Toxicology Testing in the 21st Century ndash Update of the Vision

Mel Andersen The Hamner Institutes for Health Sciences

Research Triangle Park NC USA 27709 mandersenthehamnerorg

1

In 2007 A Vision arrived at the Toxicology Community

I envision the future of safety testing toxicity pathways in vitro assays human cells

The NAS Report Recommended using new in vitro assays and computational approaches

The early 2000 approach to toxicity testing isnrsquot only cumbersome it is not optimal for toxicity testing in the 21st Century A transformative redefinition of toxicity testing testing is required to meet key design criteria and use in high throughput tools for testing

Krewski et al (2011) New Directions in Toxicity Testing Ann Rev Public Health 32 161-178

TT21C very consistent with 1983 Red Book

It is not designed to predict high dose animal toxicity or to prioritize animal testing Animals are not the lsquogold standardrsquo human biology needs to provide the gold standard Approach based on rapid in vitro tests to assess perturbations of lsquotoxicity pathwaysrsquo of relevance for human biology and to interpret them in a dose-response context Assessed over wide range of doses and interpreted in relation to structures of biological circuits and exposures that are not expected to cause significant perturbations of these pathways

Whatrsquos the Target

Possibility for Implementation ie the Strategy The NRC Report

Now Everyone has a Vision

RISK21

ToxCast ndash broad sweep and pathway signatures Individual companies efforts to modernize toxicity testing Broad pathway identification and PoT ontologies ndash Thomas Hartung John Hopkins University Case study approaches ndash eg the Human Toxicology Project and The Hamner

The NAS Report Recommended using new in vitro assays and computational approaches

The early 2000 approach to toxicity testing isnrsquot only cumbersome it is not optimal for toxicity testing in the 21st Century A transformative redefinition of toxicity testing testing is required to meet key design criteria and use in high throughput tools for testing

Krewski et al (2011) New Directions in Toxicity Testing Ann Rev Public Health 32 161-178

TT21C very consistent with 1983 Red Book

It is not designed to predict high dose animal toxicity or to prioritize animal testing Animals are not the lsquogold standardrsquo human biology needs to provide the gold standard Approach based on rapid in vitro tests to assess perturbations of lsquotoxicity pathwaysrsquo of relevance for human biology and to interpret them in a dose-response context Assessed over wide range of doses and interpreted in relation to structures of biological circuits and exposures that are not expected to cause significant perturbations of these pathways

Whatrsquos the Target

Possibility for Implementation ie the Strategy The NRC Report

Now Everyone has a Vision

RISK21

ToxCast ndash broad sweep and pathway signatures Individual companies efforts to modernize toxicity testing Broad pathway identification and PoT ontologies ndash Thomas Hartung John Hopkins University Case study approaches ndash eg the Human Toxicology Project and The Hamner

Krewski et al (2011) New Directions in Toxicity Testing Ann Rev Public Health 32 161-178

TT21C very consistent with 1983 Red Book

It is not designed to predict high dose animal toxicity or to prioritize animal testing Animals are not the lsquogold standardrsquo human biology needs to provide the gold standard Approach based on rapid in vitro tests to assess perturbations of lsquotoxicity pathwaysrsquo of relevance for human biology and to interpret them in a dose-response context Assessed over wide range of doses and interpreted in relation to structures of biological circuits and exposures that are not expected to cause significant perturbations of these pathways

Whatrsquos the Target

Possibility for Implementation ie the Strategy The NRC Report

Now Everyone has a Vision

RISK21

ToxCast ndash broad sweep and pathway signatures Individual companies efforts to modernize toxicity testing Broad pathway identification and PoT ontologies ndash Thomas Hartung John Hopkins University Case study approaches ndash eg the Human Toxicology Project and The Hamner

It is not designed to predict high dose animal toxicity or to prioritize animal testing Animals are not the lsquogold standardrsquo human biology needs to provide the gold standard Approach based on rapid in vitro tests to assess perturbations of lsquotoxicity pathwaysrsquo of relevance for human biology and to interpret them in a dose-response context Assessed over wide range of doses and interpreted in relation to structures of biological circuits and exposures that are not expected to cause significant perturbations of these pathways

