predicting toxicities with bioassays

Toxicology Prediction |

Presented By

Date

Predicting Toxicities with Bioassays

Dr. Matthew CLARK

2 December 2016

Elsevier R&D Solutions Services


• 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 alltoxicology/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


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


• 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

4

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

> 10Large 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


• 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

5

Data Sources


Target a b c d Χ2 likelihoodP likelihoodNDiagnosticOddsRatio

acetylcholine receptor 7 648 0 2304 20.36 1000 0.99 24871.59dna topoisomerase type ii (atp-hydrolyzing) 10 645 1 2303 26.43 35.14 0.99 35.49histamine receptor 19 636 6 2298 39.35 11.14 0.97 11.48histamine h4 receptor 12 643 4 2300 23.09 10.55 0.98 10.77voltage-gated sodium channel 11 644 4 2300 20.04 9.67 0.98 9.87histamine h1 receptor 63 592 30 2274 113.15 7.39 0.92 8.03acetylcholinesterase 14 641 7 2297 21.8 7.03 0.98 7.17potassium 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


• Histamine H1 receptor relation to arrhythmia may be less well known than others, but has been reported

7

Finding Supporting Evidence with Text Mining


Target a b c dChi 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

8

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!


• No direct link in literature, but there are pathways that link CA with this significant, and common, adverse event

9

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


• 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.

10

Observations

Target Number of Toxicities

sodium-dependent serotonin transporter 183

alpha-1a adrenergic receptor 181

sodium-dependent dopamine transporter 172

serotonin transporter 167


• 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

predicting toxicities with bioassays

Healthcare