Office of Research and DevelopmentNational Center for Computational Toxicologywww.epa.gov/ncct

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.

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

2

Problem Statement

3

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)?

Computational Toxicology

4

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

Office of Research and DevelopmentNational Center for Computational Toxicology

Chemical Universe>100,000

Chemicals with likely exposure potentialMixtures

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-basedPrioritization

Structure Similarity Modeling

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

Toxicity Pathways

Receptors / Enzymes / etc.Direct Molecular Interaction

Pathway Regulation / Genomics

Cellular Processes

Tissue / Organ / Organism Tox Endpoint

Chemical

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

8Ankley et al. 2010

AOP / MOA Targeted HTS Data

AOP / MOA Targeted High-throughput testing

Office of Research and DevelopmentNational Center for Computational Toxicology

Knudsen and Kleinstreuer. Birth Def Res C. 2012

AOP / MOA Targeted HTS DataProposed AOP: Embryonic Vascular Disruption

AOP / MOA Targeted High-throughput testing

ToxCast

• Combine High-throughput screening with computer models

11

Key Research and ToolsToxicity 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

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

Human Relevance/ Cost/Complexity

Throughput/ Simplicity

High-Throughput Screening Assays

10s-100s/yr

10s-100s/day

1000s/day

10,000s-100,000s/day

LTS HTSMTS uHTS

batch testing of chemicals for pharmacological/toxicological endpoints using automated liquid handling, detectors, and data acquisition

Gene-expression

14

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

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 mM in duplicate– 10 mM in duplicate (CYPs)

• Normalize data to assay window– % of control activity (central

reference – scalar reference)

16

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

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 mM high conc• 25 mM 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

NovaScreen replicas

50

60

70

80

90

100

5 10 20 30 40 50 60 70 80 90 100

% CUTOFF (solid)or MAD1 to MAD11 (dashed)

% s

ame

call

(320

6 ca

lls a

cros

s re

plic

as)

18

Example Curve Fits

hCYP 2C9 hERarAdrRa2B

hLynA Activator

hM1hKATPase

19

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

electrode

electrode

electrode

electrodewithout cell

electrode attachedwith a cell

electrode attachedwith 2 cells

Z = Z0

baseline impedance

Z = Z cell 1

impedance increased

cell

cell

electrode

Z = Z cell 2

impedance doubly increased

cells

electrode with 2 strongly-attached cells

Z = Z cell 3

impedance further increased

Z

Z

Z

Z

electrode

electrode

electrode

electrodewithout cell

electrode attachedwith a cell

electrode attachedwith 2 cells

Z = Z0

baseline impedance

Z = Z cell 1

impedance increased

cell

cell

electrode

Z = Z cell 2

impedance doubly increased

cells

electrode with 2 strongly-attached cells

Z = Z cell 3

impedance further increased

ZZZZ

ZZZZZ

ZZZZZ

ZZZZZ

20

Example Plots:

Data examples

Replicate Analysis:

21

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 mM 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

Multiplexed Reporter Gene Assay

Library of RTUs

Cell Transfection

PCR amplification

Transcription

Reverse transcription

RNA Isolation

Labeling

Processing (Hpa I)

Separation and detection (capillary electrophoresis)

XRE 2 RTU B

XRE 2 RTU B

RE 1X

RTU ARE 1X

RTU AX

RTU A

{

XRE 2 RTU B

XRE 2 RTU B

RE 1X

RTU ARE 1X

RTU AX

RTU A

X

X

XX

X

XX

Hpa I

AB

- +

X

X

XXX

XXX

XXX

Multiplexed Reporter Gene TechnologyCis: AhR

23

trans: ERa

cis: ERE

Bisphenol A HPTE

Corresponding cis and trans assays

24

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

BioSeek Assays Tested

26

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 PathwayActivation

OrganelleFunctions

Panel 1 design*:• Multiple mechanisms of toxicity• Acute, early & chronic exposure• 384-well capacity• HepG2

27

Cell Loss

Mitochondrial Membrane Potential

DNA Damage

Data Examples

28

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

HMGCS2Fatty acid metabolism

RXRPPARα

CYP2B6Xenobiotic, Steroid metabolism

RXRCARPB, Steroids, Xenobiotics(Reference Chemical: PB)

Fibrates, Xenobiotics(Reference Chemical: Fenofibric Acid)

CYP3A4Xenobiotic, Steroid metabolism

RXRPXRRIF, Bile Acids, Steroids, Xenobiotics(Reference Chemical: RIF)