Whatrsquos the Target

Possibility for Implementation ie the Strategy The NRC Report

Now Everyone has a Vision

RISK21

ToxCast ndash broad sweep and pathway signatures Individual companies efforts to modernize toxicity testing Broad pathway identification and PoT ontologies ndash Thomas Hartung John Hopkins University Case study approaches ndash eg the Human Toxicology Project and The Hamner

Possibility for Implementation ie the Strategy The NRC Report

Now Everyone has a Vision

RISK21

ToxCast ndash broad sweep and pathway signatures Individual companies efforts to modernize toxicity testing Broad pathway identification and PoT ontologies ndash Thomas Hartung John Hopkins University Case study approaches ndash eg the Human Toxicology Project and The Hamner

Now Everyone has a Vision

RISK21

ToxCast ndash broad sweep and pathway signatures Individual companies efforts to modernize toxicity testing Broad pathway identification and PoT ontologies ndash Thomas Hartung John Hopkins University Case study approaches ndash eg the Human Toxicology Project and The Hamner

Implementation of new Testing for Regulation

q-HTS Profiling amp Risk

Assessments with HTS Testing

q-HTS studies genomics for

risk assessment and

prioritization

Activities in individual

companies to use various in vitro methods

Different Approaches 2012

Case study approaches

for implementing

TT21C

TheTT21C Report

Profiling and Prioritization

Predict results of animal studies Prioritize for in vivo testing

Assist in risk assessment

ToxCast and Tox21 High Throughput Screening and

Computational Toxicology

Estimating Toxicity-Related Biological Pathway Altering Doses for High-Throughput Chemical Risk Assessment Judson RS Kavlock RJ Setzer RW Cohen Hubal EA Martin MT Knudsen TB Houck KA Thomas RS Wetmore BA Dix DJ Chem Res Toxicol 2011

q-HTS and relative potency across various assays Use both activity in assays and exposure information

ldquoMoving to Pathway Based Risk Assessmentsrdquo

Profiling and Prioritization

Predict results of animal studies Prioritize for in vivo testing

Assist in risk assessment

ToxCast and Tox21 High Throughput Screening and

Computational Toxicology

Estimating Toxicity-Related Biological Pathway Altering Doses for High-Throughput Chemical Risk Assessment Judson RS Kavlock RJ Setzer RW Cohen Hubal EA Martin MT Knudsen TB Houck KA Thomas RS Wetmore BA Dix DJ Chem Res Toxicol 2011

q-HTS and relative potency across various assays Use both activity in assays and exposure information

ldquoMoving to Pathway Based Risk Assessmentsrdquo

Estimating Toxicity-Related Biological Pathway Altering Doses for High-Throughput Chemical Risk Assessment Judson RS Kavlock RJ Setzer RW Cohen Hubal EA Martin MT Knudsen TB Houck KA Thomas RS Wetmore BA Dix DJ Chem Res Toxicol 2011

q-HTS and relative potency across various assays Use both activity in assays and exposure information

ldquoMoving to Pathway Based Risk Assessmentsrdquo

Conclusions

The current ToxCast in vitro high-throughput screening assays provide limited ability to predict in vivo toxic responses

Other Possible Uses for qHTS results

Use with exposure assessments to identify chemicals of little concern Conduct transcriptomic assessments and refined PKexposure analysis to identify compounds moving to more traditional testing

Comparing In Vitro Bioactive Doses with Exposure

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Wetmore et al Tox Sci 2011

A total of 99 of ToxCast Phase I chemicals have in vitro bioactivity at oral equivalent doses that overlap with the most highly exposed subpopulation

Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics

Noncancer Endpoints

Chemical Endpoint BMD

(mgkg-d or mgm3)a BMDL

(mgkg-d or mgm3)a DCBZ Relative Liver Weight 1746 1120 PGBE Relative Liver Weight 20670 16872

TCPN Bronchiole Epithelial Degeneration 249 167

MECL Periportal Vacuolation 21706 10363

NPTH Bronchiole Epithelial Degeneration 169 112

aBMD = Dose at 10 extra risk or 1 SD BMDL = 95 lower bound on BMD

Chemical Tissue BMD

(mgkg-d or mgm3)a BMDL

(mgkg-d or mgm3)a DCBZ Liver 2182 1583 PGBE Liver 17740 8657 TCPN Liver 228

(28)b 130 (13)b

MECL Liver 35446 19305 MECL Lung 7907 6323 NPTH Lung 1195 917 aBMD = Dose at 10 extra risk BMDL = 95 lower bound on BMD bBMD and BMDL values calculated using a multi-stage Weibull model per the EPA IRIS summary

