The Hamner Programs related to Recommendations from ......The Hamner Institutes Programs related to Implementing Recommendations from "Toxicity Testing in the 21st Century: A Vision

The Hamner Institutes Programs related to Implementing Recommendations from

"Toxicity Testing in the 21st Century: A Vision and a Strategy"

Confidential - Do Not Copy or Cite

This report envisions a not‐so‐distant future in which virtually all routine toxicity testing would be conducted in human cells or cell lines in vitro

by evaluating cellular responses in a suite of toxicity pathway assays using high throughput tests, implemented with robotic assistance.

Dose response modeling of perturbations of pathway function would be organized around computational systems biology models of the circuitry underlying each toxicity pathway.

In vitro to in vivo extrapolations would rely on pharmacokinetic models that would predict human blood and tissue concentrations under specific exposure conditions.

Overview of Key Aspects of the NAS Vision:Confidential - Do Not Copy or Cite

Option IIn Vivo

Option IITiered In Vivo

Option IIIIn Vitro/In Vivo

Option IVIn vitro

Animal biology Animal biology Primarily humanbiology

Primarily humanbiology

Apical endpoints Apical endpoints Perturbations of toxicity pathways

Perturbations of toxicity pathways

High doses High doses Broad range of doses Broad range of doses

In vitro mutagenicity Some in silico and in vitro screens

In silico screens possible

In silico screens

Low throughput Improved throughput High and mediumthroughput

High throughput

Expensive Less expensive Less expensive Less expensive

Time consuming Less time consuming Less timeconsuming

Less timeconsuming

Relative largenumber of animals

Fewer animals Substantially fewer animals

Virtually no animals

Options for Future Toxicity Testing StrategiesConfidential - Do Not Copy or Cite

BiologicInputs

NormalBiologicFunction

Morbidityand

Mortality

Cell Injury

Adaptive StressResponses

Early CellularChanges

Exposure

Tissue Dose

Biologic Interaction

Perturbation

Low DoseHigher Dose

Higher yet

A New Paradigm:Activation of Toxicity Pathways


Interpreting Toxicity Testing Resultswith Biological Modeling


Approaches to assess when perturbations are sufficient to lead to adaptation and when they are so large as to cause overt toxicity

External Stressor Level - S

Inte

rnal

Var

iable

-Y

In absence of regulation

Region of Regulationincluding altered gene expression

Toxicity


In vitro to in vivo extrapolations with PK and PBPK models


I. in vitro high throughput toxicity pathway tests (Σ )

II. Computational systems biology description of pathway circuitry for dose response modeling and dose response models – thresholds, non‐monotonic

III. Dose dependent transition studies for sequential pathway activation to understand linkage from perturbations to toxic responses

IV. PBPK Modules – Compound specific or class specific for in vitro‐ in vivo extrapolation, interpreting biomonitoring studies and inferring relationship of expected use patterns and doses to human populations

Toxicity Pathway Results and Quantitative Risk Assessments – A Possibility

n=1

132

Order hits in context


The Hamner Institutes for Health Sciences

Assessing the Exposure-Dose-Toxicity Relationship Within the EPA’s ToxCast Program –A Progress Report



Background

10

From EPA documenthttp://www.epa.gov/comptox/toxcast/files/ToxCast_Program_Outline_slides_01aug2007.pdf

• Goal of ToxCast is to develop “cost-effective innovative approaches to prioritize a large number of chemicals in a short period of time for toxicological testing.”

• The approach will use high throughput screening bioassays to identify patterns that “are strongly correlated with specific types of toxic effects observed in traditional animal toxicity testing.”



Problem with Qualitative Hazard Identification Approach

11

Reliance on in vitro assays with no linkage to dosimetry and exposure may lead to identifying hazardous, but risk irrelevant, properties of chemicals.

Goal of Our Research

Develop an integrated in vitro experimental and computational approach that helps predict dosimetry and exposure



The Hamner ToxCast Participation Project

12

Part I: Assess Target Organ Toxicity Part II: Evaluate Dosimetry & Exposure

Toxicity

Exposure Dose


The Hamner Institutes for Health Sciences | June 3, 2008


Part I: Assess Target Organ Toxicity

• Perform organ slice cultures for rat liver, lung, and kidney

• Expose cultures to 140 of the ~350 Phase I chemicals in 5 point dose response

• Measure cytotoxicity at 24 hours (ATP content)