CYP1A1/2Xenobiotic metabolism

AhR ARNTPAHs, Xenobiotics(Reference Chemical: 3-MC)

ABCB11 (BSEP)Bile acid metabolism and transport

RXRFXRBile Acids, Farnesoids(Reference Chemical: CDCA)

GAPDH CYP1A1

ABCB11 SULT2A1

HMGCS2

SLCO1B1 ACTIN

ABCB1 CYP1A2

GSTA2 CYP2B6

CYP2C9 UGT1A1 ABCG2

CYP2C19 CYP3A4

Target Gene CategoriesCYP450 (6)Transporter (4)Phase II Metabolism (3)Cholesterol Synthesis (1)Endogenous control (2)

29

Data ExamplesCYP1A1-AhR CYP2B6-CARHMGCS2-PPARα

30

NCGC Reporter Gene Assays

• Nuclear Receptors– GAL4 System (ligand detection assay)– 11 human receptors– 1 rat (PXR)– b-lactamase reporter gene assays except:– PXR assays are luciferase reporter gene assays

• p53 Reporter Gene assay– b-lactamase reporter gene assay

• Parental cell lines mostly HEK293 (also HeLa and DPX-2)

• 12-15 point concentration-response curves (single replicate)

31

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?

ERa

PPARg

Applications

33

Published Predictive Toxicity Models Predictive models: endpoints

liver tumors: Judson et al. 2010, Env Hlth Persp 118: 485-492hepatocarcinogenesis: Shah et al. 2011, PLoS One 6(2): e14584 cancer: Kleinstreuer et al. 2012, submittedrat fertility: Martin et al. 2011, Biol Reprod 85: 327-339rat-rabbit prenatal devtox: Sipes et al. 2011, Toxicol Sci 124: 109-127zebrafish vs ToxRefDB: Sipes et al. 2011, Birth Defects Res C 93: 256-267

Predictive models: pathwaysendocrine disruption: Reif et al. 2010, Env Hlth Persp 118: 1714-1720microdosimetry: Wambaugh and Shah 2010, PLoS Comp Biol 6: e1000756mESC differentiation: Chandler et al. 2011, PLoS One 6(6): e18540HTP risk assessment: Judson et al. 2011, Chem Res Toxicol 24: 451-462angiogenesis: Kleinstreuer et al. 2011, Env Hlth Persp 119: 1596-1603

Continuing To Expand & Validate Prediction Models Generally moving towards more mechanistic/AOP-based models

Predictive Model Development

11

Univariate Analysis

DATABASES

ToxCastDBin vitro

ToxRefDBin vivo

ASSAY SELECTION

ASSAY AGGREGATION

ASSAY SET REDUCTION

MULTIVARIATE MODEL

p-value statistics

Condense by gene, gene family, or pathway

Reduce by statistics (e.g. correlation)

LDAModel Optimization

x

35Martin et al 2011

Reproductive Rat Toxicity Model Features

36

36 AssaysAcross 8 Features

Balanced AccuracyTraining: 77%

Test: 74%

+-

Martin et al 2011

Reproductive Rat Toxicity Model Features

Example: Cancer SignaturesNon-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

37

Hallmarks of Cancer Hanahan and Weinberg (2000)

38

PPARa

p53

CCL2 ICAM1

Hallmarks of Cancer Hanahan and Weinberg (2011)

39

IL-1aIL-8CXCL10

Pathway Hits Raise Risk of Multiple Cancer Types

40

Hallmark-relatedADME-relatedEndpoint

Level 2: PreneoplasticLevel 3: Neoplastic

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)

41

SystemsModels

Beyond in vitro to in vivo signatures

42

Structure ClustersChemical Categories

In vitro Assays

Adverse Outcome

Pharmacokinetics

In Vitro-In Vivo Signatures

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

43

Cluster Assay Endpoint

Clusters 80% predictive of assay hit

44

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 …

EndosulfansSteroids

Office of Research and DevelopmentNational Center for Computational Toxicology

Adding PharmacokineticsReverse ToxicoKinetics (rTK)

Human Hepatocytes

(10 donor pool)

Add Chemical(1 and 10 mM)

Remove Aliquots at 15, 30, 60, 120 min

Analytical Chemistry

-5-4-3-2-10123

0 50 100 150

Ln C

onc

(uM

)

Time (min)

Nifedipine

1 uM initial

10 uM initial

Hepatic Clearance

HumanPlasma

(6 donor pool)

Add Chemical(1 and 10 mM)

Analytical Chemistry

Plasma Protein Binding

EquilibriumDialysis

45

Combine experimental data with PK Model to estimate dose-to-concentration scaling

