Download (eg, ER binding training set (TrSet))

Designing a QSAR
for ER Binding
Defining Toxicity Pathways
Across Levels of Biological Organization:
Direct Chemical Binding to ER
QSAR
Xenobiotic
MOLECULAR
ER
Binding
Toxicological
Understanding
In vitro Assays
CELLULAR
Altered
Protein
Expression
TISSUE/ORGAN
Altered
Hormone
Levels,
Ova-testis
In vivo Assays
INDIVIDUAL
Chg 2ndry
Sex Char,
Altered
Repro.
POPULATION
Skewed
Sex
Ratios,
Altered
Repro.
Risk Assessment
Relevance
QSARs for Prioritization
What:
• Prioritize chemicals based on ability to bind ER (plausibly
linked to adverse effect)
• Determine which untested chemicals should be tested in
assays that will detect this activity, prioritized above very low
risk chemicals for this effect
• Demonstrate how QSARs are built, for complex problems,
and are useful to regulators/risk assessors
Why:
•To provide EPA with predictive tools for prioritization of testing
requirements and enhanced interpretation of exposure,
hazard identification and dose-response information
•Develop the means to knows what to test, when to test, how
•FQPA - Little of no data for most inerts/antimicrobials; short
timeline for assessments;
Lessons Learned from early EPA exercise
1) High quality data is critical and should not be assumed
– Models can be no better than the data upon which they are
formulated
– Assays should be optimized to determine the adequacy for the
types of chemicals found within regulatory lists
• Assumption that assays adequate for high-medium potency chemicals
will detect low potency chemicals warrants careful evaluation
– Mechanistic understanding should be sought; new information
incorporated when available
• Assumption that ER binding mechanism was well understood
warrants careful evaluation
2) Defining a regulatory domain is not a trivial exercise
– Assumption that ~6000 HPVCs would represent additional
regulatory domains needs careful evaluation; regulatory lists need
to be defined
– Structure verification is needed for all chemicals on regulatory
lists
3) Determining coverage of regulatory domain is non-trivial
– Using a TrSet of “found” data (which included few chemicals
structures found in regulatory domain) proved to be inadequate to
complete QSAR development
– QSAR development is an iterative process that requires
systematic testing within regulatory domain of interest
Prioritizing EDC Risk Assessment Questions within Large Chemical Inventories
Developing Predictive Models is an Iterative Process
Evaluate Regulated Chemicals
For Ability to Initiate Pathway
(e.g., ER binding training set (TrSet))
Elucidate Toxicity Pathway
(e.g., ER binding to repro effects)
Initial
TrSet
(CERI/RAL)
High Quality
Data
Strategic
Chemical
Selection
Undefined
Chemical
Inventory
Evaluate
TrSet Coverage
Of Inventory
Structural
Requirements
QSAR
Model
Regulatory
Acceptance
Criteria
Estimation of Missing Data
Analogue Identification
Prioritization/Ranking
QSAR Libraries
Modeling Engine
Prioritizing EDC Risk Assessment Questions within Large Chemical Inventories
Developing Predictive Models is an Iterative Process
Evaluate Regulated Chemicals
For Ability to Initiate Pathway
(e.g., ER binding training set (TrSet))
Elucidate Toxicity Pathway
(e.g., ER binding to repro effects)
Initial
TrSet
(MED)
High Quality
Data
Strategic
Chemical
Selection
OPP Inventory
Evaluate
TrSet Coverage
Of Inventory
Structural
Requirements
Directed/designed
Training Set
QSAR
Model
Regulatory
Acceptance
Criteria
Estimation of Missing Data
Analogue Identification
Prioritization/Ranking
QSAR Libraries
Modeling Engine
HOW to test?