• Conduct standard dose-response analysis and estimate EC50s




Part II: Evaluate Dosimetry & Exposure

• Estimate human exposure (oral) at LC50

• Measure plasma protein binding

• Measure metabolic stability using human microsomes or hepatocytes

• Estimate Estimate human steady state exposure (oral) using SimCyp software



Current Progress on Project

Part I: Assess Target Organ Toxicity

• Tissue-specific cytotoxicity has been estimated for each of the 140 chemicals

• Only 48 chemicals show measureable LC50s in at least 1 tissue

• Kidney most sensitive organ (38 chemicals); lung next most sensitive (10 chemicals); and liver least sensitive (8 chemicals)

• 6 chemicals showed toxicity in 2 organs and 1 chemical showed toxicity in all 3 organs

• Median LC50 was 31 uM (Range: 1.3 – 99 uM)




Part II: Evaluate Dosimetry & Exposure

• Partnership with SimCyp formalized

• Plasma protein binding starting next week




17

Screen Shots of SimCyp




18


Defining exposed population Variability in population




19


Defining parent chemical and protein binding Defining metabolic clearance




20

Output Conc at Steady State

Statistics

CL (L/h) CLpo (L/h) Fg(Sub) Fh(Sub) Fa(Sub) Css (mg/L)

Mean 0.47 0.56 1.00 0.99 0.88 1.61

Median 0.38 0.47 1.00 0.99 0.94 0.90

5th centile 0.11 0.12 1.00 0.98 0.60 0.31

95th centile 1.06 1.36 1.00 1.00 1.00 3.57

Skewness 0.96 1.14 n/a -1.29 -1.05 8.66

cv 0.66 0.72 0.00 0.01 0.17 2.27

Min Val 0.01 0.01 1.00 0.97 0.45 0.23

Max Val 1.41 1.81 1.00 1.00 1.00 36.04

Fold 144.74 156.64 1.00 1.03 2.23 156.64

Std Dev 0.31 0.41 0.00 0.01 0.15 3.66

Input Dose

Estimate Exposure Using Reverse Dosimetry




Supporting Computational Infrastructure

• Constructed database and Java-based interface for analyzing and searching data

TEASeRToxCast Exploration, Analysis, and Search Resource

The Hamner Institute for Health SciencesBioinformatics GroupVersion 1.0 Beta




Organized Around:1) Chemicals2) Assays3) Sublists

Navigation and Search Pane

View Pane




23

3 Standard Views for Assay Results

1) Conc Response2) Single Point3) SimCyp/PK



Related Projects at The Hamner on Prediction of Human Kinetics/Toxicity

24

• Risk Assessments for Developmental Effects of Phthalates • Di-n-butyl phthalate(ACC LRI)• Di-ethylhexyl phthalate (ACC LRI, with ENVIRON)• Extension to cumulative risk assessment (EPA STAR)

• Interpretation of biomonitoring data • N-methyl-carbamates (EPA STAR CRADA)• Perfluorinated compounds (EPA STAR)• Data-poor compounds (CEFIC LRI, with IRAS)

• Common modeling strategies:• Animal to human PK extrapolation • In vitro assays for human metabolism • QSAR for human distribution



Evaluating the Potential of Genomic Endpoints to Define Mode of Action and Model Low Dose Behavior



The Evolution of Genomic Technology• Gene expression microarray technology more than a decade old

• Photolithographic microarray – Fodor et al., Nature, 1993• Spotted cDNA microarray – Schena et al., Science, 1995

• Multiple studies have demonstrated the sensitivity and reproducibility of the current generation of microarrays

Sensitivity

Abdueva et al., PLOS One, 2007

Reproducibility Across Sites and Platforms

MAQC, Nat. Biotech, 2006

• Genome-wide analysis capability for broadly surveying the transcriptional state of the cell or tissue



The Evolution of Toxicology and Risk Assessment

• Increasing acceptance of dose-dependent transitions in mechanism

• New EPA risk assessment guidelines allow for default nonlinear dose-response extrapolations when based on mode-of-action

• Within the mode-of-action framework, underlying genomic changes could be considered a ‘precursor event’



The $1,000,000 Question…

Can genomic technologies be used to identify and characterize the response of key events in the low dose region?

A Qualified

Yes



Developing a Risk-Oriented Dose Response Analysis Approach

Develop methods to collect and analyze genomic data in a risk assessment context

Benchmark dose approaches to estimate reference doses

Analysis methods to identify nonmonotonic/ threshold responsesDose #1

Dose #2

Control

Dose Y...




Dose #1

Dose #2

Control

Dose Y...

