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Bayesian Models

for Chagas Disease

Kimberley M. Zorn, Mary A. Lingerfelt, Jair L. de Siqueira-Neto,

Alex M. Clark, Sean Ekins

2Epimastigote stage in the bug

Trypomastigote stage to travel

Amastigote stage to replicate

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▶ Asymptomatic for ~70% of people (infected for life)

▶ Fatal cardiac, neurological, & digestive symptoms can

develop up to 25 years later

▶ Curable… if caught early

▶ Current treatments are not approved in the United States

Chagas Disease

NifurtimoxBenznidazole

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Epidemiology

Estimated of 300-500K

in the United States

Estimated 7-8 million

infected worldwide

https://www.cdc.gov/parasites/chagas/gen_info/vectors/index.html

https://www.dndi.org/diseases-projects/chagas/

Machine Learning and Drug Discovery

▶ Simply put: Molecular pattern recognition of biological data

▶ Fingerprints to identify these patterns

▶ Define active and inactive features

▶ Statistics to watch for: Receiver Operator Characteristic (ROC)

▶ Used to generate predictions for drug activity at a certain target

▶ Real life example - Pyronaridine (an approved antimalarial)

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Pyronaridine, Repurposed▶ Broad Institute, 4064 compounds

▶ PubChem AID 2044 (EC50)

▶ 1853 active compounds (EC50 < 1 µM)

▶ PubChem AID 2010 (Cytotoxicity)

▶ 1698 active compounds (>10 fold difference in EC50)

▶ ~ 100 compounds tested in vitro, eleven had EC50 < 10 µM

▶ Pyronaridine: 85% in vivo efficacy, EC50 = 225 nM

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Vehicle | Pyronaridine

Ekins et al., PLoS Negl Trop

Dis. 2015 Jun 26;9(6):e0003878

How can the everyday scientist

use Machine Learning?

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Private Data

Public DataPredict Activity

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AID 2044/2010 in Assay Central

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▶ Inconclusive = Inactive

▶ EC50 (< 1 µM)

▶ 1853 actives

▶ ROC = 0.78

▶ EC50 + Cytotoxicity (> 10 fold)

▶ 1689 actives

▶ ROC = 0.80

Subvalidations in Assay Central

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▶ Testing AID 2044 vs Ekins

▶ Defined testing/training set

▶ Threshold = 1 µM

▶ Six actives

▶ ROC = 0.72

▶ What else can we do with

Ekins results?

Predict Test Retrain

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AID2044 predicting Test2017 AID2044+Ekins predicting Test2017

Chagas Models in Assay Central

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▶ Tulahuen strains targeting specific life cycle stage

▶ Combined strains or stages

▶ Ki measurements

▶ PubChem data discussed herein

▶ Target specific models (cruzain & cruzipain)

▶ Various thresholds

▶ More to come!

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▶ CPI database currently contains > 150 models

▶ Molecular properties, Disease & ADME Targets

▶ Predictions for more than ten ongoing projects

▶ Assay Central compound predictions being selected for

T. cruzi bioactivity testing

▶ Share models with Java executable on any computer

www.assaycentral.org

How would you care to collaborate?

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▶ Inexpensive, fast & easy

▶ We need more data & feedback

▶ Curious about your compounds?

Predict them in Assay Central!

▶ Ongoing projects for rare & neglected

disease drug discovery, including

Ebola & TB

More information at:

www.collaborationspharma.com

Thanks!

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Collaborations Pharmaceuticals, Inc.

Dr. Sean Ekins

Dr. Maggie Hupcey

Dr. Mary Lingerfelt

Software + Chagas Testing

Dr. Alex Clark

Dr. Jair de Siqueira-Neto

Funded by R43GM122196 NIGMS

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Data Curation & Management

▶ Collect bioactivity data from public & private sources

▶ Bayesian algorithm

▶ ECFP6 descriptors

▶ GitHub to share datasets and models in-house

▶ Private server for additional data backup in-house

▶ Share executable files over Google Drive or DropBox

Prediction Scores

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Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.

Drug Repurposing for Tuberculosis

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▶ Tuberculosis (https://www.cdc.gov/tb/statistics/default.htm)

▶ 1/3 of the population is infected

▶ 1.8 million deaths in 2015

▶ Assay Central Models (~10)

▶ Public in vitro data & collaborator in vivo data

▶ Targeted models for PyrG & PanK

▶ Predicted compounds & sent for testing

▶ Vendor libraries + FDA approved drugs

▶ Two compounds active at either target, one at both

Work completed by Tom Lane

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TB Subvalidations

Work completed by Tom Lane