in Primary Human Hepatocytes Primary human hepatocytes from two donors used Cells exposed for 6, 24, and 48 hr; medium/chemical refreshed daily Concentrations tested: 40, 4, 0.4, 0.04, and 0.004 µM 16 Genes measured in multiplexed RNAse protection assay (qNPA) Genes targeted XME and transporters

29 28 Data Examples CYP1A1-AhRCYP2B6-CARHMGCS2-PPARα

30 29 NCGC Reporter Gene Assays Nuclear Receptors –GAL4 System (ligand detection assay) –11 human receptors –1 rat (PXR) –  -lactamase reporter gene assays except: –PXR assays are luciferase reporter gene assays p53 Reporter Gene assay –  -lactamase reporter gene assay Parental cell lines mostly HEK293 (also HeLa and DPX-2) 12-15 point concentration-response curves (single replicate)

31 30 NCGC: Data Calculations Data normalized to reference compound effect Curves fit to 3- or 4-parameter Hill equation Artifacts removed where obvious fluorescence or cytotoxity detected Required at least 25% efficacy of control compound to calculate AC50 AC50 values provided Antagonist format assays challenging due to effects of cytotoxicity LXR assay problematic— contaminated with GR reporter line? ER  PPAR 

32 Applications

33 32 Published Predictive Toxicity Models  Predictive models: endpoints liver tumors: Judson et al. 2010, Env Hlth Persp 118: 485-492 hepatocarcinogenesis: Shah et al. 2011, PLoS One 6(2): e14584 cancer: Kleinstreuer et al. 2012, submitted rat fertility: Martin et al. 2011, Biol Reprod 85: 327-339 rat-rabbit prenatal devtox: Sipes et al. 2011, Toxicol Sci 124: 109-127 zebrafish vs ToxRefDB: Sipes et al. 2011, Birth Defects Res C 93: 256-267  Predictive models: pathways endocrine disruption: Reif et al. 2010, Env Hlth Persp 118: 1714-1720 microdosimetry: Wambaugh and Shah 2010, PLoS Comp Biol 6: e1000756 mESC differentiation: Chandler et al. 2011, PLoS One 6(6): e18540 HTP risk assessment: Judson et al. 2011, Chem Res Toxicol 24: 451-462 angiogenesis: Kleinstreuer et al. 2011, Env Hlth Persp 119: 1596-1603  Continuing To Expand & Validate Prediction Models  Generally moving towards more mechanistic/AOP-based models

34 Predictive Model Development

35 34 Martin et al 2011 Reproductive Rat Toxicity Model Features

36 35 36 Assays Across 8 Features Balanced Accuracy Training: 77% Test: 74% 36 Assays Across 8 Features Balanced Accuracy Training: 77% Test: 74% + - Martin et al 2011 Reproductive Rat Toxicity Model Features

37 Example: Cancer Signatures Non-genotoxic carcinogens Use insights from Hallmarks of Cancer –Hanahan and Weinberg 2000, 2011 –Cancer is a multi-step progressive disease –Virtually all cancers display all hallmark processes We observe that most chemicals perturb multiple pathways Hypothesis: –A chemical that perturbs many pathways related to cancer hallmark processes will be more likely to cause cancer in the lifetime of an animal than a chemical that perturbs few such pathways –Chemicals can increase cancer risk through many different patterns of pathway perturbations 36

38 Hallmarks of Cancer Hanahan and Weinberg (2000) 37 PPAR  p53 CCL2 ICAM1

39 Hallmarks of Cancer Hanahan and Weinberg (2011) 38 IL-1a IL-8 CXCL10 IL-1a IL-8 CXCL10

40 Pathway Hits Raise Risk of Multiple Cancer Types 39 Hallmark-related ADME-related Endpoint Level 2: Preneoplastic Level 3: Neoplastic