High quality data is critical
– Assays should be optimized to determine
the adequacy for the types of chemicals
on the relevant regulatory list
• Test assays on low potency chemicals
• Test to solubility
MED Database
Focus on Molecular Initiating Event
1) rtER binding is assessed using a standard competitive
binding assay;
-chemicals are tested to compound solubility limit in the
assay media;
2) equivocal binding curves are interpreted using a higherorder assay (gene activation and vitellogenin mRNA production
in metabolically competent trout liver slices)
1000
100
10
1
0.0001
0.001
0.01
0.1
1
10
100
0.1
0.01
0.001
0.0001
rat ER vs rainbow trout ER for 55 chemicals
1000
[3H]-E2 Binding (%)
110
100
90
80
70
60
50
40
30
20
10
0
RBA (%)
E2
100
DES
179
OHTAM 35
GEN
1.7
pNP
0.046
KMF
0.030
RES
0.0006
TBS
BBC
BAM
0 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1
Concentration (Molar)
NB
NB
NB
Binding Assays
RBA %
p-tert-octylphenol
solubility limit
100
350
E2 rbtER (cyto)
PTOP rbtER (cyto)
325
E2 hER (recomb-full) FP
PTOP hER (recomb-full) FP
E2 hER (recomb-LBD)
PTOP hER (recomb-LBD)
90
300
70
275
60
250
50
225
40
30
200
20
175
10
150
CR
TL
0
-10 -9 -8 -7 -6 -5 -4
Log Concentration (M)
-3
125
-2
Polarization (mp)
Binding (%)
80
0.075
0.253
0.124
VTG Gene Activation
Vtg mRNA (copy #/400 ng total RNA)
1.0×10 8
1.0
p-tert-octylphenol
Estradiol
Control
1.0×10 7
0.9
0.8
0.7
1.0×10 6
0.6
0.5
1.0×10 5
0.4
0.3
1.0×10 4
0.2
0.1
1.0×10 3
CT
R
L
0.0
-10 -9
-8
-7
-6
-5
-4
-3
-2
Log Concentration (M)
Concentration dependent vitellogenin (VTG) gene expression as VTGmRNA
production in male rainbow trout liver slices exposed to p-t-octylphenol for 48 hrs
(Mean + STDS, n=5).
Binding Assays
RBA %
p-n-octylphenol
solubility limit
100
350
E2 rbtER (cyto)
PNOP rbtER (cyto)
325
E2 hER (recomb-full)FP
PNOP hER (recomb-full)FP 0.173
E2 hER (recomb-LBD)
PNOP hER (recomb-LBD) ND
90
300
70
275
60
250
50
225
40
30
200
20
175
10
150
CR
TL
0
-10 -9 -8 -7 -6 -5 -4
Log Concentration (M)
-3
125
-2
Polarization (mp)
Binding (%)
80
0.027
Binding Assays
RBA %
4-n-butylaniline
solubility limit
100
90
70
300
E2 hER (recomb-LBD)
BA hER (recomb-LBD)
275
60
250
50
40
225
30
20
200
10
175
CR
TL
0
-10 -9 -8 -7 -6 -5 -4
Log Concentration (M)
-3
150
-2
Polarization (mp)
Binding (%)
80
325
E2 rbtER (cyto)
0.0004
BA rbtER (cyto)
E2 hER (recomb-full)FP
BA hER (recomb-full)FP 0.007
350
NB
VTG Gene Activation
1.0×10 7
1.0
4-n-butylaniline
Estradiol
Control
0.9
0.8
0.7
1.0×10 6
0.6
1.0×10 5
0.5
0.4
1.0×10 4
0.3
0.2
1.0×10 3
0.1
1.0×10 2
CT
R
L
Vtg mRNA (copy #/400 ng total RNA)
1.0×10 8
0.0
-10 -9
-8
-7
-6
-5
Log Concentration (M)
4-n-butylaniline
(Mean + STDS, n=5)
-4
-3
-2
Binding Assays
4,4'-sulfonyldiphenol
110
solubility limit
100
RBA %
90
Binding (%)
80
70
60
E2 rbtER (cyto)
SDP rbtER (cyto)
0.0020
E2 hER (recomb-LBD)
SDP hER (recomb-LBD)
0.0055
50
40
30
20
10
0
-10
-11
-10
-9
-8
-7
-6
-5
-4
-3
Log Concentration (M)
-2
-1
Binding Assays
ethylparaben
solubility limit
RBA %
90
E2 rbtER (cyto)
EP rbtER (cyto)
0.0008
80
E2 hER(recomb-LBD)
EP hER (recomb-LBD)
ND
70
60
50
40
30
20
10
