Principal component analysis

1
Principal component analysis
2
Heat Map Cluster Analysis
3
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

4
Where Predictive Mechanistic Toxicology Fit
Clinical Development
Drug Discovery
PreClinical Testing
Phase IV
FDA
Mechanistic studies
Pattern-based
Mechanism-based
Predictive screens
5
Mechanistic Toxicology Using Genomics/Transcriptom
ics
6
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 15169(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.

7
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 15169(3)205-21.)
8
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 15169(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.

9
Predictive Toxicology

• Prediction Probability
• Best estimate from available information
• Does not provide definitive result or answer
• Provides alerts and/or guidance

10
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

11
Pattern-based Predictive Screens Using
Genomics/Transcriptomics
12
Genomic Profiling - comparing toxins From Ulrich
Friend (2002) Nature Reviews, 184-88
13
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

14
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)

15
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

16
A Few References

• Review of Arrays and Data analysis
• Lockhart Winzeler (2000) genomics, gene
  expression and DNA arrays. Nature 405827-836.
• Hypothesis generation
• Crosby et al (2000) Morphologic analysis
  correlates with gene expression changes in
  cultured F344 rat mesothelial cells. Toxicol.
  Applied Pharmacol. 169205-221.
• Screening
• Burczynski et al (2000) Toxicogenomics-based
  discrimination of toxic mechanism in HepG2 human
  hepatoma cells. Toxicological Sciences
  58399-415.
• Waring et al (2001) Clustering of hepatotoxins
  based on mechanism of toxicity using gene
  expression profiles. Toxicology and Applied
  Pharmacology175, 28-42.