Summary of methods developed by Elsevier R&D Solutions to measure risk of clinical toxicities

1. Toxicology Prediction |
Presented By
Date
Predicting Toxicities with Bioassays
Dr. Matthew CLARK
2 December 2016
Elsevier R&D Solutions Services

2. Toxicology Prediction |
• Relating dose-dependent toxicology data to targets inhibited by
drugs finds relationships between targets and observed
toxicology
• Those targets may be markers that predict toxicities
• Correlation is not causation. The value of the target can be enhanced by
gathering more supporting evidence via pathway analysis.
• In some cases the “toxicology” is highly related to the indication
• Using high-quality data from regulatory findings to define toxicology, and
assays reported in literature and patents, we can explore all
toxicology/target relationships to find which are significant.
• A “Toxicology” (e.g. lowering blood pressure) may be an indication; therefore
this may also help find new first-in-class targets and repurpose drugs.
Summary

3. Toxicology Prediction |
1. Separate approved drugs into classes based on observation of adverse
events at the therapeutic dose.
• E.g those that have had arrhythmia reports vs those that do not.
• 9058 unique events/classes of events reported for approved drugs at therapeutic doses
• 3612 approved drugs/formulations
2. For each drug look up all targets inhibited beyond a given threshold value
• E.g. pX > 6 log units.
3. For each adverse event/target combination create a 2x2 contingency matrix
to compute statistical relation
• Measure with chi-squared
• Used chi-squared to filter at 99.999% confidence level
• Measure likelihood ratio – increased odds of adverse event if drug inhibits that target.
• a,b,c,d count of drugs in each category.
Process for Relating Toxicology to Biological Pathways
Drugs with event Drugs w/o event
Target inhibited a b
Target not inhibited c d

4. Toxicology Prediction |
• Statistical methods are well known
• Likelihood ratio : likelihood that a given test result would be expected in a patient with
a disorder compared to the likelihood that same result would occur in a patient without
the disorder.
• Likelihood Ratio
• sensitivity / 1- specificity;
• 7.4 in this case
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Bioassay Data Treated as a Diagnostic Test
Condition positive Condition negative
Test
outcome
positive
True positive
20
False positive
180
Test
outcome
negative
False negative
10
True negative
1820
Sensitivity
TP/(TP+FN) = 0.67
Specificity
TN/(FP+TN) = 0.91
Patients with confirmed bowel cancer
Fecal occult
blood test
screen
outcome
LR Interpretation
> 10
Large and often conclusive increase in the likelihood of
disease
5 - 10 Moderate increase in the likelihood of disease
2 - 5 Small increase in the likelihood of disease
1 - 2 Minimal increase in the likelihood of disease
1 No change in the likelihood of disease

5. Toxicology Prediction |
• Toxicology
• PharmaPendium has reported adverse events for given doses used to classify drugs as
either having/not having the toxicology
• Inhibition
• Reaxys Medicinal Chemistry has bioassay reports
• OpenPhacts data integrated to demonstrate integration of external data
• Biological Pathways
• PathwayStudio
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Data Sources

6. Toxicology Prediction |
Target a b c d Χ2 likelihoodP likelihoodN
Diagnostic
OddsRatio
acetylcholine receptor 7 648 0 2304 20.36 1000 0.99 24871.59
dna topoisomerase type ii (atp-
hydrolyzing) 10 645 1 2303 26.43 35.14 0.99 35.49
histamine receptor 19 636 6 2298 39.35 11.14 0.97 11.48
histamine h4 receptor 12 643 4 2300 23.09 10.55 0.98 10.77
voltage-gated sodium channel 11 644 4 2300 20.04 9.67 0.98 9.87
histamine h1 receptor 63 592 30 2274 113.15 7.39 0.92 8.03
acetylcholinesterase 14 641 7 2297 21.8 7.03 0.98 7.17
potassium voltage-gated channel
subfamily h member 2 44 611 23 2281 72.82 6.73 0.94 7.16
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Example – Targets Related to Arrhythmia
7 drugs that cause arrhythmia inhibit acetylcholine receptor (a) no drugs that do not
cause arrhythmia inhibit it (c). Therefore inhibiting acetylcholine receptor is identified
as being a marker for a drug causing arrhythmia.
This can be understood further with pathway analysis

7. Toxicology Prediction |
• Histamine H1 receptor relation to arrhythmia may be less well known than
others, but has been reported
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Finding Supporting Evidence with Text Mining

8. Toxicology Prediction |
Target a b c d
Chi
squared
Likelihood
P
Likelihood
N
Diagnostic
Odds
Ratio
prostaglandin e2 receptor ep3 subtype 9 85 0 2865 244.48 274305.7 0.9 304784.1
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Prostaglandin – Blood Pressure Reduction Link
This relationship is known, but this data mining may identify new
therapeutic targets to treat hypertension and other diseases
In this case lowering blood
pressure is reported as an
adverse event, however it
could be your endpoint!

9. Toxicology Prediction |
• No direct link in literature, but there are pathways that link CA with this
significant, and common, adverse event
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Urticaria (Rash)
Target a b c d X2
likelihood
P
carbonic anhydrase 5b, mitochondrial 15 1,011 1 1,932 22.23 28.26
carbonic anhydrase 15 19 1,007 3 1,930 23.90 11.93
carbonic anhydrase 4 18 1,008 3 1,930 22.11 11.30

10. Toxicology Prediction |
• Some targets appear implicated in several related toxicities
• HIV protease activity is related to peripheral neuropathy
• That is not a human target, so must be off target activity causing issue
• Is the human target binding site similar to HIV protease’s? If so can it be a surrogate
assay?
• The goal is that an inexpensive bioassay can be linked to a
clinical toxicology and be used to assess risk.
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Observations
Target Number of Toxicities
sodium-dependent serotonin transporter 183
alpha-1a adrenergic receptor 181
sodium-dependent dopamine transporter 172
serotonin transporter 167

11. Toxicology Prediction |
• Assumes that all relevant bioassays have been performed
• Choice of assays may be biased – certain drugs are looked at only for specific indications
• We can create predictive models to fill in “holes” where compounds do not have actual
assay data
• Some toxicities are related to the indication
• E.g. cancer patients have many reported events related to their disease. Like death.
• Many kinase targets are thus linked with death.
• Assignment of toxicology has some imprecision
• “Peripheral neuropathy”, or “peripheral sensory neuropathy”
• We can use higher MedDRA levels to help normalize from preferred-term level
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Limitations