Looking Back at Mycobacterium tuberculosis Mouse Efficacy Testing To Move New Drugs Forward

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ACS 2014 talk in Dallas March 16

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Looking Back at Mycobacterium tuberculosis Mouse Efficacy Testing To Move New Drugs Forward

  1. 1. OBJECTS IN MIRROR ARE CLOSER THAN THEY APPEAR Looking Back at Mycobacterium tuberculosis Mouse Efficacy Testing To Move New Drugs Forward Sean Ekins1,2, Robert C. Reynolds3 , Antony J. Williams4, Alex M. Clark5 and Joel S. Freundlich6 1 Collaborative Drug Discovery 2 Collaborations in Chemistry 3 University of Alabama at Birmingham 4 Royal Society of Chemistry 5 Molecular Materials Informatics 6 Rutgers University – New Jersey Medical School
  2. 2.  Tuberculosis kills 1.6-1.7m/yr (~1 every 8 seconds)  1/3rd of worlds population infected!!!!
  3. 3. streptomycin (1943) para-aminosalicyclic acid (1949) isoniazid (1952) pyrazinamide (1954) cycloserine (1955) ethambutol (1962) rifampicin (1967)  Multi drug resistance in 4.3% of cases  Extensively drug resistant increasing incidence  one new drug (bedaquiline) in 40 yrs
  4. 4. Ponder et al., Pharm Res 31: 271-277, 2014
  5. 5. Over 5 years analyzed in vitro data and built models Top scoring molecules assayed for Mtb growth inhibition Mtb screening molecule database/s High-throughput phenotypic Mtb screening Descriptors + Bioactivity (+Cytotoxicity) Bayesian Machine Learning classification Mtb Model Molecule Database (e.g. GSK malaria actives) virtually scored using Bayesian Models New bioactivity data may enhance models Identify in vitro hits and test models 3 x published prospective tests >20% hit rate Multiple retrospective tests 3-10 fold enrichment N H S N Ekins et al., Pharm Res 31: 414-435, 2014 Ekins, et al., Tuberculosis 94; 162-169, 2014 Ekins, et al., PLOSONE 8; e63240, 2013 Ekins, et al., Chem Biol 20: 370-378, 2013 Ekins, et al., JCIM, 53: 3054−3063, 2013 Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011 Ekins et al., Mol BioSyst, 6: 840-851, 2010 Ekins, et al., Mol. Biosyst. 6, 2316-2324, 2010,
  6. 6. • BAS00521003/ TCMDC-125802 reported to be a P. falciparum lactate dehydrogenase inhibitor • Only one report of antitubercular activity from 1969 - solid agar MIC = 1 mg/mL (“wild strain”) - “no activity” in mouse model up to 400 mg/kg - however, activity was solely judged by extension of survival! Bruhin, H. et al., J. Pharm. Pharmac. 1969, 21, 423-433. . MIC of 0.0625 ug/mL • 64X MIC affords 6 logs of kill • Resistance and/or drug instability beyond 14 d Vero cells : CC50 = 4.0 mg/mL Selectivity Index SI = CC50/MICMtb = 16 – 64 In mouse no toxicity but also no efficacy in GKO model – probably metabolized. Ekins et al.,Chem Biol 20, 370–378, 2013 Taking a compound in vivo identifies issues
  7. 7. Tested >350,000 molecules Tested ~2M 2M >300,000 >1500 active and non toxic Published 177 100s 800 Big Data: Screening for New Tuberculosis Treatments How many will become a new drug? How do we learn from this big data? Others have likely screened another 500,000
  8. 8. Can combining Big and Small data (in vitro, in vivo) help us find better compounds, faster ? Avoid testing as many molecules Need to ensure PK/ADME properties are good …
  9. 9. What is the next bottleneck? $ $ $ $ $Five-fold increase in the publication of TB mouse model studies from 1997 to 2009 Franco, PLoS One 7, e47723 (2012).
  10. 10. Billions of $ of your money spent on TB but no database of mouse in vivo data !
  11. 11. Hunting for the in vivo data It’s out there.. be patient
  12. 12. PubMed, SciFinder (CAS, Columbus OH) Web of Knowledge (Thomson Reuters) Individual journals (e.g. Tuberculosis, the Journal of Medicinal Chemistry, and PLOS journals). Searched for papers with compounds tested in murine acute and chronic Mtb infection models. “tuberculosis and in vivo and mouse”, “Tuberculosis and efficacy and mouse” and “comparison and antituberculosis and mouse”. Compounds from >100 sources -papers, books, slides… Data sources
  13. 13. Building the mouse TB database Manually curated, structures sketched Mobile Molecular DataSheet (MMDS) iOS app or ChemDraw (Perkin Elmer) Downloaded from www.chemspider.com Combined with pertinent data fields 1 log10 reduction in Mtb colony-forming units (CFUs) in the lungs Publically available CDD TB database (In process)