Fit Genes With Statistical Models

Group Genes by Functional

Ontologies

Calculate Summary

Values for Each Cellular Function

Average, median, min,...


Thomas et al.Toxicol Sci. 98:240, 2007

5 doses + control (n = 4 - 8 animals per dose)

30



Dose Response Analysis of Five NTP Carcinogens

31

Chemical Route Doses Target Tissue

1,4-Dichlorobenzene Gavage 100, 300, 500, 500, 600 mg/kg Liver

Propylene glycol mono-t-butyl ether Inhalation 25, 75, 300, 800, 1200 ppm Liver

1,2,3-Trichloropropane Gavage 2, 6, 20, 40, 60 mg/kg Liver

Methylene Chloride Inhalation 100, 500, 2000, 3000, 4000 ppm Liver, Lung

Naphthalene Inhalation 0.5, 3, 10, 20, 30 ppm Lung

*Female B6C3F1 mice, 90 day time point




Dose #1

Dose #2

Control

Dose Y...



Ontologies

Calculate Summary





•All genes fit to power, linear, 2°, and 3° polynomial models

•Least complex model that best fits the data was selected (i.e., nested likelihood ratio test and AIC)

•BMD and BMDL calculated

32



Model Fits for an Example Gene

p = 0.755

Benchmark dose (BMD) ~ Transcriptional NOEL

33



Extension to Transcriptional Dose-Response Data


Ontologies

Calculate Summary






•Three biological domains supported by current gene ontology (GO) annotations – biological process, molecular function, and cell component

•The structure of the GO annotations is a directed acyclic graph

•A single GO category can have multiple parent and child GO terms

•A gene can exist in more than one GO category just like a gene can have multiple functionsDose #1

Dose #2

Control

Dose Y...

34



0

10

20

30

40

50

60

70

80

0 300 600

Rat

e of

Hep

atoc

ellu

lar

Ade

nom

a/C

arci

nom

a

Dose (mg/kg)

*

GO Accession GO Term NameTotal

GenesGenes with

BMD BMD Mean BMD SDBMD

MinimumBMDL Mean

BMDL Minimum

GO:0006686 sphingomyelin biosynthetic process 3 3 50.2 3.0 46.7 31.2 29.6GO:0016226 iron-sulfur cluster assembly 3 3 84.6 22.5 62.6 58.0 45.6GO:0031163 metallo-sulfur cluster assembly 3 3 84.6 22.5 62.6 58.0 45.6

Other Relevant CategoriesGO:0008284 positive regulation of cell proliferation 189 63 316.5 186.5 48.1 211.4 30.5GO:0006974 response to DNA damage stimulus 228 107 303.4 160.6 46.7 190.0 29.9GO:0006954 inflammatory response 197 72 346.2 166.7 47.9 240.3 36.0

Dichlorobenzene

Comparison Between Transcriptomic Dose Response and Tumor Response



Propylene Glycol Mono-t-butyl Ether


GenesGenes with


MinimumBMDL Mean BMDL Minimum

GO:0045995 regulation of embryonic development 7 3 189.7 151.0 83.5 106.9 55.0GO:0007625 grooming behavior 6 3 211.8 172.7 100.4 112.5 62.8GO:0032312 regulation of ARF GTPase activity 19 3 231.8 119.1 120.1 129.5 70.9GO:0031570 DNA integrity checkpoint 21 4 234.1 73.1 170.7 132.5 84.3


0

10

20

30

40

50

60

70

80

90

0 75 300 1200

Rat

e of

Hep

atoc

ellu

lar

Ade

nom

a/C

arci

nom

a

Dose (ppm)

*




1,2,3-Trichloropropane


GenesGenes with


Minimum BMDL Mean BMDL MinimumGO:0042307

positive regulation of protein import into nucleus 3 3 10.36 4.40 5.66 6.25 3.40GO:0001832 blastocyst growth 13 3 12.56 2.95 9.18 7.55 6.22GO:0015813 glutamate transport 4 3 12.70 7.37 6.94 7.25 4.99


0

10

20

30

40

50

60

0 6 20 60

Rat

e of

Hep

atoc

ellu

lar

Ade

nom

a/C

arci

nom

a

Dose (mg/kg)

*




Methylene Chloride (Liver)

0

10

20

30

40

50

60

70

80

90

0 2000 4000

Rat

e of

Hep

atoc

ellu

lar

Ade

nom

a/C

arci

nom

a

Dose (ppm)


