Collaborative innovation in biomedicine talk, April 5 2011

1. Collaborative Drug Discovery: A Platform For Transforming Neglected Disease R&D and Beyond Sean Ekins Collaborative Drug Discovery, Burlingame, CA. Collaborations in Chemistry, Jenkintown, PA. Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, NJ. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD. www.collaborativedrug.com

2. In the long history of human kind (and animal kind, too) those who have learned to collaborate and improvise most effectively have prevailed. Charles Darwin

3. What does "Collaboration" mean to you? Michael Pollastri • collaboration, to me, means that folks from disparate disciplines or skills work together towards the same end-goal. … A collaboration means free and open data sharing, transparent goals and intentions, and a relationship that allows open (frank) and constructive discussion. Markus Sitzmann • The internet is the perfect place to share (certain) data and many of the new technologies and format available at the Web (REST, SOAP etc.) are perfect to use data collaboratively.

4. Typical Lab: The Data Explosion Problem & Collaborations DDT Feb 2009

7. CDD: Single Click to Key Functionality

8. CDD: Mining across projects and datasets

9. CDD: 2 way linking with ChemSpider www.chemspider.com

11. > 15 public datasets for TB Including Novartis data on TB hits >300,000 cpds Patents, Papers Annotated by CDD Open to browse by anyone http://www.collaborativedrug.com/register Molecules with activity against

12. Ekins et al, Trends in Microbiology Feb 2011

13. Visualizing in CDD www.collaborativedrug.com

14. Searching for TB molecular mimics Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Mbio, 2: e00301-10, 2011

15. Simple descriptor analysis on > 300,000 compounds 4.72 (1.99) 77.75 (30.17)** 42.43 (8.94)* 0.12 (0.34)** 4.24 (1.58) 1.11 (0.82)** 3.38 (1.36)** 352.59 (70.87) Inactive < 90% inhibition at 10uM (N =100,931) 4.76 (1.99) 70.28 (29.55) 41.88 (9.44) 0.19 (0.40) 4.18 (1.66) 0.98 (0.84) 4.04 (1.02) 349.58 (63.82) Active ≥ 90% inhibition at 10uM (N =1702) TAACF-NIAID CB2 4.91 (2.35) 85.06 (32.08)* 43.38 (10.73) 0.09 (0.31)** 4.86 (1.77) 1.14 (0.88) 2.82 (1.44)** 350.15 (77.98)** Inactive < 90% inhibition at 10uM (N = 216367) 4.85 (2.43) 83.46 (34.31) 42.99 (12.70) 0.20 (0.48) 4.89 (1.94) 1.16 (0.93) 3.58 (1.39) 357.10 (84.70) Active ≥ 90% inhibition at 10uM (N = 4096) MLSMR RBN PSA Atom count RO 5 HBA HBD logP MWT Dataset

16. Bayesian Classification Models Good Bad active compounds with MIC < 5uM Laplacian-corrected Bayesian classifier models were generated using FCFP-6 and simple descriptors. 2 models 220,000 and >2000 compounds Ekins et al., Mol BioSyst, 6: 840-851, 2010

17. Bayesian Classification Dose response Good Bad Ekins et al., Mol BioSyst, 6: 840-851, 2010

18. Bayesian Classification TB Models Leave out 50% x 100 Ekins et al., Mol BioSyst, 6: 840-851, 2010 65.47 ± 7.96 67.21 ± 7.05 66.85 ± 4.06 0.75 ± 0.01 0.73 ± 0.01 MLSMR dose response set (N = 2273) 77.13 ± 2.26 78.59 ± 1.94 78.56 ± 1.86 0.86 ± 0 0.86 ± 0 MLSMR All single point screen (N = 220463) Sensitivity Specificity Concordance Internal ROC Score External ROC Score Dateset (number of molecules)

19. Ekins and Freundlich, Pharm Res, In press 2011 100K library Novartis Data FDA drugs Additional test sets Suggests models can predict data from the same and independent labs Initial enrichment – enables screening few compounds to find actives 21 hits in 2108 cpds 34 hits in 248 cpds 1702 hits in >100K cpds

