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.