41 Understanding Success and Failure Why In vitro to in vivo can work: –Chemicals cause effects through direct molecular interactions that we can measure with in vitro assays Why in vitro to in vivo does not always work: –Pharmacokinetics issues: biotransformation, clearance (FP, FN) –Assay coverage: don’t have all the right assays (FN) –Tissue issues: may need multi-cellular networks and physiological signaling (FN) –Statistical power issues: need enough chemicals acting through a given MOA to be able to build and test model (FN) –Homeostasis: A multi-cellular system may adapt to initial insult (FP) –In vitro assays are not perfect! (FP, FN) –In vivo rodent data is not perfect! (FP, FN) 40 Systems Models

42 Beyond in vitro to in vivo signatures 41 Structure Clusters Chemical Categories In vitro Assays Adverse Outcome Pharmacokinetics In Vitro-In Vivo Signatures

43 Combining Chemical Structure and In Vitro Assays Structure clustering based on chemical fragments –FP3, FP4, MACCS, PADEL, PubChem (~2700 total) –Hierarchical clustering and then set variable cutoffs –For examples: ~12 chemicals / cluster Goals –Find clusters that are highly predictive of each assay (read-across) –Assay structure alerts: alternatives assessments –Assay QC 42 Cluster Assay Endpoint

44 Clusters 80% predictive of assay hit 43 ER Assays Estrogens Conazoles CYP Binding Assays Alkyl Phenols Surfactants GPCR Binding Assays Alachlor … Captan … Inflammation Assays Surfactants Chemical Set 2 Chemical Set 1 Assays Data Set Incomplete Azoles Tetracycline … Endosulfans Steroids

45 Office of Research and Development National Center for Computational Toxicology Adding Pharmacokinetics Reverse ToxicoKinetics (rTK) Human Hepatocytes (10 donor pool) Add Chemical (1 and 10  M) Remove Aliquots at 15, 30, 60, 120 min Analytical Chemistry Hepatic Clearance Human Plasma (6 donor pool) Add Chemical (1 and 10  M) Analytical Chemistry Plasma Protein Binding Equilibrium Dialysis 44 Combine experimental data with PK Model to estimate dose-to-concentration scaling Collaboration with Thomas et al.., Hamner Institutes Publications: Rotroff et al, ToxSci 2010, Wetmore et al, ToxSci 2012

46 Triclosan Pyrithiobac-sodium log (mg/kg/day) Rotroff, et al. Tox.Sci 2010 Wetmore et al Tox Sci 2012 45 Range of in vitro AC50 values converted to human in vivo daily dose Actual Exposure (est. max.) margin Combining in vitro activity and dosimetry

47 Application: Endocrine Disruption Prioritization –Screening thousands of chemicals –Developing activity thresholds of concern Dose-relevance –Combining in vitro data with PK modeling –Refining activity thresholds of concern Investigating the broader range of phenotypes of concern –Use many available in vitro tests and computer models as complement to EDSP animal tests 46

48 Initial Prioritization Application: EDSP21 Use high-throughput in vitro assays and modeling tools to prioritize chemicals for EDSP Tier 1 screening assays 47

49 ER / AR Focus: EDSP21 Endocrine Disruptor Screening Program –FQPA, SDWA 1996 contain provisions for screening for chemicals and pesticides for possible endocrine effects –Test pathways: estrogen, androgen, thyroid, steroidogenesis (EATS) –Universe of chemicals: 5000-6000 Tier 1 screening battery (T1S): 11 in vitro & in vivo assays –Development and validation > 10 years – >$1 M per chemical –Current throughput < 100 chemicals / year EDSP21 goal: –Prioritize chemicals for T1S –Hypothesis: EATS (in vitro)+ more likely to be T1S+ –Use many EATS in vitro assays –Combine with modeling, use, occurrence and exposure information 48

50 Characterizing chemicals for estrogen signaling pathway activity Active vs. inactive Potency and efficacy spectrum across assays Agonist … Antagonist Partial … full Agonist / Antagonist ER  vs. ER  Metabolically activated or deactivated Cell type specificity ER-mediated or not 49 All Data is preliminary and unpublished