GenesGenes with


Minimum BMDL Mean BMDL MinimumGO:0002891 positive regulation of immunoglobulin mediated

immune response 5 3 808.2 225.9 547.7 497.2 385.4GO:0043525

positive regulation of neuron apoptosis 4 3 856.4 604.2 292.8 442.3 183.3GO:0030212 hyaluronan metabolic process 4 4 876.8 571.0 453.7 589.2 330.5


*

*




Methylene Chloride (Lung)

GO Accession GO Term Name Total GenesGenes with


Minimum BMDL Mean BMDL MinimumGO:0001892 embryonic placenta development 12 3 305.3 239.1 57.5 188.3 160.0GO:0007000 nucleolus organization and biogenesis 3 3 345.3 322.1 32.4 204.2 195.1GO:0042558

pteridine and derivative metabolic process 13 3 360.0 301.8 54.1 210.2 188.0Other Relevant Categories

GO:0008284 positive regulation of cell proliferation 189 60 2416.4 254.3 1390.9 1811.9 166.4GO:0006974 response to DNA damage stimulus 228 105 2309.5 222.9 1375.0 1686.4 150.9GO:0006954 inflammatory response 197 64 2482.2 188.5 1250.6 1748.4 131.6

*

*




Naphthalene (Lung)


GenesGenes with


MinimumBMDL Mean

BMDL Minimum

GO:0007622 rhythmic behavior 6 3 7.01 3.62 3.75 3.98 2.59GO:0019856 pyrimidine base biosynthetic process 3 3 7.18 4.87 2.01 4.17 3.27GO:0042403 thyroid hormone metabolic process 7 3 8.32 3.12 5.21 5.04 1.81

Other Relevant CategoriesGO:0002218 activation of innate immune response 7 3 9.43 2.54 8.70 6.19 1.59GO:0002444 myeloid leukocyte mediated immunity 10 4 9.47 6.19 5.77 6.10 3.88GO:0008284 positive regulation of cell proliferation 189 80 19.04 4.79 6.97 12.60 3.24GO:0006974 response to DNA damage stimulus 228 89 18.24 2.60 7.64 11.56 1.63GO:0006954 inflammatory response 197 88 17.42 1.57 8.33 11.93 1.12

*





Develop methods to collect and analyze genomic data in a risk assessment context

Benchmark dose approaches to estimate reference doses

Analysis methods to identify nonmonotonic/ threshold responsesDose #1

Dose #2

Control

Dose Y...



Threshold and Nonmonotonic Responses in Risk Assessment

• There is continued debate on the use of threshold dose-response models for genotoxic and some nongenotoxiccarcinogens

• Nonmonotonic dose response more common than a threshold response across a broad set of toxicology studies (Calabrese and Baldwin, Tox Sci 2004)

• The EPA cancer guidelines permit the use of data on obligatory precursor events to inform the nature of the dose response below the concentrations at which the toxic sequelae are observed

Swenberg et al., Chem Res Tox 2008



Threshold and Nonmonotonic Responses in Risk Assessment

0 2000 4000 6000 8000 10000 12000

10-4

10-3

10-2

2

Cel

l Pro

lifer

atio

n R

ate

Formaldehyde Flux (pmol/mm2-hr-ppm)

• Formaldehyde is an example of a potential nonmonotonic/ threshold carcinogen

• Formaldehyde is produced endogenously through one carbon metabolism

• Evidence of nonmonotonic cell proliferation response

• Some evidence of a threshold in the nasal tumor response

0

10

20

30

40

50

60

Tumor R

esponse (%)

0 0.7 2 6 10 15

Exposure Concentration (ppm)

Kerns et al., 1983

Monticello et al., 1990



Dose Response Analysis of Formaldehyde Inhalation

44

Chemical Route Doses Time PointTarget Tissue

Formaldehyde Inhalation 0.7, 2, 6, 15 ppm 6 h, 5 d, and 19 d Nose

Epithelium from hightumor region



Formaldehyde Benchmark Doses by Gene Ontology Category

Time Point6 hr 5 day 19 day

Mean (ppm) SD

Min (ppm) Count

Mean (ppm) SD

Min (ppm) Count

Mean (ppm) SD

Min (ppm) Count

Protein Import into Nucleus GO:0000059 2.02 0.34 1.53 4

Complement Activation, Alternative GO:0006957 1.58 0.28 1.37 3

Positive T-cell Selection GO:0043368 1.18 0.17 1.02 3

Other Selected Categories

Positive Regulation of Cell Proliferation GO:0008284 7.82 4.26 0.64 100 7.52 3.78 0.63 114 6.85 3.90 0.57 137

Response to DNA Damage Stimulus GO:0006974 6.82 4.07 0.64 103 7.12 3.79 0.84 105 6.57 3.73 0.70 124

Inflammatory Response GO:0006954 7.61 4.64 0.86 100 8.16 3.78 0.79 132 7.15 4.03 0.49 140

Most Sensitive GO Categories with Time



Developing a Nonmonotonic Dose Response Analysis Approach

Dose #1

Dose #2

Control

Dose Y...