Cancer Endpoints

Comparing In Vitro Bioactive Doses with Exposure

Fent

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late

Dim

ethy

l pht

hala

teD

iazo

xon

000001

00001

0001

001

01

1

10

100

1000

10000

100000

Ora

l Equ

ival

ent D

ose

or E

stim

ated

Exp

osur

e(m

gkg

day

)

Wetmore et al Tox Sci 2011

A total of 99 of ToxCast Phase I chemicals have in vitro bioactivity at oral equivalent doses that overlap with the most highly exposed subpopulation

Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics

Noncancer Endpoints

Chemical Endpoint BMD

(mgkg-d or mgm3)a BMDL

(mgkg-d or mgm3)a DCBZ Relative Liver Weight 1746 1120 PGBE Relative Liver Weight 20670 16872

TCPN Bronchiole Epithelial Degeneration 249 167

MECL Periportal Vacuolation 21706 10363

NPTH Bronchiole Epithelial Degeneration 169 112

aBMD = Dose at 10 extra risk or 1 SD BMDL = 95 lower bound on BMD

Chemical Tissue BMD

(mgkg-d or mgm3)a BMDL

(mgkg-d or mgm3)a DCBZ Liver 2182 1583 PGBE Liver 17740 8657 TCPN Liver 228

(28)b 130 (13)b

MECL Liver 35446 19305 MECL Lung 7907 6323 NPTH Lung 1195 917 aBMD = Dose at 10 extra risk BMDL = 95 lower bound on BMD bBMD and BMDL values calculated using a multi-stage Weibull model per the EPA IRIS summary

Cancer Endpoints

Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics

Noncancer Endpoints

Chemical Endpoint BMD

(mgkg-d or mgm3)a BMDL

(mgkg-d or mgm3)a DCBZ Relative Liver Weight 1746 1120 PGBE Relative Liver Weight 20670 16872

TCPN Bronchiole Epithelial Degeneration 249 167

MECL Periportal Vacuolation 21706 10363

NPTH Bronchiole Epithelial Degeneration 169 112

aBMD = Dose at 10 extra risk or 1 SD BMDL = 95 lower bound on BMD

Chemical Tissue BMD

(mgkg-d or mgm3)a BMDL

(mgkg-d or mgm3)a DCBZ Liver 2182 1583 PGBE Liver 17740 8657 TCPN Liver 228

(28)b 130 (13)b

MECL Liver 35446 19305 MECL Lung 7907 6323 NPTH Lung 1195 917 aBMD = Dose at 10 extra risk BMDL = 95 lower bound on BMD bBMD and BMDL values calculated using a multi-stage Weibull model per the EPA IRIS summary

Cancer Endpoints

01

1

10

100

1000

01 1 10 100 1000

Low

est A

pica

l BM

D (m

gkg

d)

Lowest Pathway Transcriptional BMD (mgkgd)

01

1

10

100

1000

01 1 10 100 1000

Low

est A

pica

l BM

D (m

gkg

d o

r ppm

)

Lowest Pathway Transcriptional BMD (mgkgd)

01

1

10

100

1000

01 1 10 100 1000

Low

est A

pica

l BM

D (m

gkg

d)

Lowest Pathway Transcriptional BMD (mgkgd)

01

1

10

100

1000

01 1 10 100 1000

Low

est A

pica

l BM

D (m

gkg

d)

Lowest Pathway Transcriptional BMD (mgkgd)

Temporal Changes in Correlation Between Non-Cancer and Transcriptional Endpoints

Bladder Liver Thyroid

4 Weeks 13 Weeks

5 Days 2 Weeks

r = 0881 r = 0971

r = 0971 r = 0957

A Data-Driven 21st Century Tox and RA Framework

Human In Vitro Pharmacokinetic Assays

and IVIVE Modeling

Conservative First Order Human Exposure Characterization

Define First Order Margin-of-Exposure

MOE gt lsquoXrsquo

Tier 1 Testing In Vitro Assays for Bioactivity

Potent Specific Interacting Chemicals

Weak Non-Specific Interacting Chemicals

Define Tentative Mode-of-Action

Tier 3 Testing [Standard Tox Studies]