0
CR
TL
Binding (%)
100
-10 -9 -8 -7 -6 -5 -4
Log Concentration (M)
-3
-2
Binding Assays
resorcinol sulfide
350
RBA %
100
E2 rbtER (cyto)
RES rbtER (cyto)
325
90
300
70
275
60
250
50
225
40
30
200
20
175
10
150
CR
TL
0
-10 -9 -8 -7 -6 -5 -4
Log Concentration (M)
-3
125
-2
E2 hER (recomb-full)FP
RES hER (recomb-full)FP 0.0098
Polarization (mp)
Binding (%)
80
0.00057
VTG Gene Activation
Vtg mRNA (copy #/400 ng total RNA)
1.0×10 8
1.0×10 7
1.0
Resorcinol sulfide
Estradiol
Control
0.9
0.8
1.0×10 6
0.7
1.0×10 5
0.6
0.5
1.0×10 4
0.4
0.3
1.0×10 3
0.2
1.0×10 2
CT
R
L
1.0×10 1
0.1
0.0
-10 -9
-8
-7
-6
-5
-4
-3
-2
Log Concentration (M)
resorcinol sulfide
(Mean + STDS, n=5; dashed line indicates toxic concentrations).
WHAT to test?
Data collected needs to address the
problem
• Expand training set to cover types of
chemicals on the relevant regulatory
lists
Prioritizing EDC Risk Assessment Questions within Large Chemical Inventories
Developing Predictive Models is an Iterative Process
Evaluate Regulated Chemicals
For Ability to Initiate Pathway
(e.g., ER binding training set (TrSet))
Elucidate Toxicity Pathway
(e.g., ER binding to repro effects)
Initial
TrSet
(MED)
High Quality
Data
Strategic
Chemical
Selection
OPP Inventory
Evaluate
TrSet Coverage
Of Inventory
Structural
Requirements
Directed/designed
Training Set
QSAR
Model
Regulatory
Acceptance
Criteria
Estimation of Missing Data
Analogue Identification
Prioritization/Ranking
QSAR Libraries
Modeling Engine
2) Defining a regulatory domain is not a
trivial exercise
3) Determining coverage of regulatory
domain is non-trivial
– Using a TrSet of “found” data (which
included few chemicals structures found
in regulatory domain) proved to be
inadequate to complete QSAR
development
– QSAR development is an iterative process
that requires systematic testing within
regulatory domain of interest
Define the Problem:
Food Use Pesticide Inerts
List included:
937 entries
-(36 repeats + 8 invalid CAS#)
893 entries
893 entries = 393 discrete + 500 non-discrete substances
(44% discrete : 56% non-discrete)
393 discrete chemicals include:
organics
inorganics
organometallics
500 non-discrete substances include:
147 polymers of mixed chain length
170 mixtures
183 undefined substances
OPP Chemical Inventories
Total
Discrete
Defined
Mixtures
Polymers
Undefined
Substance
Food Use
Inerts
893
393
170
147
183
Antimicrobials
224
169
27
6
22
Sanitizers
104
69
10
19
6
Antimicrobials
+ Sanitizers
299
211
35
25
28
HPV
IUR 2002
2708
1605
284
50
769
Total Inerts*
2891
1462
155
579
695
1110
873
33
10
194
Chemical
Category
(OPP website,
Aug 2004)
Registered
Pesticide
Active
Ingredients*
* Structure verification in progress
Prioritizing EDC Risk Assessment Questions within Large Chemical Inventories
Developing Predictive Models is an Iterative Process
Evaluate Regulated Chemicals
For Ability to Initiate Pathway
(e.g., ER binding training set (TrSet))
Elucidate Toxicity Pathway
(e.g., ER binding to repro effects)
Initial
TrSet
(MED)
High Quality
Data
Strategic
Chemical
Selection
OPP Inventory
Evaluate
TrSet Coverage
Of Inventory