51 Office of Research and Development National Center for Computational Toxicology Pro-ligand ER Active ligand Cofactor ER-regulated gene expression Cell proliferation Oxidative stress pathways Non-ER-mediated cell proliferation pathways Non-ligand- mediated activation of ER activity Attagene NCGC ACEA Odyssey Thera Odyssey Thera Novascreen Using multiple lines of evidence to test for ER activity Odyssey Thera and Attagene assays have metabolic capacity

52 Estrogen signaling pathway assays 51 source_name_aid sourceconditionorganismtissueCell FormatCell Type ACEA_T47D ACEA humanbreastCell lineT47D ATG_ERa_TRANS Attagene humanliverCell lineHepG2 ATG_ERE_CIS Attagene humanliverCell lineHepG2 Tox21_ERa_BLA_Agonist Tox21 humankidneyCell lineHEK293T Tox21_ERa_BLA_Antagonist Tox21 humankidneyCell lineHEK293T Tox21_ERa_LUC_BG1_Agonist Tox21 humanovarianCell lineBG1 Tox21_ERa_LUC_BG1_Antagonist Tox21 humanovarianCell lineBG1 NVS_NR_bER Novascreen bovineuterustissue extract NVS_NR_hER Novascreen humanbreastCell line: cell extract NVS_NR_mERa Novascreen mouseuterustissue extract OT_ER_ERaERa Odyssey Thera +/- S9humankidneyCell lineHEK293T OT_ER_ERaERb Odyssey Thera +/- S9humankidneyCell lineHEK293T OT_ER_ERbERb Odyssey Thera +/- S9humankidneyCell lineHEK293T OT_ERa_GFPERa_ERE Odyssey Thera +/- S9humancervixCell lineHeLa OT_ERa_ERE_LUC_Agonist Odyssey Thera human Cell line: bulk transiently transfected CHO-K1 OT_ERa_ERE_LUC_Antagonist Odyssey Thera human Cell line: bulk transiently transfected CHO-K1 OT_ERb_ERE_LUC_Antagonist Odyssey Thera human Cell line: bulk transiently transfected CHO-K1

53 NCGC ER BG1-LUC vs. BLA Agonist Assays

54 53 Metabolic Capacity : +/- S9 for metabolism

55 Antagonist behavior in OT-PCA (ICI) 54 ERb-ERb ERa-ERa

56 55

57 56 -S9 +S9 Activation

58 57 -S9 +S9 ERα/ERβ Deactivation

59 58 White (-S9) Black (+S9) Comparing Odyssey Thera assays across potent estrogens

60 Initial Exposure Evaluation: Use Categories Mapping Chemicals to Use Categories Category hierarchy Chemical to Product Then Product to Category Chemical To Category Many sources of information on chemical use, mapped to categories Laundry detergent, industrial solvent, baby care

61 Initial Exposure Evaluation: Use Categories Mapping Use Categories to Scenarios Paint Food Additive Pesticide Baby Adult Kitchen Garage Multiple Category Hierarchies Map to Exposure Scenario Concepts Map to Exposure Scenarios

62 Detailed Exposure and Toxicokinetics Evaluation Model Detailed Exposure and Toxicokinetics Exposure modeling is goal of ExpoCast program Toxicokinetics uses Reverse Toxicokinetics (RTK) Combining RTK and HTS potency scores yields first-order estimate of dose that yields no biological effect: –BPAD – Biological Pathway Altering Dose –Core idea of HTRA – High-throughput Risk Assessment

63 Clustering 1763 chemicals by the media into which they partition most Could infer behavior of understudied chemicals from similar, well-known counterparts – “fate read- across High Throughput Fate Predictions 62 Detailed Exposure and Toxicokinetics Evaluation