Short-term Rodent Transcriptomic

Studies Refined Pharmacokinetic

Estimates

Refined Second Order Human Exposure Characterization

Define Second Order Margin-of-Exposure

MOE gt lsquoXrsquo

Tier 2 Testing Confirm In Vivo

Mode-of-Action and Human Relevance

bull Trend towards assessment based on Toxicity Pathways Mode-of-Action (MoA) and Adverse Outcome Pathway (AOP)

Being mindful of the prevailing terminology

Exposure

Molecular Initiating Event

Organelle Effects

Cellular Effects

Tissue Effects

Organ Response

Individual Response

Population Response

Toxicity Pathway

Mode of Action

Adverse outcome pathway

q-HTS Assays AgonistAntagonist

Modes

Targeted MOA-based pathway

assays

QSAR Methods Computational

Biology Safety-Based

TT21C Assessment

CSBP Modeling amp

QIVIVE

HCA Assays Multi-Endpoints

A third approach ndash case studies based on toxicity pathways and modes of action

Interpretive Tools for a TT21C Approach

Some advantages with a Case Study Approach

1 Design assays for purpose ndash ie collecting information for adversity and use in risk assessment

2 Develop extrapolation methods to use test results for regulation

3 Establish (or at least discuss) from the start the optimal use of in vitro information so we avoid developing institutionalized default methods

4 Create a risksafety based process that can be quickly used as other toxicity pathways are enumerated

4 Early on look at prototypes for pathways with MIEs that are receptor mediated and others that are related to chemical reactivity

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

A Data-Driven 21st Century Tox and RA Framework

Human In Vitro Pharmacokinetic Assays

and IVIVE Modeling

Conservative First Order Human Exposure Characterization

Define First Order Margin-of-Exposure

MOE gt lsquoXrsquo

Tier 1 Testing In Vitro Assays for Bioactivity

Potent Specific Interacting Chemicals

Weak Non-Specific Interacting Chemicals

Define Tentative Mode-of-Action

Tier 3 Testing [Standard Tox Studies]

Short-term Rodent Transcriptomic

Studies Refined Pharmacokinetic

Estimates

Refined Second Order Human Exposure Characterization

Define Second Order Margin-of-Exposure

MOE gt lsquoXrsquo

Tier 2 Testing Confirm In Vivo

Mode-of-Action and Human Relevance

bull Trend towards assessment based on Toxicity Pathways Mode-of-Action (MoA) and Adverse Outcome Pathway (AOP)

Being mindful of the prevailing terminology

Exposure

Molecular Initiating Event

Organelle Effects

Cellular Effects

Tissue Effects

Organ Response

Individual Response

Population Response

Toxicity Pathway

Mode of Action

Adverse outcome pathway

q-HTS Assays AgonistAntagonist

Modes

Targeted MOA-based pathway

assays

QSAR Methods Computational

Biology Safety-Based

TT21C Assessment

CSBP Modeling amp

QIVIVE

HCA Assays Multi-Endpoints

A third approach ndash case studies based on toxicity pathways and modes of action

Interpretive Tools for a TT21C Approach

Some advantages with a Case Study Approach

1 Design assays for purpose ndash ie collecting information for adversity and use in risk assessment

2 Develop extrapolation methods to use test results for regulation

3 Establish (or at least discuss) from the start the optimal use of in vitro information so we avoid developing institutionalized default methods

4 Create a risksafety based process that can be quickly used as other toxicity pathways are enumerated

4 Early on look at prototypes for pathways with MIEs that are receptor mediated and others that are related to chemical reactivity

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

bull Trend towards assessment based on Toxicity Pathways Mode-of-Action (MoA) and Adverse Outcome Pathway (AOP)

