In vitro

Report
Computational Toxicology
Richard Judson
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.
Office of Research and Development
National Center for Computational Toxicology
www.epa.gov/ncct
UNC, November 2012
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
Problem Statement
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)?
2
Computational Toxicology
Benefits
• Less expensive
• More chemicals screened
• Fewer animals
• Solution oriented
in vitro testing
• Innovative
Cancer
• Multi-disciplinary
ReproTox
• Collaborative
DevTox
• Catalytic
NeuroTox
PulmonaryTox
• Transparent
ImmunoTox
Bioinformatics/
Machine Learning
3
Initial Exposure
Evaluation:
Use Categories
Initial Objective:
Risk-based
Prioritization
Mixtures
HTS Chemical
Library
Chemicals with likely
exposure potential
Structure Similarity
Modeling
Chemical Universe
>100,000
Chemicals w/o
HTS or structural
similarity
Structural neighbors
to HTS library
AOP / MOA Targeted
High-throughput testing
Active chemicals and
structural neighbors
Detailed Exposure and
Toxicokinetics Evaluation
High, Medium, Low
priority bins
Office of Research and Development
National Center for Computational Toxicology
Inactive chemicals and
structural neighbors
Very Low
priority bin
AOP / MOA Targeted
High-throughput testing
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
Toxicity Pathways
Chemical
Receptors / Enzymes / etc.
Direct Molecular Interaction
Pathway Regulation /
Genomics
Cellular Processes
Tissue / Organ / Organism Tox Endpoint
6
AOP / MOA Development
AOP
AOP/ /MOA
MOATargeted
Targeted
High-throughput
HTS Datatesting
• 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
Proposed AOP: Embryonic Vascular Disruption
Office of Research and Development
National Center for Computational Toxicology
AOP
AOP/ /MOA
MOATargeted
Targeted
High-throughput
HTS Datatesting
Knudsen and
Kleinstreuer.
Birth Def Res
C. 2012
ToxCast
• Combine High-throughput screening with computer
models
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
10
Tox21 qHTS 10K Library
EPA
NCGC
– Drugs
– Drug-like
compounds
– Active
pharmaceutical
ingredients
•
Pesticides actives and
inerts
•
•
Industrial chemicals
•
OECD Molecular
Screening Working
Group
Endocrine Disruptor
Screening Program
•
FDA Drug Induced
Liver Injury Project
•
Failed Drugs
AOP
AOP/ /MOA
MOATargeted
Targeted
High-throughput
HTS Datatesting
NTP
•
•
NTP-studied compounds
•
NICEATM/ICCVAM
validation reference
compounds for regulatory
tests
•
External collaborators
(e.g., Silent Spring
Institute, U.S. Army Public
Health Command)
•
Formulated mixtures
NTP nominations and
related compounds
High-Throughput Screening Assays
batch testing of chemicals for pharmacological/toxicological endpoints
using automated liquid handling, detectors, and data acquisition
LTS
MTS
HTS
uHTS
Gene-expression
1000s/day
10s-100s/yr
10s-100s/day
10,000s100,000s/day
Human Relevance/
Cost/Complexity
Throughput/
Simplicity
12
High Throughput Screening 101
Chemical Exposure
96-, 384-, 1536 Well Plates
HTS Robotic Platform
Cell Population
Pathway
Assay Target Biology
(e.g., Estrogen Receptor)
13
HTS: High Throughput Screening
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 substrateintensity quench
– Fluorescent enzyme substratemobility shift
• Initial screening:
– 25 mM in duplicate
– 10 mM in duplicate (CYPs)
• Normalize data to assay
window
– % of control activity (central
reference – scalar reference)
14
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
15
Biochemical Concentration-Response Testing
NovaScreen replicas
• 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-
% same call
(3206 calls across replicas)
100
90
80
70
60
50
5
10
20
30
40
50
60
70
80
90
100
% CUTOFF (solid)
or MAD1 to MAD11 (dashed)
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
16
Example Curve Fits
rAdrRa2B
hCYP 2C9
hLynA
Activator
hERa
hM1
hKATPase
17
Real Time Cell Growth Kinetics
• Cytotoxicity with potential
mechanistic interpretation
• Human A549 lung carcinoma cell
line
Z = Z0
electrode
baseline
impedance
Z = Z cell 1
electrode
electrode attached
with a cell
Z
impedance
increased
cell
cells
Z = Z cell 2
electrode
electrode attached
with 2 cells
– 8 conc/3-fold serial dilutions
– Duplicate wells
• Real-time measuremens during