Structural
Requirements
Directed/designed
Training Set
QSAR
Model
Regulatory
Acceptance
Criteria
Estimation of Missing Data
Analogue Identification
Prioritization/Ranking
QSAR Libraries
Modeling Engine
Original ER Binding Training Sets
• Initial focus of ER binding data sets from 1990s - 2004:
– Steroids, anti-estrogens (high potency binders)
– Organochlorines
– Alkylphenols
CERI
hER
NCTR
rER
MED
rtER
Food
Use
Inerts
Antimicrobial
HPV
Inerts
HPV
TSCA
Steroid,
Anti-E2,
OrganoCl
150
(30%)
91
(40%)
37
2
(<1%)
2
(1%)
6
(1%)
178
(3%)
Alkylphenols
35
(7%)
13
(6%)
22
3
(1%)
7
(3%)
6
(1%)
71
(1%)
Covered
groups as
% of total
37%
46%
2%
4%
2%
4%
Building New Training Sets
• New inventories
– Food Use Inerts
– Antimicrobials and Sanitizers
– HPV inerts
– Total Inerts
– HPV TSCA chemicals
CERI
(hER)
NCTR
(rER)
ORDMED
(rtER)
Food
Use
Inerts
A/S
HPV
Inerts
HPV
TSCA
Acyclics
3
(0.6%)
6
(2.6%)
22
(10%)
230
(59%)
121
(57%)
291
(65%)
2655
(41%)
Aromatic
Sulfates
4
(0.8%)
1
(0.4%)
15
88
(22%)
6
(3%)
15
(3%)
347
(5%)
Prioritizing EDC Risk Assessment Questions within Large Chemical Inventories
Developing Predictive Models is an Iterative Process
Evaluate Regulated Chemicals
For Ability to Initiate Pathway
(e.g., ER binding training set (TrSet))
Elucidate Toxicity Pathway
(e.g., ER binding to repro effects)
Initial
TrSet
(MED)
High Quality
Data
Strategic
Chemical
Selection
OPP Inventory
Evaluate
TrSet Coverage
Of Inventory
Structural
Requirements
Directed/designed
Training Set
QSAR
Model
Regulatory
Acceptance
Criteria
Estimation of Missing Data
Analogue Identification
Prioritization/Ranking
QSAR Libraries
Modeling Engine
QSAR Principles for ER interactions
• Chemical are “similar” if they produce the same
biological action from the same initiating event
– Not all chemicals bind ER in same way, i.e., not all
“similar”
– ER binders are “similar” if they have the same type of
interaction within the receptor
• QSARs require a well-defined/well understood biological
system; assay strengths and limitations understood
• QSARs for large list of diverse chemicals
– require iterative process – test, hypothesize,
evaluate, new hypothesis, test again, etc.
– to gain mechanistic understanding to group similar
acting chemicals; build model within a group
Estrogen binding pocket
schematic representation
T 347
C
E 353
H 524
A
R 394
J. Katzenellenbogen
B
A-B Mechanism
T 347
C
E 353
H
A
H 524
CH3 H
HO
B
OH
R 394
H H
Distance = 10.8 for 17-Estradiol
Probability density .
A-B Mechanism
T 347
C
Distance .
Based on 39 CERI Steroidal Structures
E 353
H
A
HO
B
OH
R 394
H H
9.73<Distance<11.5
Akahori; Nakai (CERI)
H 524
CH3 H
Probability density .
A-C Mechanism
T 347
C
Distance .
OH
Based on 21 RAL A-C Structures
H 524
E 353
A
HO
R 394
Katzenellenbogen
B
Probability density .
A-B-C Mechanism
T 347
C
OH
Distance .
Based on 66 RAL A-B-C Structures
N
E 353
A
HO
R 394
Katzenellenbogen
N
OH
B
H 524
Hypothesis testing
Hypothesis: Chemicals with
interatomic distance between
O-atoms satisfying distance
criteria for a binding type have
the potential to bind ER based
on electronic interactions.