Being mindful of the prevailing terminology

Exposure

Molecular Initiating Event

Organelle Effects

Cellular Effects

Tissue Effects

Organ Response

Individual Response

Population Response

Toxicity Pathway

Mode of Action

Adverse outcome pathway

q-HTS Assays AgonistAntagonist

Modes

Targeted MOA-based pathway

assays

QSAR Methods Computational

Biology Safety-Based

TT21C Assessment

CSBP Modeling amp

QIVIVE

HCA Assays Multi-Endpoints

A third approach ndash case studies based on toxicity pathways and modes of action

Interpretive Tools for a TT21C Approach

Some advantages with a Case Study Approach

1 Design assays for purpose ndash ie collecting information for adversity and use in risk assessment

2 Develop extrapolation methods to use test results for regulation

3 Establish (or at least discuss) from the start the optimal use of in vitro information so we avoid developing institutionalized default methods

4 Create a risksafety based process that can be quickly used as other toxicity pathways are enumerated

4 Early on look at prototypes for pathways with MIEs that are receptor mediated and others that are related to chemical reactivity

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

q-HTS Assays AgonistAntagonist

Modes

Targeted MOA-based pathway

assays

QSAR Methods Computational

Biology Safety-Based

TT21C Assessment

CSBP Modeling amp

QIVIVE

HCA Assays Multi-Endpoints

A third approach ndash case studies based on toxicity pathways and modes of action

Interpretive Tools for a TT21C Approach

Some advantages with a Case Study Approach

1 Design assays for purpose ndash ie collecting information for adversity and use in risk assessment

2 Develop extrapolation methods to use test results for regulation

3 Establish (or at least discuss) from the start the optimal use of in vitro information so we avoid developing institutionalized default methods

4 Create a risksafety based process that can be quickly used as other toxicity pathways are enumerated

4 Early on look at prototypes for pathways with MIEs that are receptor mediated and others that are related to chemical reactivity

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Interpretive Tools for a TT21C Approach

Some advantages with a Case Study Approach

1 Design assays for purpose ndash ie collecting information for adversity and use in risk assessment

2 Develop extrapolation methods to use test results for regulation

3 Establish (or at least discuss) from the start the optimal use of in vitro information so we avoid developing institutionalized default methods

4 Create a risksafety based process that can be quickly used as other toxicity pathways are enumerated

4 Early on look at prototypes for pathways with MIEs that are receptor mediated and others that are related to chemical reactivity

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Some advantages with a Case Study Approach

1 Design assays for purpose ndash ie collecting information for adversity and use in risk assessment

2 Develop extrapolation methods to use test results for regulation

3 Establish (or at least discuss) from the start the optimal use of in vitro information so we avoid developing institutionalized default methods

4 Create a risksafety based process that can be quickly used as other toxicity pathways are enumerated

4 Early on look at prototypes for pathways with MIEs that are receptor mediated and others that are related to chemical reactivity

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Receptor mediated pathways

PPARα and other nuclear hormone receptors (CAR AhR ERα etc) appear to share a similar signaling logic

Peroxisome proliferation

Cellular proliferationcarcinogenesis

Inflammation

Fatty acid metabolism

In rodents

In humans

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Systems Pharmacology has some things to teach us

Phenotypic consequences for

each grouping

Mapping Receptor Mediated Pathways

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Modeling receptor mediated Pathway Dynamics ndash Ultrasensitive response motifs to assess dose response

Stimulus (S)

MKKK

MKK MKK P MKK P P

MAPK MAPK P MAPK P P

MKKK a

DAG

PKC

AA

cPLA2

PDGF-R

SHC

Grb2Sos

Ras

Raf

MAPK

MEK PP2A

MKP

Ca2+

NIH 3T3 Fibroblasts

Transcription Factors

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Functional annotation enrichment of genes to look at processes affected with human PPARα

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Bioinformatics and gene clustering to look at dose response of processes

Dose (uM)

Fold Change

01μM 10μM

100μM Lipid transport

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Constructing a regulatory network G

enes

Transcription Factors

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

10μM

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well

Src ()

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Extend to rat See differences in gene pathways altered in rat Compare dose response in vitro in rat with liver primary cells to see dose response across intact liver Determine common structural processes that control output of pathways in different species Do a formal safety assessment with the CSB-Pathway Model

After some fits and starts we are making good progress with PPARα In the process of completing confirmatory studies with kinase inhibitors and knock-downs then writing the dose response model