Z
cell
– ACEA experience with line
– Reference compound effects
• Concentration-response testing
electrode
without cell
Z
impedance doubly
increased
Z = Z cell 3
Z
electrode with 2
strongly-attached cells
impedance further
increased
exposure (0-72 hr)
• IC50 and LELs calculated
18
Data examples
Replicate Analysis:
Example Plots:
19
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
20
Multiplexed Reporter Gene Technology
Cis: AhR
21
Corresponding cis and trans assays
Bisphenol A
HPTE
trans: ERa
cis: ERE
22
BioSeek: BioMAP® Technology Platform
Assays
Profile Database
Informatics
LPS
BF4T
SM3C
Human primary cells
Disease-like culture
conditions
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
23
BioSeek Assays Tested
24
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
Stress Pathway
Activation
Organelle
Functions
Oxidative
Stress
Panel 1 design*:
• Multiple mechanisms of toxicity
• Acute, early & chronic exposure
• 384-well capacity
• HepG2
DNA Damage
Cell Cycle
CSK Integrity
25
Data Examples
Cell
Loss
Mitochondrial
Membrane
Potential
DNA
Damage
26
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
27
Data Examples
CYP1A1-AhR
HMGCS2-PPARα
CYP2B6-CAR
28
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)
29
NCGC: Data Calculations
ERa
• 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?
PPARg
30
Applications
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
32
Predictive Model Development
DATABASES
ToxCastDB
in vitro
x
ToxRefDB
in vivo
ASSAY SELECTION
Univariate Analysis
p-value statistics
ASSAY AGGREGATION
Condense by gene, gene
family, or pathway
ASSAY SET REDUCTION
Reduce by statistics (e.g.
correlation)
MULTIVARIATE MODEL
LDA
Model Optimization
11
Reproductive Rat Toxicity
Model Features
Martin et al 2011
34
Reproductive Rat Toxicity
Model Features
36 Assays
Across 8 Features
+
Balanced Accuracy
Training: 77%
Test: 74%
Martin et al 2011
35
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
Hallmarks of Cancer
Hanahan and Weinberg (2000)
PPARa
p53
CCL2
ICAM1
37
Hallmarks of Cancer
Hanahan and Weinberg (2011)
IL-1a
IL-8
CXCL10
38
Pathway Hits Raise Risk of Multiple Cancer Types
Level 2: Preneoplastic
Level 3: Neoplastic
Hallmark-related
ADME-related
Endpoint
39
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:
Systems
Models
– 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
Beyond in vitro to in vivo signatures
In vitro
Assays
Structure Clusters
Chemical Categories
Pharmacokinetics
Adverse
Outcome
In Vitro-In Vivo Signatures
41
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
Cluster
Assay
Endpoint
42
Data Set Incomplete
Chemical Set 1
Clusters 80% predictive of assay hit
Chemical Set 2
ER Assays
Assays
Steroids
Estrogens
Endosulfans
CYP Binding Assays
Inflammation
Assays
Azoles
Alkyl Phenols
Conazoles
Tetracycline …
Surfactants
Alachlor …
Surfactants
Captan …
43
GPCR Binding Assays
Adding Pharmacokinetics
Reverse ToxicoKinetics (rTK)
Nifedipine
3
Ln Conc (uM)
2
1
0
-1
-2
1 uM initial
-3
10 uM initial
-4
-5
0
50
100
150
Time (min)
Human
Hepatocytes
(10 donor pool)
Add Chemical
(1 and 10 mM)
Remove
Aliquots at 15,
30, 60, 120 min
Analytical
Chemistry
Hepatic
Clearance
Plasma Protein
Binding
Human
Plasma
(6 donor pool)
Add Chemical
(1 and 10 mM)
Equilibrium
Dialysis
Analytical
Chemistry
Combine experimental data with PK Model to estimate
dose-to-concentration scaling
Collaboration with Thomas et al.., Hamner Institutes
Office of Research andRotroff
Development et al, ToxSci 2010, Wetmore et al, ToxSci 2012
Publications:
National Center for Computational Toxicology
44
Rotroff, et al. Tox.Sci 2010
Etoxazole
Emamectin
Buprofezin
Dibutyl phthalate
Pyraclostrobin
Parathion
Isoxaben
Pryrithiobac-sodium
Bentazone
Propetamphos
2,4-D
S-Bioallethrin
MGK
Atrazine
Bromacil
Fenoxycarb
Forchlorfenuron
Methyl Parathion
Triclosan
Rotenone
Cyprodinil
Isoxaflutole
Acetamiprid
Zoxamide
Diuron
Bensulide
Vinclozolin
Oxytetracycline DH
Dicrotophos
Metribuzin
Triadimefon
Thiazopyr
Fenamiphos
Clothianidin
Bisphenol-A
Alachlor
Acetochlor
Diazoxon
Dichlorvos
Chlorpyriphos-oxon
log (mg/kg/day)
Combining in vitro activity and dosimetry
Range of in vitro AC50
values converted to human
Triclosan
Pyrithiobac-sodium
in vivo daily dose
margin
Actual Exposure (est. max.)
