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1
Bayesian Models
for Chagas Disease
Kimberley M. Zorn, Mary A. Lingerfelt, Jair L. de Siqueira-Neto,
Alex M. Clark, Sean Ekins
2
Epimastigote stage in the bug
Trypomastigote stage to travel
Amastigote stage to replicate
3
▶ 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
4
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)
5
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
6
Vehicle | Pyronaridine
Ekins et al., PLoS Negl Trop
Dis. 2015 Jun 26;9(6):e0003878
How can the everyday scientist
use Machine Learning?
7
Private Data
Public Data
Predict Activity
8
AID 2044/2010 in Assay Central
9
▶ Inconclusive = Inactive
▶ EC50 (< 1 µM)
▶ 1853 actives
▶ ROC = 0.78
▶ EC50 + Cytotoxicity (> 10 fold)
▶ 1689 actives
▶ ROC = 0.80
Subvalidations in Assay Central
10
▶ 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
11
AID2044 predicting Test2017 AID2044+Ekins predicting Test2017
Chagas Models in Assay Central
12
▶ 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!
13
▶ 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?
14
▶ 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!
15
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
16
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
17
Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.
Drug Repurposing for Tuberculosis
18
▶ 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
19
TB Subvalidations
Work completed by Tom Lane

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Bayesian Models for Chagas Disease

  • 1. 1 Bayesian Models for Chagas Disease Kimberley M. Zorn, Mary A. Lingerfelt, Jair L. de Siqueira-Neto, Alex M. Clark, Sean Ekins
  • 2. 2 Epimastigote stage in the bug Trypomastigote stage to travel Amastigote stage to replicate
  • 3. 3 ▶ 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
  • 4. 4 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) 5
  • 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 6 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
  • 8. 8
  • 9. AID 2044/2010 in Assay Central 9 ▶ Inconclusive = Inactive ▶ EC50 (< 1 µM) ▶ 1853 actives ▶ ROC = 0.78 ▶ EC50 + Cytotoxicity (> 10 fold) ▶ 1689 actives ▶ ROC = 0.80
  • 10. Subvalidations in Assay Central 10 ▶ 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 11 AID2044 predicting Test2017 AID2044+Ekins predicting Test2017
  • 12. Chagas Models in Assay Central 12 ▶ 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!
  • 13. 13 ▶ 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? 14 ▶ 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! 15 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
  • 16. 16 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 17 Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.
  • 18. Drug Repurposing for Tuberculosis 18 ▶ 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