  14. 14. MW AlogP HBD HBA Num Rings Num Arom Rings FPSA RBN Active (N = 362) 417.25 ± 454.39 3.11 ± 2.71* 1.49 ± 2.17 6.68 ± 8.33 2.96 ± 2.09* 1.72 ± 1.46 0.29 ± 0.13* 7.84 ± 22.48 Inactive (N = 411) 386.95 ± 440.40 3.89 ± 4.88 1.39 ± 1.86 5.75 ± 6.20 2.51 ± 2.70 1.90 ± 2.37 0.31 ± 0.14 8.09 ± 16.05 MW AlogP HBD HBA Num Rings Num Arom Rings FPSA RBN Active (N = 362) 417.25 ± 454.39 3.11 ± 2.71* 1.49 ± 2.17 6.68 ± 8.33 2.96 ± 2.09* 1.72 ± 1.46 0.29 ± 0.13* 7.84 ± 22.48 Inactive (N = 411) 386.95 ± 440.40 3.89 ± 4.88 1.39 ± 1.86 5.75 ± 6.20 2.51 ± 2.70 1.90 ± 2.37 0.31 ± 0.14 8.09 ± 16.05 Mean of molecular descriptors for N = 773 in vivo Mtb dataset, comparing actives and inactives The AlogP observation is opposite to what we see for in vitro data
  15. 15. 30 years with little TB mouse in vivo dataMIND THE TB GAP
  16. 16. Where are the New TB drugs to be found? PCA of in vivo (yellow) and compounds with known targets (blue) PCA of TB in vitro actives (blue) TB in vivo (yellow) PCA of In vivo actives and inactives Inactive (blue) Active (yellow)
  17. 17. Machine Learning Models Bayesian Support Vector Machine Recursive partitioning (single and multiple trees) Using Accelrys Discovery Studio and R. RP Forest RP Single Tree SVM Bayesian 0.75 0.71 0.77 0.73ROC 5 fold cross validation
  18. 18. Leave-one- out ROC Leave-out 50% x 100 External ROC Score Leave-out 50% x 100 Internal ROC Score Leave-out 50% x 100 Concordance Leave-out 50% x 100 Specificity Leave-out 50% x 100 Sensitivity 0.77 0.72± 0.02 0.74 ± 0.02 66.91± 2.24 74.23 ± 8.96 58.46 ± 9.19 Bayesian Model Studio and R. Model statistics are reasonable
  19. 19. RP Forest RP Single Tree SVM Bayesian 3 /11 (27.2%) 4/11 (36.4%) 7/11 (63.6%) 8/11 (72.7%) External Test set Studio and R. 11 additional active molecules obtained from 1953-2013
  20. 20. Bayesian Models: Back to the Dark Ages in vivo computational models 24 consensus actives
  21. 21. MoDELS RESIDE IN PAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE How do we share them? How do we use Them?
  22. 22. ECFP_6 FCFP_6 • Collected, deduplicated, hashed • Sparse integers • Invented for Pipeline Pilot: public method, proprietary details • Often used with Bayesian models: many published papers • Built a new implementation: open source, Java, CDK – stable: fingerprints don't change with each new toolkit release – well defined: easy to document precise steps – easy to port: already migrated to iOS (Objective-C) for TB Mobile app • Provides core basis feature for CDD open source model service
  23. 23. Dataset Leave one out ROC Published Reference Leave one out ROC Open fingerprints In vivo data (773 molecules) FCFP_6 fingerprints 0.77 this study 0.75 Combined model (5304 molecules) FCFP_6 fingerprints 0.71 J Chem Inf Model 53:3054- 3063. 0.77 MLSMR dual event model (2273 molecules) and FCFP_6 fingerprints 0.86 PLOSONE 8:e63240 0.83 Same datasets – Versus published data
  24. 24. Open fingerprints and bayesian method used in TB Mobile Vers.2 Predict targets Cluster molecules Could we add in vivo prediction models to this? Ekins et al., J Cheminform 5:13, 2013 Clark et al., submitted 2014
  25. 25. Optimal Human properties Optimal Mouse properties Optimal TB entry properties
  26. 26. In vitro data In vivo data Target data ADME/Tox data & Models Drug-like scaffold creation TB Prediction Tools TB Publications Data sources and tools we could integrate
  27. 27. «Tuberculosis and mouse in vivo model » ~338 papers in PubMed «Malaria and mouse in vivo model » ~314 papers in PubMed We could do this again for a different disease Small data: Mouse In vivo model data
  28. 28. A much higher ratio of compounds were tested in vivo to in vitro in the 1940s-1960s rather than now Infrastructure to provide a clear understanding of the position of compounds in the pipeline is essentially lacking Shortage of new candidates suggest we may lack the commitment and resources we had 6o years ago Use machine learning in vivo models to prioritize Mouse studies Ekins, Nuermberger & Freundlich Submitted The Clock is ticking
  29. 29. Replacement Reduction Refinement Descriptors + Bioactivity Literature data on in vivo testing ~ 1500 molecules Test models with in vivo testing after in vitro ADME and in vivo PK Bayesian, SVM, Trees etc
  30. 30. http://goo.gl/vPOKS http://goo.gl/iDJFR TB Mobile Vers 2 – Is on iTunes and Google play and it is FREE http://goo.gl/7fGFW
  31. 31. Acknowledgments: Richard S. Pottorf, Anna Coulon Spektor, Dr. Barbara Laughon 2R42AI088893-02 from the National Institute of Allergy And Infectious Diseases. (PI: S. Ekins) R43 LM011152-01 from the National Library of Medicine (PI S. Ekins).

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