64 High-Throughput Risk Assessment (HTRA) Risk assessment approach –Estimate upper dose that is still protective –RfD, BMD are standard, animal-based quantities –Compare to estimated steady state exposure levels Contributions of high-throughput methods –Focus on molecular pathways whose perturbation can lead to adversity –Screen hundreds to thousands of chemicals in in vitro assays for those targets –Estimate oral dose using H-T pharmacokinetic modeling Incorporate population variability and uncertainty 63 Detailed Exposure and Toxicokinetics Evaluation

65 What is High-Throughput Risk Assessment? Where does risk assessment come in? –Estimate upper dose that is still protective –RfD, BMD, POD Where does high-throughput come in? –Focus on molecular pathways and targets whose perturbation can lead to adversity –Screen hundreds to thousands of chemicals in in vitro assays for those targets –Get oral dose using H-T pharmacokinetic modeling Incorporate population variability and uncertainty 64

66 Why do HTRA? Thousands of chemicals with no or little animal data Need starting points for setting health-protective exposure levels These starting points can be used to prioritize further testing 65

67 HTRA Basic Outline 1. Define molecular pathways linked to adverse outcomes 2. Measure activity in vitro in concentration-response (PD) 3. Estimate external dose to internal concentration scaling (PK) 4. Estimate dose at which pathway is perturbed in vivo 5. Estimate population variability and uncertainty in PK and PD 6. Estimate lower end of dose range for perturbation of pathway 66

68 HTRA-BPAD Key Ideas HTRA = High Throughput Risk Assessment BPAD = Biological Pathway Altering Dose BPAC = Biological Pathway Altering Concentration C ss = Concentration to Dose ratio from PK model Key Ideas: –Define biological pathways whose alteration can lead to adverse outcomes Pathway perturbation = MOA Key Event evidence –Develop in vitro assays that measure chemical activity in biological pathways –Determine in vitro concentration required to alter pathway (BPAC) –Estimate oral dose required to reach BPAC (BPAD = BPAC/C ss ) –Incorporate variability and uncertainty 67

69 Estimating the concentration-to- dose scaling Use Reverse Toxicokinetics approach (RTK) –Led by R. Thomas, Hamner Inst. Uses experimental data on –Intrinsic clearance in human hepatocytes –Human plasma protein binding –Integrate using one-compartment PBPK model Yields C ss (concentration at steady state) –Units of  M/(mg/kg/day) Dose = Concentration / C ss RTK (SimCyp) provides estimates of population variability Need to add estimates of uncertainty 68

70 Estimate BPAD BPAD = BPAC / C ss Each are modeled as being log-normal BPAD has a population distribution, so take a protective level as the lower 99% tail (BPAD 99 ) Add in uncertainty and take the lower 95% bound on BPAD 99 to give a more protective lower bound –BPADL 99 69

71 Adverse Effect Toxicity Pathway Key Events MOA HTS Assays Intrinsic Clearance Plasma Protein Binding Populations PK Model Biological Pathway Activating Concentration (BPAC) Probability Distribution Dose-to-Concentration Scaling Function (C ss ) Probability Distribution for Dose that Activates Biological Pathway BPAD PharmacodynamicsPharmacokinetics R. Thomas et al., Hamner Inst.

72 Uncertainty and variability RTK modeling explicitly incorporates human population variability in PK (SimCyp) Other uncertainty and variability … –PK uncertainty due to model and data uncertainty –PD variability due to intrinsic variability in enzymes, receptors, pathways –PD uncertainty due to details of assay performance, etc. Need to develop approach to move away from using defaults for HTRA –Follow similar path to what is being developed for standard RA 71

73 Conazoles and Liver Hypertrophy Conazoles are known to cause liver hypertrophy and other liver pathologies Believed to be due (at least in part) to interactions with the CAR/PXR pathway ToxCast has measured many relevant assays Calculate BPAD for 14 conazoles and compare with liver hypertrophy NEL/100 72

