1.
1
Bayesian Models
for Chagas Disease
Kimberley M. Zorn, Mary A. Lingerfelt, Jair L. de Siqueira-Neto,
Alex M. Clark, Sean Ekins

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Epimastigote 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
Nifurtimox
Benznidazole

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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/

5.
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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6.
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

7.
How can the everyday scientist
use Machine Learning?
7
Private Data
Public Data
Predict Activity

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9.
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

10.
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?

11.
Predict  Test  Retrain
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AID2044 predicting Test2017 AID2044+Ekins predicting Test2017

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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

14.
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

15.
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

17.
Prediction Scores
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Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.

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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