Use functional group frequency rules and biological informatics to mine hepatotoxicity biomarkers

1. An InSilico driven in-vitro
organotoxicity->hepatotoxicity
platform

2. Overview
 Ontomine-wet lab bridge
 Why do we need our own platforms
 Criteria for insilico>in-vitro->in-vivo platforms
 Data Collection
 Ontomine Analysis & Matrix Processing and Analysis
 Annotation of result
 Gene Expression Analysis
 Final BioMarkers

3. Ontomine-wetlab bridge
Ontomine is pretty smart at predicting bioactivity and
toxicity endpoints from 2d chemical structure. (e.g. Redoxis,
Egyptian study)
Ontomine can link drug targets affected with particular
biological or toxicity endpoints. This increases confidence in
predictions.
Publication searches, patent searches and microarray
analysis along with protein expression and structure
information can help discover toxicity linked proteins.
Thus we can potentially discover predictive or diagnostic
biomarkers of hepatotoxicity that need to be validated in the
wet lab.
Validated biomarkers can be patented and a proprietary high
throughout platform developed for drug discovery services.

4. •Differentiation from competition. So many CRO's with the same offerings!
• How long can we get business on labor cost alone or the “INDIA ADVANTAGE”?
•Scientific excellence. More high profile customers will turn to us if we have
proprietary platforms that are comparable if not better and faster.
•Long term business potential. In this competitive sector, IP is the only way to
survive in the long term
•Informatics or offering specific wet lab services cannot hope to have the impact
that a combined offering can have.
•Informatics need not be a precursor to wet lab analysis. It can also serve as an
engine for discovering interesting things e.g. biomarkers , lead identification tools.
•We all get an exciting scientific career and not a 9-5 job running assays.
The need to build platforms

5. 
Novel patentable Proteins / mRNA/ genes as
biomarkers.

High throughput formats. Choice of Multiple
technologies.

Lower costs was compared to other options in early
discovery.

Focus on high value end points e.g. hepatotoxicity,
renal toxicity etc. that have been lead causes of drug
withdrawals

Development of (preferably) non-invasive
hepatotoxicity diagnostic platform

Diagnostic or predictive?
Criteria for Toxicicity platform

6. 
Selection of model compounds for hepatotoxic
mechanism from reliable sources like toxicology
journals/encyclopedia/webresources etc

Compounds with wide variety of end point
pathologies like General hepatotoxicity,
Cholestasis, Necrosis and others were used.

Drug-Toxicity Matrix Preparation (~50
compounds were considered)
Data Collection
Compound Cholestasis Hepatitis Inducer/enlarger Necrosis
Flutamide -1 -1 -1 1 1 -1 -1 -1 -1
Methyldopa -1 -1 -1 1 -1 -1 -1 -1 -1
Dantrolene -1 -1 -1 1 -1 -1 -1 -1 -1
Diflunisal -1 -1 -1 1 -1 -1 -1 -1 -1
Perhexilene -1 -1 -1 1 -1 1 -1 1 -1
Genotoxic
carcinogen
Non-
genotoxic
Microvesicular
steatosis
Macrovesicular
steatosis
Steatosis
General

7. 03/23/09
Matrix
Generation
Primary matrix with 788 Targets
Secondary matrix with 116 Targets
PROCESS-I
MATRIX GENERATION
ONTOMINE
PROCESSING
Ontomine
Predicted
788 Targets
50-Known
Hepato
Toxins
Ontomine Analysis

8. 03/23/09
Result Annotation
• Predicted biomarkers were annotated
for biological relevance (NCBI-Gene DB),
literataure mining (PubGene), secretory
behaviour (Secretory Protein Database
[SPD]), tissue expression (Expasy, PIR),
protein structure information (PDB)

9. 03/23/09
Gene Expression Analysis
• Toxicogenomics data was taken from
NCB-GEO (Accession :GSE13442)
• Experiment Title : “Blood gene expression of
rats exposed to acetaminophen (hepatotoxic) or
methyl parathion (neurotoxic)”
• Experiment Design : 38 samples were analyzed in this
experiment. 15 rats were intraperitoneally administered
acetaminophen (AP), 15 rats were intraperitoneally administered
methyl parathion (MP), and 8 rats were intraperitoneally
administered vegetable oil. At time intervals of 4, 12, and 24 hours
following administration of acetaminophen or methyl parathion, 4
rats per chemical were sacrificed and blood was collected. At the
168-hour time interval, 3 rats per chemical were sacrificed and
blood was collected. The control animals were sacrificed at the 24-
hour time interval. Therefore, there were four rats in each of the 4-,
12-, and 24-hour time points for each chemical and four control rats
for each chemical. Similarly, there were three rats in the 168-hour
time interval for each chemical.

10. 03/23/09
Gene Expression Analysis
• QC analysis : Genes with S/N value >3 and flag
< 5000 in atleast 60% of samples were
retained (ABI specification)
• Differential expressed genes were found by
linear modeling (R package) with a cutoff BH
corrected pValue of 0.01 (1216 DE genes)
• Rat gene symbols were translated to
corresponding human gene symbol with the
help of HomoloGene ids. (for result
comparison)

11. 03/23/09
Gene
Expression
Analysis
1216 DE genes
Secretory,
Toxico-
genomics
Literature
Search
for
115
Targets
Tissue
Expression
81 putative
novel BM
34 BM with
literature info
Final Results:
* 11 novel biomarkers expressed in
blood (5 are non-patented)
Gene Names Description
NCF1 Neutrophil cytosolic factor 1
MCL1 Mcl-1
BCL2A1 Bcl-XL
EIF2B5 Catalytic epsilon subunit of the translation initiation factor eIF2B
HSP90AB1 90-kda heat shock protein beta HSP90 beta

12. 03/23/09
Gene Names Description
NCF1 Neutrophil cytosolic factor 1
MCL1 Mcl-1
BCL2A1 Bcl-XL
EIF2B5 Catalytic epsilon subunit of the translation initiation factor eIF2B
HSP90AB1 90-kda heat shock protein beta HSP90 beta
CLK3 CDC-like kinase 3 isoform hclk3
CDC42 Cell division cycle 42 (GTP binding protein, 25kDa)
KLF5 Kruppel-like factor 5
G6PD Glucose-6-phosphate dehydrogenase isoform a
HIF1A Hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)
NR4A1 Nuclear receptor subfamily 4, group A, member 1
Biomarkers list

13. 03/23/09
Biomarkers Validation using
Ontomine
• Validation set with known toxicity were
taken (they were not part of Ontomine KB)
• Predicted toxicity by Ontomine: Hepatitis &
Necrosis.
• Target proteins were compared with 11
Biomarkers identified by our analysis.
– 10 out of 11 biomarkers were found :)

14. 03/23/09
For here to ?
• Study CRO's offering toxicity
platforms, costs, strategies
• Extend functionality to more organ
toxicity end points
• More specific sub-categories in
hepatotoxicity.
• Design in-vitro, vivo assays
• Validation and patenting