•
Hypothesize structural
parameter(s)
associated with toxicity
•
Select chemicals that
satisfy the hypothesis
•
Test, and confirm or
modify hypothesis
• Because acyclics are > 50% of inventories, what is the
possibility that any acyclics satisfy criteria of high affinity
binding types?
• Selected acyclics for testing that met A_B distance; no binders
found (charged cmpds – apparent binding but no activation)
• As suspected, most OPP chemicals could not be evaluated with
the A_B or A_C mechanism models;
• Need to refine ER binding hypotheses to investigate additional
binding types
– Chemicals interact with ER in more than one way, influencing
data interpretation and model development;
– Need to group chemicals by like activity, then attempt to
model as a group that initiate action through same chemicalbiological interaction mechanism, and should have common
features
– Find common features and predict which other untested
chemicals may have similar activity – prioritize for testing
HOW to interpret test results?
High quality data is critical
• ER binding hypotheses refined
– Chemicals interact with ER in more than
one way, influencing data interpretation
and model development
A-B Mechanism
T 347
C
E 353
H
A
H 524
CH3 H
HO
B
OH
R 394
H H
Distance = 10.8 for 17-Estradiol
CH3
OH
QOxygen=-0.318
H
A
H
B
H
QOxygen=-0.253
HO
-0.225
-0.245
Local O or N charge
-0.265
-0.285
Alkyl Phenols
Alkyl Anilines
RAL - AC
-0.305
-0.325
-0.345
-0.365
0
1
2
3
4
5
Log(Kow)
6
7
8
9
A Mechanism
T 347
C
E 353
H 524
A
B
HO
R 394
CH3
B Mechanism
T 347
C
E 353
H 524
A
NH2
R 394
H3C
B
MED Trout Alkyl Phenols
1
0
1
2
3
4
6
MED
Trout5Alkyl Phenols
7
8
AP p-n-chain
AP p-(t or s)-branched
AP o-(t or s)-branched
AP m-t-branched
0.1
1
AP p-n-chain
AP p-(t or s)-branched
AP o-(t or s)-branched
AP m-t-branched
0.01
0.8
0.7
0.001
0.6
Active
log(RBA)
0.9
0.5
0.4
0.0001
0.3
0.2
0.00001
log(KOW)
0.1
0
0
1
2
3
4
log(KOW)
5
6
7
8
MED Trout Alkylphenols
1
0
1
2
3
4
5
6
MED
Trout
A-type
7
8
alkyl
0.1
1
hind
Alky
0.01
0.8
alkyl
0.7
Alkyl
hinde
0.001
0.6
Active
log(RBA)
0.9
0.5
0.4
0.0001
0.3
0.2
0.00001
log(KOW)
0.1
0
0
1
2
3
4
log(KOW)
5
6
7
8
MED Trout
1
0
1
2
3
4
5
MED Trout
6
7
8
0.1
1.2
alkyl phenols
parabens salicylates
1
0.01
parabens - trihydroxy
alkyl phenols
parabens
0.8
parabens salicylates
0.001
Active
log(RBA)
parabens
parabens - trihydroxy
0.6
0.0001
0.4
0.00001
0.2
log(KOW)
0
0
1
2
3
4
log(KOW)
5
6
7
8
Anilines & Phthalates
1
0
1
2
3
4
5
Anilines & Phthalates
6
7
8
0.1
1
alkyl anilines
phthalates
0.01
0.8
0.7
alkyl anilines
phthalates
0.6
0.001
Active
log(RBA)
0.9
0.5
0.4
0.0001
0.3
0.2
0.00001
log(KOW)
0.1
0
0
1
2
3
4
log(KOW)
5
6
7
8
CH3 H
OH
QOxygen=-0.318
H
H
H
QOxygen=-0.253
HO
-0.225
-0.245
Local O or N charge
-0.265
Alkyl Phenols
Alkyl Anilines
Parabens
Phtalates
RAL - AC
-0.285
-0.305
-0.325
-0.345
-0.365
0
1
2
3
4
5
Log(Kow)
6
7
8
9
MED Trout
1000
MED Trout
100
1.2
10
AB
AC
log(RBA)
1
1
DDT
0
1
2
3
4
5
6
7
8
A-type
B-Type
AB
0.1
AC
0.8
DDT
0.01
Active
A-type
B-Type
0.6
0.001
0.4
0.0001
0.00001
0.2
log(KOW)
0
0
1
2
3
4
log(KOW)
5
6
7
8
Yes
Chemical
Universe
Contains Cycle
No
Contains two
or more nucleophilic
Sites (O or N)
Yes
Possible High Affinity,
A-B; A-C; or
A-B-C type binder
No
Non binder
(RBA<0.00001)
Other Mechanisms
Steric
Exclusion Parameter
Attenuation?