2nd and 3rd

Polynomial Genes With Min or Max in Dose

Range

Gene Ontology Enrichment

Analysis

5 doses + control (n = 4 - 8 animals per dose)

46

Identify Nonmonotonic

Genes



Formaldehyde Nonmonotonic Genes by Gene Ontology Category

GO Category Count % p-Value Benjamini Adj p-value FDRnucleic acid metabolic process 648 15.1 7.40E-10 9.10E-07 0metabolic process 1499 35 5.20E-08 3.20E-05 0RNA processing 106 2.5 8.90E-08 4.00E-05 0post-translational protein modification 308 7.2 1.50E-07 5.80E-05 0antigen processing and presentation of peptide antigen 27 0.6 6.60E-07 2.20E-04 0protein localization 200 4.7 1.10E-06 3.50E-04 0protein amino acid phosphorylation 173 4 1.00E-05 2.20E-03 0regulation of gene expression 399 9.3 1.20E-05 2.30E-03 0RNA splicing 55 1.3 1.40E-05 2.50E-03 0I-kappaB kinase/NF-kappaB cascade 47 1.1 1.90E-05 3.10E-03 0ubiquitin cycle 79 1.8 8.70E-05 1.00E-02 0.2Golgi vesicle transport 40 0.9 1.20E-04 1.40E-02 0.2ER to Golgi vesicle-mediated transport 26 0.6 1.30E-04 1.30E-02 0.2cellular localization 238 5.6 1.30E-04 1.30E-02 0.2cell cycle 154 3.6 1.50E-04 1.50E-02 0.3DNA repair 58 1.4 3.30E-04 3.10E-02 0.6cell division 45 1.1 5.30E-04 4.90E-02 1response to DNA damage stimulus 70 1.6 8.60E-04 7.40E-02 1.6regulation of progression through cell cycle 86 2 1.20E-03 9.40E-02 2.2protein kinase cascade 111 2.6 1.50E-03 1.10E-01 2.7base-excision repair 12 0.3 1.60E-03 1.10E-01 3nuclear export 16 0.4 1.70E-03 1.20E-01 3.3protein folding 58 1.4 1.80E-03 1.30E-01 3.4epidermal growth factor receptor signaling pathway 13 0.3 1.90E-03 1.30E-01 3.6Ras protein signal transduction 57 1.3 2.00E-03 1.30E-01 3.7



Current Internal Thinking on Application to Risk Assessment



One current challenge is discriminating among “adaptive” and “toxic” responses...

Application of Transcriptomic Data to Dose Response Assessment

TranscriptomicDose-Response/

Time CourseData

Toxic Responses

Adaptive Responses

TranscriptionalRfD

(based on targettissue dose)

Transcriptomic-basedRisk

Assessment

Until we can discriminate between adaptive and toxic responses, can we define a “transcriptionally neutral” RfD as a safe exposure level?

All Responses

De minimusTranscriptional

RfD(based on target

tissue dose)



• Applying genomic tools to dose response studies allows a broad survey of the transcriptional response and their change with dose.

• Integrating benchmark dose analysis allows reference doses to be estimated for individual genes and functional categories.

• First steps are being taken to use the information in a genomics-based risk assessment.

• Challenges will be determining which functional categories represent adverse versus adaptive effects and what summary values to use for each category.

Wrap-up and SummaryConfidential - Do Not Copy or Cite


• Java-based graphical interface

• Straight forward analysis workflow

• Model fitting performed using C and Fortran dynamic link libraries “borrowed” from BMDS software

• GO analysis uses MySQLdatabase residing at The Hamner Institutes

• New version (v1.3) has enhanced graphics capability and other features

• ITS FREE!!!!

http://sourceforge.net/projects/bmdexpress

Yang et al., BMC Genomics. 2007 Oct 25;8(1):387

Software for Genomic Dose Response Analysis


http://sourceforge.net/projects/bmdexpress

Documents

The Hamner Programs related to Recommendations from ......The Hamner Institutes Programs related to Implementing Recommendations from "Toxicity Testing in the 21st Century: A Vision