Wetmore et al Tox Sci 2012
45
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
Initial Prioritization Application: EDSP21
Use high-throughput in vitro assays and modeling tools
to prioritize chemicals for EDSP Tier 1 screening assays
47
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
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
All Data is preliminary and unpublished
49
Using multiple lines of evidence to test for ER activity
Pro-ligand
Active ligand
ER
Odyssey Thera and
Attagene assays
have metabolic
capacity
Novascreen
Odyssey
Thera
Oxidative
stress
pathways
Non-ligandmediated
activation of
ER activity
Non-ER-mediated
cell proliferation
pathways
Cofactor
Odyssey
Thera
ER-regulated
gene expression
Attagene
Office of Research and Development
National Center for Computational Toxicology
Attagene
NCGC
Cell
ACEA
proliferation
Estrogen signaling pathway assays
source
ACEA
ACEA_T47D
Attagene
ATG_ERa_TRANS
Attagene
ATG_ERE_CIS
Tox21
Tox21_ERa_BLA_Agonist
Tox21
Tox21_ERa_BLA_Antagonist
Tox21
Tox21_ERa_LUC_BG1_Agonist
Tox21_ERa_LUC_BG1_Antagonist Tox21
Novascreen
NVS_NR_bER
Novascreen
NVS_NR_hER
Novascreen
NVS_NR_mERa
Odyssey Thera
OT_ER_ERaERa
Odyssey Thera
OT_ER_ERaERb
Odyssey Thera
OT_ER_ERbERb
Odyssey Thera
OT_ERa_GFPERa_ERE
Odyssey Thera
source_name_aid
condition
+/- S9
+/- S9
+/- S9
+/- S9
organism
human
human
human
human
human
human
human
bovine
human
mouse
human
human
human
human
human
OT_ERa_ERE_LUC_Agonist
Odyssey Thera
human
Odyssey Thera
human
OT_ERa_ERE_LUC_Antagonist
OT_ERb_ERE_LUC_Antagonist
tissue
breast
liver
liver
kidney
kidney
ovarian
ovarian
uterus
breast
uterus
kidney
kidney
kidney
cervix
Cell Format
Cell line
Cell line
Cell line
Cell line
Cell line
Cell line
Cell line
tissue extract
Cell line: cell extract
tissue extract
Cell line
Cell line
Cell line
Cell line
Cell line: bulk
transiently
transfected
Cell line: bulk
transiently
transfected
Cell line: bulk
transiently
transfected
Cell Type
T47D
HepG2
HepG2
HEK293T
HEK293T
BG1
BG1
HEK293T
HEK293T
HEK293T
HeLa
CHO-K1
CHO-K1
CHO-K1
51
1e-03
1e-06
1e-09
Strong
Moderate-Strong
Moderate
Weak
Very Weak
Negative-Weak
Negative
Antagonist
Unknown
1e-12
AC50 (Tox21_ERa_LUC_BG1_Agonist) M
NCGC ER
BG1-LUC vs. BLA Agonist
Tox21_ERa_BLA_Agonist_ratio
Assays Tox21_ERa_LUC_BG1_Agonist
1e-12
1e-09
1e-06
1e-03
AC50 (Tox21_ERa_BLA_Agonist_ratio) M
Metabolic Capacity: +/- S9 for metabolism
53
Antagonist behavior in OT-PCA (ICI)
ERb-ERb
ERa-ERa
54
55
-S9
+S9
Activation
56
ERα/ERβ
-S9
+S9
Deactivation
57
Comparing Odyssey
Thera
assays across potent
estrogens
White (-S9)
Black (+S9)
58
Mapping Chemicals to
Use Categories
Initial Exposure
Evaluation:
Use Categories
Many sources of information on
chemical use, mapped to categories
Chemical to Product
Then
Product to Category
Chemical
To
Category
Laundry detergent,
industrial solvent,
baby care
Category hierarchy
Initial Exposure
Evaluation:
Use Categories
Mapping Use Categories
to Scenarios
Map to Exposure
Scenarios
Paint
Pesticide
Baby
Garage
Food Additive
Map to Exposure
Scenario Concepts
Adult
Kitchen
Multiple
Category
Hierarchies
Model Detailed Exposure
and Toxicokinetics
Detailed Exposure and
Toxicokinetics Evaluation
