Transcript Genomic Technologies and Approaches in Toxicology

Principal component analysis
Vehicle
Vehicle
Treated
Treated
NM_016999
NM_017075
NM_017340
NM_012930
NM_0173060
NM_031315
NM_013214
NM_017321
NM_017177
NM_031853
NM_013561
NM_022407
NM_022298
M11794
BE110688
NM_030850
Heat Map
& Cluster
Analysis
Ethinyl Estradiol 500mg/kg
Ethinyl Estradiol 500mg/kg
Ethinyl Estradiol 500mg/kg
Fenbufen 250mg/kg
Ibuprofen 500mg/kg
Fenbufen 250mg/kg
Ibuprofen 500mg/kg
Fenbufen 250mg/kg
Diflunisal 500mg/kg
Diflunisal 500mg/kg
Benzbromarone 200mg/kg
Diethy-hexyl-phthalate 1000mg/kg
Diflunisal 750mg/kg
Diethy-hexyl-phthalate 1000mg/kg
Benzbromarone 200mg/kg
Diflunisal 500mg/kg
Diflunisal 500mg/kg
Benzafibrate 500mg/kg
Benzafibrate 500mg/kg
Benzafibrate 500mg/kg
Ibuprofen 500mg/kg
Diethy-hexyl-phthalate 1000mg/kg
Clofibrate 600mg/kg
Clofibrate 600mg/kg
WY14643 100mg/kg
WY14643 100mg/kg
WY14643 100mg/kg
WY14643 100mg/kg
Perfluoro-n-heptanoic Acid 150mg/kg
Perfluoro-n-octanoic Acid 150mg/kg
Perfluoro-n-octanoic Acid 150mg/kg
Perfluoro-n-octanoic Acid 150mg/kg
Perfluoro-n-decanoic Acid 50mg/kg
Perfluoro-n-heptanoic Acid 150mg/kg
WY14643 100mg/kg
Clofibrate 600mg/kg
WY14643 100mg/kg
Diiso-nonyl-phthalate 1000mg/kg
Diiso-nonyl-phthalate 1000mg/kg
Perfluoro-n-decanoic Acid 50mg/kg
Perfluoro-n-decanoic Acid 50mg/kg
Log2(expression ratio)
-2
0
2
Applications of genomics in toxicology
Mechanistic Toxicology
• Investigative toxicology
– Hypothesis generation
• Risk assessment
– Understanding the mechanism of toxicity at the molecular
level
Predictive toxicology
• Compound avoidance
– Elimination of liabilities
• Compound selection
– Select compound with least toxic liability from a series
• Compound management
– Tailor conventional studies and perform timely
investigational toxicology studies
Where Predictive & Mechanistic
Toxicology Fit
Drug
Discovery
PreClinical
Testing
Clinical
Development
FDA
Mechanistic studies
Pattern-based
Mechanism-based
Predictive screens
Phase
IV
Mechanistic Toxicology Using
Genomics/Transcriptomics
Morphologic Analysis Correlates with Gene Expression
Changes in Cultured F344 Rat Mesothelial Cells
L. M. Crosby,* K. S. Hyder,† A. B. DeAngelo,‡ T. B. Kepler,§ B. Gaskill, G. R. Benavides, L.
Yoon, and K. T. Morgan (Toxicol Appl Pharmacol. 2000 Dec 15;169(3):205-21.)
The gene expression pattern of mesothelial cells in
vitro was determined after 4 or 12 h exposure to
the rat mesothelial, kidney, and thyroid carcinogen
and oxidative stressor potassium bromate
(KBrO3).
Gene expression changes observed using cDNA
arrays indicated oxidative stress, mitotic arrest,
and apoptosis in treated immortalized rat
peritoneal mesothelial cells.
Morphologic Analysis Correlates with Gene Expression Changes
in Cultured F344 Rat Mesothelial Cells
L. M. Crosby,* K. S. Hyder,† A. B. DeAngelo,‡ T. B. Kepler,§ B. Gaskill, G. R. Benavides, L. Yoon,
and K. T. Morgan (Toxicol Appl Pharmacol. 2000 Dec 15;169(3):205-21.)