Collaboration with Thomas et al.., Hamner InstitutesPublications: Rotroff et al, ToxSci 2010, Wetmore et al, ToxSci 2012

Etox

azole

Emam

ectin

Bupr

ofezin

Dibu

tyl ph

thalat

ePy

raclos

trobin

Parat

hion

Isoxa

ben

Pryri

thiob

ac-so

dium

Benta

zone

Prop

etamp

hos

2,4-D

S-Bi

oalle

thrin

MGK

Atraz

ine

Brom

acil

Feno

xyca

rbFo

rchlor

fenur

onMe

thyl P

arathi

onTr

iclos

anRo

tenon

eCy

prod

inil

Isoxa

flutol

eAc

etami

prid

Zoxa

mide

Diur

onBe

nsuli

de

Vinclo

zolin

Oxyte

tracy

cline

DH

Dicro

topho

sMe

tribu

zinTr

iadim

efon

Thiaz

opyr

Fena

miph

osCl

othian

idin

Bisp

heno

l-AAla

chlor

Aceto

chlor

Diaz

oxon

Dich

lorvo

sCh

lorpy

ripho

s-oxo

n

TriclosanPyrithiobac-sodium

log

(mg/

kg/d

ay)

Rotroff, et al. Tox.Sci 2010 Wetmore et al Tox Sci 2012

46

Range of in vitro AC50 values converted to human in vivo daily dose

Actual Exposure (est. max.)

margin

Combining in vitro activity and dosimetry

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

47

Initial Prioritization Application: EDSP21

Use high-throughput in vitro assays and modeling tools to prioritize chemicals for EDSP Tier 1 screening assays

48

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

49

Characterizing chemicals for estrogen signaling pathway activity

• Active vs. inactive• Potency and efficacy spectrum across assays• Agonist … Antagonist• Partial … full Agonist / Antagonist• ERa vs. ERb• Metabolically activated or deactivated• Cell type specificity• ER-mediated or not

50

All Data is preliminary and unpublished

Office of Research and DevelopmentNational Center for Computational Toxicology

Pro-ligand

ERActive ligand

Cofactor

ER-regulated gene expression

Cell proliferation

Oxidative stress

pathways

Non-ER-mediatedcell proliferation

pathways

Non-ligand-mediatedactivation of ER activity

Attagene AttageneNCGC ACEA

Odyssey Thera

Odyssey Thera

Novascreen

Using multiple lines of evidence to test for ER activity

Odyssey Thera and Attagene assays have metabolic capacity

Estrogen signaling pathway assays

52

source_name_aid source condition organism tissue Cell Format Cell TypeACEA_T47D ACEA human breast Cell line T47DATG_ERa_TRANS Attagene human liver Cell line HepG2ATG_ERE_CIS Attagene human liver Cell line HepG2Tox21_ERa_BLA_Agonist Tox21 human kidney Cell line HEK293TTox21_ERa_BLA_Antagonist Tox21 human kidney Cell line HEK293TTox21_ERa_LUC_BG1_Agonist Tox21 human ovarian Cell line BG1Tox21_ERa_LUC_BG1_Antagonist Tox21 human ovarian Cell line BG1NVS_NR_bER Novascreen bovine uterus tissue extract NVS_NR_hER Novascreen human breast Cell line: cell extract NVS_NR_mERa Novascreen mouse uterus tissue extract OT_ER_ERaERa Odyssey Thera +/- S9 human kidney Cell line HEK293TOT_ER_ERaERb Odyssey Thera +/- S9 human kidney Cell line HEK293TOT_ER_ERbERb Odyssey Thera +/- S9 human kidney Cell line HEK293TOT_ERa_GFPERa_ERE Odyssey Thera +/- S9 human cervix Cell line HeLa

OT_ERa_ERE_LUC_AgonistOdyssey Thera human Cell line: bulk

transiently transfected

CHO-K1

OT_ERa_ERE_LUC_AntagonistOdyssey Thera human Cell line: bulk

transiently transfected

CHO-K1

OT_ERb_ERE_LUC_AntagonistOdyssey Thera human Cell line: bulk

transiently transfected

CHO-K1

NCGC ER BG1-LUC vs. BLA Agonist Assays

54

Metabolic Capacity: +/- S9 for metabolism

Antagonist behavior in OT-PCA (ICI)

55

ERb-ERb

ERa-ERa

56

57

-S9+S9

Activation

58

-S9+S9

ERα/ERβ

Deactivation

59

White (-S9)Black (+S9)