• 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
Detailed Exposure and
Toxicokinetics Evaluation
High Throughput Fate Predictions
Clustering 1763
chemicals by the media
into which they partition
most
Could infer behavior of
understudied chemicals
from similar, well-known
counterparts – “fate readacross
62
High-Throughput Risk
Assessment (HTRA)
Detailed Exposure and
Toxicokinetics Evaluation
• 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
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
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
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
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
67
Estimating the concentration-todose 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
68
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
69
Pharmacodynamics
Pharmacokinetics
Dose-to-Concentration
Scaling Function (Css)
Probability Distribution
Adverse Effect
MOA
Key Events
Toxicity Pathway
BPAD
HTS Assays
Probability Distribution
for Dose
that Activates
Biological Pathway
PK Model
Biological Pathway Activating
Concentration (BPAC)
Probability Distribution
Intrinsic
Clearance
Populations
Plasma Protein
Binding
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
71
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
Conazole / CAR/PXR results
BPAD Range
“RfD”
Exposure estimate
LEL, NEL
73
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
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
Virtual Tissues
• Virtual Liver
• Virtual embryo
• Virtual Tissue Knowledgebase
Virtual Tissues
Systems Models of Toxicity Pathways
chemicals
pathways
Moving beyond
empirical models, to
multi-scale models of
complex biological
systems.
networks
cell states
tissue function
Identify Key Targets and Pathways
For Prioritization
Quantitative
Dose-Response
Models
Next Generation
Risk assessments
77
Virtual Liver
• Cell-based computer model simulates chemical actions in virtual
liver to estimate how much chemical it takes to lead to healthrelated 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
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
Data and Databases
• ACToR
• ToxRefDB
• ToxCastDB
• ExpoCastDB
EPA’s Need for Toxicity Data
Too Many Chemicals
9912
Too Little Data (%)
60
10000
50
1000
40
30
100
20
10
10
1
0
IRIS
TRI
Pesticides
Acute
Cancer
Inerts
CCL 1 & 2
HPV
Dev Tox
Repro Tox
Gentox
MPV
Judson, et al EHP (2009)
ACToR
Aggregated Computational Toxicology Resource
http://actor.epa.gov/
ACToR API
Chemical
ACToR Core
Internet
Searches
Tabular Data,
Links to Web
Resources
ToxRefDB
In Vivo Study
Data - OPP
Chemical ID,
Structure
ToxMiner
ExpoCastDB
ToxCast Data –
Exposure Data –
NCCT, ORD,
NERL, NCCT
Collaborators
(In Development)
(Currently Internal)
82
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
Exposure Data: ExpoCast
Exposure Science for Prioritization and Toxicity Testing
Environment
Sources
Human
Population
Distribution/Fate
Cl
N
Products
NH
N
N
NH
Exposure
Biotransformation
Chemicals
Biomonitoring
Host
Susceptibility
Exposure
Database
Mechanistic
Models
Informatics
Approaches
Knowledge
Systems
Network
Models
Exposome
85

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