Morphologic Analysis Correlates with Gene Expression
Changes in Cultured F344 Rat Mesothelial Cells
L. M. Crosby,* K. S. Hyder,† A. B. DeAngelo,‡ T. B. Kepler,§ B. Gaskill, G. R. Benavides, L.
Yoon, and K. T. Morgan (Toxicol Appl Pharmacol. 2000 Dec 15;169(3):205-21.)
Increases occurred in oxidative stress responsive genes; transcriptional
regulators; protein repair components; DNA repair components; lipid
peroxide excision enzyme PLA2; and apoptogenic components.
Numerous signal transduction, cell membrane transport, membraneassociated receptor, and fatty acid biosynthesis and repair components
were altered
Propose a model for KBrO3-induced carcinogenicity in the F344 rat
mesothelium is proposed, whereby KBrO3 generates a redox signal that
activates p53 and results in transcriptional activation of oxidative stress
and repair genes, dysregulation of growth control, and imperfect DNA
repair leading to carcinogenesis.
Predictive Toxicology
Prediction = Probability
Best estimate from available information
Does not provide definitive result or
answer
Provides alerts and/or guidance
Predictive Toxicology in Compound
Management
In Drug Development
Selection/deselection of compounds
Initiate a proactive investigative toxicology
programme
• to be forewarned is to be forearmed
Risk assessment
• Conventional toxicology studies test the hypotheses
generated by predictive toxicology
• (hazard + dose response + risk = assessment)
Decision making using both sets of data
Pattern-based Predictive Screens
Using Genomics/Transcriptomics
Genomic Profiling - comparing toxins
From Ulrich & Friend (2002) Nature Reviews, 1:84-88
Toxicogenomics-based Discrimination of Toxic Mechanism in
HepG2 Human Hepatoma Cells
ME Burczynski, M McMillian, J Ciervo, L Li, JB Parker, RT Dunn, S Hicken, S Farr & MD Johnson
Toxicological Sciences 58, 399-415 2000
Initial comparisons of the expression patterns for 100 toxic
compounds using a 250 gene microarray failed to discriminate
between toxicant classes
However, taking multiple replicate observations of gene
expression for cisplatin, diflunisal & flufenamic acid yielded a
reproducible discriminatory subsets of genes.
The subsets not only discriminated between the three
compounds but also showed predictive value for the other 100
toxic compounds tested.
“Supervised learning”
• Based on statistics and understanding of mechanism
Application of genomics/transcriptomics
in toxicology - What has been learned?
Hypotheses can be generated
Mechanisms can be unravelled
Profiles can discriminate between compounds
• Understanding molecular mechanisms helps
Profiles can classify compounds/mechanisms
Not a standalone technology to identify
toxicity (never an expectation)
Application of genomics/transcriptomics
in toxicology - Current understanding
Rapid hypothesis generation
Rapid classification
Additive not standalone
• Particularly for mechanistic investigations
Questions of sensitivity/reproducibility
• Most gene expression changes at high doses
• Interlab variation
Developing more realistic expectations
through collaboration and open debate
• ILSI, MGED/EBI database standard
A Few References
Review of Arrays and Data analysis
Lockhart & Winzeler (2000) genomics, gene expression and DNA
arrays. Nature 405:827-836.
Hypothesis generation
Crosby et al (2000) Morphologic analysis correlates with gene
expression changes in cultured F344 rat mesothelial cells. Toxicol.
& Applied Pharmacol. 169:205-221.
Screening
Burczynski et al (2000) Toxicogenomics-based discrimination of
toxic mechanism in HepG2 human hepatoma cells. Toxicological
Sciences 58:399-415.
Waring et al (2001) Clustering of hepatotoxins based on
mechanism of toxicity using gene expression profiles. Toxicology
and Applied Pharmacology
175, 28-42.