Comparing Odyssey Theraassays across potent estrogens

Initial Exposure Evaluation:

Use CategoriesMapping Chemicals toUse Categories

Category hierarchy

Chemical to ProductThen

Product to CategoryChemical To

Category

Many sources of information on chemical use, mapped to categories

Laundry detergent, industrial solvent, baby care

Initial Exposure Evaluation:

Use CategoriesMapping Use Categoriesto Scenarios

Paint Food AdditivePesticide

Baby Adult

KitchenGarage

Multiple Category

Hierarchies

Map to Exposure Scenario Concepts

Map to Exposure Scenarios

Detailed Exposure and Toxicokinetics EvaluationModel 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

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

63

Detailed Exposure and Toxicokinetics Evaluation

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

64

Detailed Exposure and Toxicokinetics Evaluation

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

65

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

66

HTRA Basic Outline

1. Define molecular pathways linked to adverse outcomes2. 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 vivo5. Estimate population variability and uncertainty in PK and PD6. Estimate lower end of dose range for perturbation of pathway

67

HTRA-BPAD Key Ideas• HTRA = High Throughput Risk Assessment• BPAD = Biological Pathway Altering Dose• BPAC = Biological Pathway Altering Concentration• Css = 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/Css)– Incorporate variability and uncertainty

68

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 Css (concentration at steady state)–Units of mM/(mg/kg/day)

• Dose = Concentration / Css

• RTK (SimCyp) provides estimates of population variability• Need to add estimates of uncertainty

69

Estimate BPAD

• BPAD = BPAC / Css

• Each are modeled as being log-normal• BPAD has a population distribution, so take a protective level as the lower 99% tail (BPAD99)

• Add in uncertainty and take the lower 95% bound on BPAD99 to give a more protective lower bound –BPADL99

70

Adverse Effect

Toxicity Pathway

Key Events

MOA

HTS Assays

Intrinsic Clearance

Plasma Protein Binding

PopulationsPK Model

Biological Pathway Activating Concentration (BPAC)Probability Distribution

Dose-to-ConcentrationScaling Function (Css)Probability Distribution

Probability Distribution for Dose

that Activates Biological Pathway

BPAD

Pharmacodynamics Pharmacokinetics

R. Thomas et al. , Hamner Inst.

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

72

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

73

Conazole / CAR/PXR results

74

LEL, NEL

BPAD Range

Exposure estimate

“RfD”

HTRA Summary1. Select Toxicity-related pathways2. Develop assays to probe them3. Estimate concentration at which pathway is “altered” (PD)4. Estimate concentration-to-dose scaling (PK)5. Estimate PK and PD uncertainty and variability6. 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

75

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

76

Virtual Tissues

• Virtual Liver• Virtual embryo• Virtual Tissue Knowledgebase

78

Virtual Tissues Systems Models of Toxicity Pathways

chemicals pathways networks cell states tissue function

Quantitative Dose-Response

Models

Next GenerationRisk assessments

Moving beyond empirical models, to multi-scale models of

complex biological systems.

Identify Key Targets and Pathways For Prioritization

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

79

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

80

Data and Databases

• ACToR• ToxRefDB• ToxCastDB• ExpoCastDB

Too Many Chemicals Too Little Data (%)

EPA’s Need for Toxicity Data

0

10

20

30

40

50

60

Acute Cancer GentoxDev Tox Repro Tox

1

10

100

1000

10000

IRIS TRI PesticidesInerts CCL 1 & 2 HPVMPV

Judson, et al EHP (2009)

9912

83

ACToRAggregated Computational Toxicology Resource

Tabular Data,Links to Web Resources

Chemical ID, StructureChemical

Internet Searches

ACToR API

ToxRefDB

http://actor.epa.gov/

ToxMiner ExpoCastDB

In Vivo Study Data - OPP

ToxCast Data – NCCT, ORD, Collaborators(Currently Internal)

Exposure Data – NERL, NCCT(In Development)

ACToR Core

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)

ToxCastDB – ToxCast Data

• Links – Chemicals– Assays– Genes– Pathways– Endpoints

• Allows data analyses– Statistical associations– Biologically drive data mining

86

ExposureBackground Exposure

HumanEnvironment

Biomonitoring

Population

Uptake

N

N N

NH NH

Cl

Exposure Media

Contact

Products

Sources

Chemicals

HostSusceptibility

Biotransformation

ExposomeInformatics Approaches

Network Models

Knowledge Systems

Mechanistic Models

Exposure

Database

Distribution/Fate

Exposure Data: ExpoCastExposure Science for Prioritization and Toxicity Testing