74 Conazole / CAR/PXR results 73 LEL, NEL BPAD Range Exposure estimate “RfD”

75 HTRA Summary 1. Select Toxicity-related pathways 2. Develop assays to probe them 3. Estimate concentration at which pathway is “altered” (PD) 4. Estimate concentration-to-dose scaling (PK) 5. Estimate PK and PD uncertainty and variability 6. Combine to get BPAD distribution and safe tail Many (better) variants can be developed for each step (1-6) Use for analysis and prioritization of data poor chemicals 74

76 Summary Goal is to do high-throughput risk- based screening Apply to thousands of chemicals First-order estimates of: –Hazard: based on adverse outcome pathways –Exposure: far and near field routes –Toxicokinetics End product: –Prioritized list for more detailed testing –Catalog of potential AOPs that chemicals can trigger 75

77 Virtual Tissues Virtual Liver Virtual embryo Virtual Tissue Knowledgebase

78 77 Virtual Tissues Systems Models of Toxicity Pathways chemicals pathwaysnetworkscell states tissue function Quantitative Dose-Response Models Quantitative Dose-Response Models Next Generation Risk assessments Next Generation Risk assessments Moving beyond empirical models, to multi-scale models of complex biological systems. Identify Key Targets and Pathways For Prioritization Identify Key Targets and Pathways For Prioritization

79 Virtual Liver Cell-based computer model simulates chemical actions in virtual liver to estimate how much chemical it takes to lead to health- related effects Selection of every day chemicals with known human health effects will be used to develop proof it can be used for chemical toxicity prediction Organize evidence about biological networks to clarify toxic effects of new chemicals (mechanism of action) Uses ToxCast™ and other chemical data to simulate how chemicals could cause liver disease and cancer in humans 78

80 Virtual Embryo Goal: Will be used to accurately predict the potential for environmental chemicals to affect the embryo –Plans to use a selection of every day chemicals with known health effects in animal tests to determine if it is possible to use a virtual embryo model to predict the potential developmental toxicity of chemicals –Research uses fast, automated chemical screening data from ToxCast, ACToR & v-Liver to create simulations examining how chemicals could cause developmental problems –Initially focuses on early eye, vascular and limb development –Conducts experiments using stem cells and zebrafish to generate data 79

81 Data and Databases ACToR ToxRefDB ToxCastDB ExpoCastDB

82 Too Many ChemicalsToo Little Data (%) EPA’s Need for Toxicity Data Judson, et al EHP (2009) 9912

83 82 ACToR Aggregated Computational Toxicology Resource Tabular Data, Links to Web Resources Chemical ID, Structure Chemical Internet Searches ACToR API ToxRefDB ToxMiner ExpoCastDB In Vivo Study Data - OPP ToxCast Data – NCCT, ORD, Collaborators (Currently Internal) Exposure Data – NERL, NCCT (In Development) ACToR Core

84 ToxRefDB – Animal Study Level Data Extracted from OPP internal DB Relational phenotypic/toxicity database Provides in vivo anchor for ToxCast predictions Three study types Chronic/Cancer rat and mouse (Martin, et al, EHP 2008) Rat multigenerational Reproduction (Martin, et al, submitted) Rat & Rabbit developmental (Knudsen, et al, internal review) Two types of synthesis Supervised (common individual phenotypes) Unsupervised (machine based clustering of phenotype patterns)

85 ToxCastDB – ToxCast Data Links –Chemicals –Assays –Genes –Pathways –Endpoints Allows data analyses –Statistical associations –Biologically drive data mining

86 85 Exposure Background Exposure HumanEnvironment Biomonitoring Population Uptake Exposure Media Contact Products Sources Chemicals Host Susceptibility Biotransformation Exposome Informatics Approaches Network Models Knowledge Systems Mechanistic Models Exposure Database Distribution/Fate Exposure Data: ExpoCast Exposure Science for Prioritization and Toxicity Testing

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