Immunology journal club, 4/7/2014

1. Repositioning Old Drugs For New
Indications Using Computational
Approaches
Yannick Pouliot, PhD
Khatri Laboratory
4/7/2014

2. Discovering New Drugs Is Getting Much Harder

3. One Approach: Finding Unrecognized
Indications For Existing Drugs
• Most drugs have more than one activity beyond their
approved indication(s)
– usually unrecognized
– example: thalidomide now approved for treatment of myeloma
• Focus of existing drugs because already FDA-approved…
– “known” safety profile
– increasingly off-patent
• But, how to identify unrecognized indications?
– “wet lab” approaches
– computational approaches

4. Target: Inflammatory Bowel
Diseases
Approach Is A Sausage Machine:

5. Inflammatory Bowel Diseases - Overview
• IBD = Ulcerative colitis, Crohn’s disease
• Inflammation of intestinal tract
• Chronic, progressive, episodic
– North America: >1M patients
• High heritability
• No cure; poor treatment options
– surgery
– drugs with serious adverse effects (e.g., corticosteroids)

6. Players In Computational Drug
Repositioning
functional genomics data repository
with >1M samples
integration of medical terminologies,
classification, and coding standards
free relational database server;
dominant in life sciences
free statistical analysis and plotting
language; dominant in life sciences

7. WARNING
If You Go Fishing And Don’t Know
What Fish Look Like, You Will
Definitely Find Something.
It Just Won’t Be A Fish.

8. The False Discovery Rate:
A Key Concept Of Data Mining
“The q-value of a test measures the proportion of false
positives incurred … when that particular test is called
significant” Dabney, A., Storey, J. & Warnes, G., R Package "qvalue". (2011).
• When evaluating many significance test results, you must have an a
priori expectation of what you will consider to be a “true” result
across many tests, as well as for individual significance tests
• A significance test whose value results in rejecting the null
hypothesis can still be a false positive because “too many” tests
were performed.
• Controlled by multiple hypothesis testing, and the resulting q-value
threshold.
• Relies on “bootstrapping” permutation to generate a distribution of
scores based on observed values

9. Hypothesis
Drugs that increase gene expression in the reverse direction to
what is observed in IBD vs. normal tissues should decrease
symptoms
Assessment Method:
1. Characterize the effect of drugs on human gene transcript
levels
2. Characterize the difference in human gene transcript levels
between disease and normal tissue pairs
3. Find drugs that induce the reciprocal signature observed in
disease

10. Source Data
Disease data
• Assemble MySQL database of 176 gene expression microarray
datasets from GEO
– diseased vs. normal tissue pairs
– 100 specific diseases manually reviewed and encoded using UMLS
identifiers
Drug data (“Connectivity Map”)
Gene expression microarray profiles of effects of 164 drugs in:
–
–
–
–
breast cancer: MCF7 epithelial cell line
prostate cancer: PC3 epithelial cell line
leukemia: HL60
melanoma: SKMEL5

11. Finding Drug Candidates Using Rank-Ordered,
Drug-Disease Anti-Correlation Scores
1. Compute an anti-similarity score for
each drug-disease pairs (anti-correlation
score)
2. Compute P-values of anti-correlation
scores (significance testing) using
distance between observed score vs.
scores of 100 randomly-generated
comparisons
3. Retain correlation that have FDR
values better than 0.05

12. Selected Drug: Topiramate
(Candidate #2)
• Anticonvulsant drug whose activity “may be due to a
combination of potential mechanisms: Blocks neuronal
voltage-dependent sodium channels, enhances GABA(A)
activity, antagonizes AMPA/kainate glutamate receptors, and
weakly inhibits carbonic anhydrase” -- UpToDate
• Off-patent since 2009!

13. Validation In Rat Model of IBD - 1
• Sprague-Dawley rats
treated with TNBS to
induce colitis
• Video endoscopy at days
3 and 7 post-induction
• Readout is fraction of
animals with diarrhea
• Result: treatment with
topiramate decreases
diarrhea (one-way
ANOVA p<0.005)
pos
control
topiramate
prednisolone
neg control

14. Validation In Rat Model of IBD - 2

15. Evaluation of Eight Randomly-Selected
Counter-Expressed Genes
microarray
qPCR
Evaluation of Eight RandomlySelected Counter-Expressed
Genes

16. Since Then…
• Upcoming case report to be published of topiramate working
in a patient refractory to steroids.

17. Summary
• Proof of principle that drugs that affect gene transcript levels
in an anti-correlated manner to the normal/disease pattern
can ameliorate symptoms
• Everything from public domain
– data
– algorithm
– software
•
In principle, applicable to any domain where data exist