Yes
No
No
Non binder
(RBA<0.00001)
Yes
Activity Range
log KOW <1.4
Yes
No
A_Type Binder
B_Type Binder
High Binding Affinity
A-B; A-C; or A-B-C type
No
A
RBA=a*logP +b
•Alkyl Phenols
•Benzoate
•Parabens
•Benzketones
Classes with special
structural rules
A or B type ?
B
No
Yes
Undefined decision
parameter?
RBA=a*logP +b
•Anilines
•Phthalates
Yes
Undefined decision
parameter?
Significant
Binding Affinity
Non binder
(RBA<0.00001)
Non binder
Ex: Progesterone
No
Corticossterone
(RBA<0.00001)
Low Affinity Binder
A-B; A-C; or A-B-C type
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Initiating Events
Structure
Structure
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
2-D
Structure
StructureChemical
ER Binding
Chemical
3-D
Structure/
Properties
Molecular
Chemical
Structure
Molecular
Chemical 2-D
Structure
2-D
Structure
Structure
Chemical
2-D
Structure
Structure
Chemical 2-D
Structure
Chemical
3-D
Structure/
Properties
ER Binding
ER Molecular
Binding
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
ER Binding
Chemical 3-D
Chemical
3-D
Structure/
Properties
Structure/Structure
Molecular
2-D
Structure
Properties Chemical
ER Binding
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Chemical
3-D
Structure/
Properties
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
Cellular
ER
Transctivation
Organ Individual
Organ
Altered
Vitellogenin Induction
Reproduction/
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
ER
Cellular
Transctivation
ER
Transctivation
VTG mRNA
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Sex Steroids
Vitellogenin
Development
InductionIndividual
Organ
Altered
Vitellogenin Induction
Reproduction/
Sex Steroids
Development
Cellular
VTG
ER mRNA
Transctivation
Sex Steroids
Individual
Organ
Altered
Vitellogenin Induction
Reproduction/
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Libraries of Toxicological Pathways
Individual
Altered
Reproduction/
Development
Impaired Reproduction/Development
Mapping Toxicity Pathways to Adverse Outcomes
Mapping Toxicity Pathways to Adverse Outcomes
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Initiating Events
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Structure
Chemical 2-D
Structure
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Molecular
Chemical
3-D
Structure/
Properties
ER Binding
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
Cellular
ER
Transctivation
Organ
Vitellogenin Induction
Individual
Altered
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
VTG mRNA
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Sex Steroids
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Reproduction/
Development
Libraries of Toxicological Pathways
Adverse Outcomes
Structure
Chemical 2-D
Acknowledgements:
MED – J. Denny, R. Kolanczyk, B. Sheedy, M. Tapper;
SSC – C. Peck; B. Nelson; T. Wehinger, B.
Johnson; L. Toonen; R. Maciewski
NRC Post-doc: H. Aladjov
Bourgus University - LMC: O. Mekenyan, and many
others
Chemicals Evaluation Research Institute (CERI), Japan
- Y. Akahori, N. Nakai
EPA/NERL-Athens: J. Jones
EPA/OPP:
EFED - S. Bradbury, J. Holmes
RD - B.Shackleford, P. Wagner
AD - J. Housenger, D. Smegal
HED – L. Scarano
Mentors: G. Veith, L. Weber, and J.M. McKim, III