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Principal component analysis
Treated
Vehicle
Treated
Vehicle
Heat Map & Cluster Analysis
-2 0 2
Log2(expression ratio)
-2 0 2
Log2(expression ratio)
Ethinyl Estradiol 500mg/kgEthinyl Estradiol 500mg/kgEthinyl Estradiol 500mg/kgFenbufen 250mg/kgIbuprofen 500mg/kgFenbufen 250mg/kgIbuprofen 500mg/kgFenbufen 250mg/kgDiflunisal 500mg/kgDiflunisal 500mg/kgBenzbromarone 200mg/kgDiethy-hexyl-phthalate 1000mg/kgDiflunisal 750mg/kgDiethy-hexyl-phthalate 1000mg/kgBenzbromarone 200mg/kgDiflunisal 500mg/kgDiflunisal 500mg/kgBenzafibrate 500mg/kgBenzafibrate 500mg/kgBenzafibrate 500mg/kgIbuprofen 500mg/kgDiethy-hexyl-phthalate 1000mg/kgClofibrate 600mg/kgClofibrate 600mg/kgWY14643 100mg/kgWY14643 100mg/kgWY14643 100mg/kgWY14643 100mg/kgPerfluoro-n-heptanoic Acid 150mg/kgPerfluoro-n-octanoic Acid 150mg/kgPerfluoro-n-octanoic Acid 150mg/kgPerfluoro-n-octanoic Acid 150mg/kgPerfluoro-n-decanoic Acid 50mg/kgPerfluoro-n-heptanoic Acid 150mg/kgWY14643 100mg/kgClofibrate 600mg/kgWY14643 100mg/kgDiiso-nonyl-phthalate 1000mg/kgDiiso-nonyl-phthalate 1000mg/kgPerfluoro-n-decanoic Acid 50mg/kgPerfluoro-n-decanoic Acid 50mg/kg
NM
_016
999
NM
_017
075
NM
_017
340
NM
_012
930
NM
_017
3060
NM
_031
315
NM
_013
214
NM
_017
321
NM
_017
177
NM
_031
853
NM
_013
561
NM
_022
407
NM
_022
298
M11
794
BE
1106
88N
M_0
3085
0
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
Phase IV
FDA
Mechanism-based
Mechanistic studies Pattern-based
Predictive screens
Mechanistic Toxicology Using Genomics/Transcriptomics
Morphologic Analysis Correlates with Gene Expression Changes in Cultured F344 Rat Mesothelial CellsL. 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 CellsL. 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 CellsL. 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, membrane-associated 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 = ProbabilityBest estimate from available informationDoes not provide definitive result or answerProvides alerts and/or guidance
Predictive Toxicology in Compound Management
In Drug DevelopmentSelection/deselection of compoundsInitiate 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 classesHowever, 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 generatedMechanisms can be unravelled Profiles can discriminate between compounds
• Understanding molecular mechanisms helps
Profiles can classify compounds/mechanismsNot a standalone technology to identify toxicity (never an expectation)
Application of genomics/transcriptomics in toxicology - Current understanding
Rapid hypothesis generationRapid classificationAdditive 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 ReferencesReview of Arrays and Data analysis
Lockhart & Winzeler (2000) genomics, gene expression and DNA arrays. Nature 405:827-836.
Hypothesis generationCrosby et al (2000) Morphologic analysis correlates with gene expression changes in cultured F344 rat mesothelial cells. Toxicol. & Applied Pharmacol. 169:205-221.
ScreeningBurczynski 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 Pharmacology175, 28-42.
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