With PPARα

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Common structure of cellular stress pathways with sensors transcriptional factors and tranducers (commonly kinase mediated pathways)

The second motifhellip

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

Common structures associate with common control processes in controlling cellular stress ndash a model from yeast

Adapted from Miermont et al Signal Trans 2011 Muzzey et all Cell 2009 and Mettetal et all Science 2008

S

Y

int

Feedback Control

Assessing mechanistic basis for homeostasis threshold behaviors and overall dose response

Homeostasis requires perfect adaptation of rapidly acting pathways (post-translational modification) and perfect adaptation of slower acting pathways (transcriptional) Integral feedback underlies perfect adaptation in multiple signaling pathways

A Safety Assessment Schematic for Using mode-of-action based pathwasy assays for safety assessment ndash most of the work for implementation with first 2 or 3 case studies

in vitro-in vivo dosimetry

PK Modeling

in vivo human exposure lsquostandardrsquo

mgkgday

lsquoValidatedrsquo in vitro assays for

endocrine pathway activities

Computational Systems Biology Pathway (CSBP)

Modeling

Assessing adversity in vitro

Point of Departure

(concentration)

Acceptable concentration in vitro (ugl)

Vision is holding up well while undergoing healthy refinement and scrutiny Key technologies continue to mature and should accelerate decisions about value of specific assays IVIVE and CSBP modeling for TT21C The testing capacity is growing rapidly and many industries in the US see the possibilities of large amounts of data in the public domain with few interpretive tools Some urgency to get more quantitative approaches in place and show their use to compare safety assessments competed with alternative methodologies

Conclusions on this The 5th Anniversary of the NRC TT21C

Rusty Thomas Harvey Clewell Rebecca Clewell Sudin Bhattacharya Qiang Zhang Patrick McMullen Jingbo Pi

Colleagues and Collaborators on the Projects

Paul Carmichael Andrew White Andrew Scott Kim Boekelheide Marty Stephens Daniel Krewski

With support to the Hamner from ACC-LRI Dow Dow Corning Exxon Mobil Foundation Unilever

• Toxicology Testing in the 21st Century ndash Update of the Vision
• In 2007 A Vision arrived at the Toxicology Community
• Slide Number 3
• Slide Number 4
• Slide Number 5
• Slide Number 6
• Now Everyone has a Vision
• Slide Number 8
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Conclusions
• Comparing In Vitro Bioactive Doses with Exposure
• Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics
• Slide Number 15
• Slide Number 16
• Slide Number 17
• Slide Number 18
• Slide Number 19
• Slide Number 20
• Slide Number 21
• Slide Number 22
• Slide Number 23
• Slide Number 24
• Slide Number 25
• Constructing a regulatory network
• Slide Number 27
• MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well
• Slide Number 29
• Slide Number 30
• Slide Number 31
• Assessing mechanistic basis for homeostasis threshold behaviors and overall dose response
• Slide Number 33
• Slide Number 34
• Slide Number 35

A Safety Assessment Schematic for Using mode-of-action based pathwasy assays for safety assessment ndash most of the work for implementation with first 2 or 3 case studies

in vitro-in vivo dosimetry

PK Modeling

in vivo human exposure lsquostandardrsquo

mgkgday

lsquoValidatedrsquo in vitro assays for

endocrine pathway activities

Computational Systems Biology Pathway (CSBP)

Modeling

Assessing adversity in vitro

Point of Departure

(concentration)

Acceptable concentration in vitro (ugl)

Vision is holding up well while undergoing healthy refinement and scrutiny Key technologies continue to mature and should accelerate decisions about value of specific assays IVIVE and CSBP modeling for TT21C The testing capacity is growing rapidly and many industries in the US see the possibilities of large amounts of data in the public domain with few interpretive tools Some urgency to get more quantitative approaches in place and show their use to compare safety assessments competed with alternative methodologies

Conclusions on this The 5th Anniversary of the NRC TT21C

Rusty Thomas Harvey Clewell Rebecca Clewell Sudin Bhattacharya Qiang Zhang Patrick McMullen Jingbo Pi

Colleagues and Collaborators on the Projects

Paul Carmichael Andrew White Andrew Scott Kim Boekelheide Marty Stephens Daniel Krewski

