the hamner programs related to recommendations from ......the hamner institutes programs related to...
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The Hamner Institutes Programs related to Implementing Recommendations from
"Toxicity Testing in the 21st Century: A Vision and a Strategy"
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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
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Interpreting Toxicity Testing Resultswith Biological Modeling
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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
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In vitro to in vivo extrapolations with PK and PBPK models
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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
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Assessing the Exposure-Dose-Toxicity Relationship Within the EPA’s ToxCast Program –A Progress Report
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Background
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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.”
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Problem with Qualitative Hazard Identification Approach
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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
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The Hamner ToxCast Participation Project
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Part I: Assess Target Organ Toxicity Part II: Evaluate Dosimetry & Exposure
Toxicity
Exposure Dose
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The Hamner Institutes for Health Sciences | June 3, 2008
The Hamner ToxCast Participation Project
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
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The Hamner ToxCast Participation Project
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
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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)
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Current Progress on Project
Part II: Evaluate Dosimetry & Exposure
• Partnership with SimCyp formalized
• Plasma protein binding starting next week
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Current Progress on Project
17
Screen Shots of SimCyp
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Current Progress on Project
18
Screen Shots of SimCyp
Defining exposed population Variability in population
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Current Progress on Project
19
Screen Shots of SimCyp
Defining parent chemical and protein binding Defining metabolic clearance
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Current Progress on Project
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
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Current Progress on Project
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
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The Hamner Institutes for Health Sciences | June 3, 2008
Current Progress on Project
Organized Around:1) Chemicals2) Assays3) Sublists
Navigation and Search Pane
View Pane
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Current Progress on Project
23
3 Standard Views for Assay Results
1) Conc Response2) Single Point3) SimCyp/PK
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Related Projects at The Hamner on Prediction of Human Kinetics/Toxicity
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• 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
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Evaluating the Potential of Genomic Endpoints to Define Mode of Action and Model Low Dose Behavior
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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
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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’
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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
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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...
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Developing a Risk-Oriented Dose Response Analysis Approach
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,...
Average, median, min,...
Thomas et al.Toxicol Sci. 98:240, 2007
5 doses + control (n = 4 - 8 animals per dose)
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Dose Response Analysis of Five NTP Carcinogens
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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
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Developing a Risk-Oriented Dose Response Analysis Approach
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,...
Average, median, min,...
Thomas et al.Toxicol Sci. 98:240, 2007
•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
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Model Fits for an Example Gene
p = 0.755
Benchmark dose (BMD) ~ Transcriptional NOEL
33
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Extension to Transcriptional Dose-Response Data
Group Genes by Functional
Ontologies
Calculate Summary
Values for Each Cellular Function
Average, median, min,...
Average, median, min,...
Thomas et al.Toxicol Sci. 98:240, 2007
Fit Genes With Statistical Models
•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
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0
10
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0 300 600
Rat
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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
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Propylene Glycol Mono-t-butyl Ether
GO Accession GO Term NameTotal
GenesGenes with
BMD BMD Mean BMD SDBMD
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
Other Relevant CategoriesGO:0008284 positive regulation of cell proliferation 189 36 599.4 389.2 92.0 376.0 59.0GO:0006974 response to DNA damage stimulus 228 43 546.1 404.9 84.5 349.9 55.9GO:0006954 inflammatory response 197 28 610.7 392.9 96.8 383.2 61.1
0
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90
0 75 300 1200
Rat
e of
Hep
atoc
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Ade
nom
a/C
arci
nom
a
Dose (ppm)
*
Comparison Between Transcriptomic Dose Response and Tumor Response
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1,2,3-Trichloropropane
GO Accession GO Term NameTotal
GenesGenes with
BMD BMD Mean BMD SDBMD
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
Other Relevant CategoriesGO:0008284 positive regulation of cell proliferation 189 57 36.37 17.70 7.33 22.37 3.95GO:0006974 response to DNA damage stimulus 228 75 36.26 17.62 5.47 22.69 3.31GO:0006954 inflammatory response 197 55 38.32 17.68 5.66 24.89 3.40
0
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60
0 6 20 60
Rat
e of
Hep
atoc
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lar
Ade
nom
a/C
arci
nom
a
Dose (mg/kg)
*
Comparison Between Transcriptomic Dose Response and Tumor Response
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Methylene Chloride (Liver)
0
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70
80
90
0 2000 4000
Rat
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Dose (ppm)
GO Accession GO Term NameTotal
GenesGenes with
BMD BMD Mean BMD SDBMD
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
Other Relevant CategoriesGO:0008284 positive regulation of cell proliferation 189 74 2413.3 1173.5 196.0 1690.0 136.4GO:0006974 response to DNA damage stimulus 228 94 2850.4 987.8 361.3 2042.8 210.9GO:0006954 inflammatory response 197 72 2237.6 1267.5 181.2 1505.9 128.2
*
*
Comparison Between Transcriptomic Dose Response and Tumor Response
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Methylene Chloride (Lung)
GO Accession GO Term Name Total GenesGenes with
BMD BMD Mean BMD SDBMD
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
*
*
Comparison Between Transcriptomic Dose Response and Tumor Response
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Naphthalene (Lung)
GO Accession GO Term NameTotal
GenesGenes with
BMD BMD Mean BMD SDBMD
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
*
Comparison Between Transcriptomic Dose Response and Tumor Response
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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...
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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
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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
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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
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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
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Developing a Nonmonotonic Dose Response Analysis Approach
Dose #1
Dose #2
Control
Dose Y...
Fit Genes With Statistical Models
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
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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
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Current Internal Thinking on Application to Risk Assessment
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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)
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• 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
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• 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
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