Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Bayesian Models for Chagas Disease

537 views

Published on

Oral presentation given in MEDI session at 2017 ACS in DC.
co-authors Kimberley M. Zorn, Mary A. Lingerfelt, Jair L. de Siqueira-Neto, Alex M. Clark, Sean Ekins

describes drug repurposing and machine learning - for more details see www.collaborationspharma.com

Published in: Science
  • Be the first to comment

  • Be the first to like this

Bayesian Models for Chagas Disease

  1. 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. 2 Epimastigote stage in the bug Trypomastigote stage to travel Amastigote stage to replicate
  3. 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. 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. 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. 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. 7. How can the everyday scientist use Machine Learning? 7 Private Data Public Data Predict Activity
  8. 8. 8
  9. 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. 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. 11. Predict  Test  Retrain 11 AID2044 predicting Test2017 AID2044+Ekins predicting Test2017
  12. 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. 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. 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. 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. 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. 17. Prediction Scores 17 Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.
  18. 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
  19. 19. 19 TB Subvalidations Work completed by Tom Lane

×