With support to the Hamner from ACC-LRI Dow Dow Corning Exxon Mobil Foundation Unilever

• Toxicology Testing in the 21st Century ndash Update of the Vision
• In 2007 A Vision arrived at the Toxicology Community
• Slide Number 3
• Slide Number 4
• Slide Number 5
• Slide Number 6
• Now Everyone has a Vision
• Slide Number 8
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Conclusions
• Comparing In Vitro Bioactive Doses with Exposure
• Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics
• Slide Number 15
• Slide Number 16
• Slide Number 17
• Slide Number 18
• Slide Number 19
• Slide Number 20
• Slide Number 21
• Slide Number 22
• Slide Number 23
• Slide Number 24
• Slide Number 25
• Constructing a regulatory network
• Slide Number 27
• MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well
• Slide Number 29
• Slide Number 30
• Slide Number 31
• Assessing mechanistic basis for homeostasis threshold behaviors and overall dose response
• Slide Number 33
• Slide Number 34
• Slide Number 35

Vision is holding up well while undergoing healthy refinement and scrutiny Key technologies continue to mature and should accelerate decisions about value of specific assays IVIVE and CSBP modeling for TT21C The testing capacity is growing rapidly and many industries in the US see the possibilities of large amounts of data in the public domain with few interpretive tools Some urgency to get more quantitative approaches in place and show their use to compare safety assessments competed with alternative methodologies

Conclusions on this The 5th Anniversary of the NRC TT21C

Rusty Thomas Harvey Clewell Rebecca Clewell Sudin Bhattacharya Qiang Zhang Patrick McMullen Jingbo Pi

Colleagues and Collaborators on the Projects

Paul Carmichael Andrew White Andrew Scott Kim Boekelheide Marty Stephens Daniel Krewski

With support to the Hamner from ACC-LRI Dow Dow Corning Exxon Mobil Foundation Unilever

• Toxicology Testing in the 21st Century ndash Update of the Vision
• In 2007 A Vision arrived at the Toxicology Community
• Slide Number 3
• Slide Number 4
• Slide Number 5
• Slide Number 6
• Now Everyone has a Vision
• Slide Number 8
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Conclusions
• Comparing In Vitro Bioactive Doses with Exposure
• Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics
• Slide Number 15
• Slide Number 16
• Slide Number 17
• Slide Number 18
• Slide Number 19
• Slide Number 20
• Slide Number 21
• Slide Number 22
• Slide Number 23
• Slide Number 24
• Slide Number 25
• Constructing a regulatory network
• Slide Number 27
• MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well
• Slide Number 29
• Slide Number 30
• Slide Number 31
• Assessing mechanistic basis for homeostasis threshold behaviors and overall dose response
• Slide Number 33
• Slide Number 34
• Slide Number 35

Rusty Thomas Harvey Clewell Rebecca Clewell Sudin Bhattacharya Qiang Zhang Patrick McMullen Jingbo Pi

Colleagues and Collaborators on the Projects

Paul Carmichael Andrew White Andrew Scott Kim Boekelheide Marty Stephens Daniel Krewski

With support to the Hamner from ACC-LRI Dow Dow Corning Exxon Mobil Foundation Unilever

• Toxicology Testing in the 21st Century ndash Update of the Vision
• In 2007 A Vision arrived at the Toxicology Community
• Slide Number 3
• Slide Number 4
• Slide Number 5
• Slide Number 6
• Now Everyone has a Vision
• Slide Number 8
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Conclusions
• Comparing In Vitro Bioactive Doses with Exposure
• Noncancer and Cancer Points-of-Departure for Apical Endpoints with Genomics
• Slide Number 15
• Slide Number 16
• Slide Number 17
• Slide Number 18
• Slide Number 19
• Slide Number 20
• Slide Number 21
• Slide Number 22
• Slide Number 23
• Slide Number 24
• Slide Number 25
• Constructing a regulatory network
• Slide Number 27
• MAPK1 amp MAPK3 are integral parts of the PPARα kinase network amp likely src as well
• Slide Number 29
• Slide Number 30
• Slide Number 31
• Assessing mechanistic basis for homeostasis threshold behaviors and overall dose response
• Slide Number 33
• Slide Number 34
• Slide Number 35