20. Novartis aerobic and anaerobic TB hits Ekins and Freundlich, Pharm Res, In press 2011 Anaerobic compounds showed statistically different and higher mean descriptor property values compared with the aerobic hits (e.g. molecular weight, logP, hydrogen bond donor, hydrogen bond acceptor, polar surface area and rotatable bond number) The mean molecular properties for the Novartis compounds are in a similar range to the MLSMR and TAACF-NIAID CB2 hits

21. CDD provides support to 3 groups with their respective 3 major pharma partners Extended BMGF Grant for TB

22. CDD is a partner on a 5 year project supporting >20 labs and proving cheminformatics support www.mm4tb.org More Medicines for Tuberculosis

23. Malaria data in CDD > 22,000 compounds Ekins, Hohman and Bunin in: Collaborative Computational Technologies for Biomedical Research , Edited by Sean Ekins, Maggie A. Z. Hupcey, Antony J. Williams.Published 2011 by John Wiley & Sons, Inc

24. GSK data– Malaria hits Gamo et al., Nature , 2010, 465 , 305-310 http://www.collaborativedrug.com/register

25. http://www.slideshare.net/ekinssean Ekins S and Williams AJ, MedChemComm, 1: 325-330, 2010. Analysis of malaria data

26. Multiple antimalarial datasets Ekins and Williams Drug Disc Today 15; 812-815, 2010 Ekins and Williams, MedChemComm, 1: 325-330, 2010. screening hits in total are not ‘lead-like’ (MW < 350, LogP< 3) closest to ‘natural product lead-like’. Although GSK suggests that the compounds are “drug-like” the evidence for this is weak 5.8 ± 3.0 53.4 ± 21.2 0.2 ± 0.6 5.3 ± 1.5 1.8 ± 1.0 3.8 ± 1.6 341.6 ± 67.0 Antimalarial drugs (N = 14) 7.1 ± 7.7 90.6 ± 104.4 0.6 ± 0.9 5.4 ± 4.7 2.1 ± 3.4 2.2 ± 2.7 458.0 ± 298.6 Johns Hopkins Subset > 50% malaria inhibition at 96h (N = 165) 5.4 ± 9.6 96.0 ±139.8 0.3 ± 0.8 5.1 ± 5.5 2.4 ± 4.6 1.2 ± 3.4 349.1 ± 355.8 Johns Hopkins All FDA drugs (N = 2615) 5.6 ± 3.0 74.7 ± 37.9 0.4 ± 0.7 4.7 ± 2.1 1.2 ± 1.1 3.7 ± 2.0 398.2 ± 105.3 Novartis (N = 5695) 5.2 ±2.3 72.2 ±29.3 0.2 ± 0.4 4.9 ± 1.8 1.1 ± 0.8 3.8 ± 1.6 385.3 ± 71.2 St Jude (N = 1524) 7.2 ± 3.4 76.8 ± 30.0 0.8 ± 0.8 5.6 ± 2.0 1.8 ± 1.0 4.5 ± 1.6 478.2 ± 114.3 GSK data (N = 13,471) RBN PSA (Å 2 ) Lipinski rule of 5 alerts HBA HBD logP MW Dataset

27. TB Compound libraries and filter failures Filtering using SMARTs filters to remove thiol reactives, false positives etc at University of New Mexico (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter) Ekins et al., Mol Biosyst, 6: 2316-2324, 2010

28. Antimalarial Compound libraries and filter failures Ekins and Williams Drug Disc Today 15; 812-815, 2010 % Failure

29. Ekins and Freundlich, Pharm Res, In press 2011 Correlation between the number of SMARTS filter failures and the number of Lipinski violations for different types of rules sets with FDA drug set from CDD (N = 2804) Suggests # of Lipinski violations may also be an indicator of undesirable chemical features that result in reactivity

30. Summary Active compounds vs Mtb and P. Falciparum have higher mean molecular weights and logP values A high proportion of compounds that fail the Abbott filters for reactivity when compared to drugs and antimalarials Understanding the chemical properties and characteristics of compounds = better compounds for lead optimization. St Jude and Novartis datasets should be screened vs Mtb as their property space is close to TB actives GSK compounds may not be an ideal starting point for lead optimization for malaria

34. 2D Similarity search with “hit” from screening Export database and use for 3D searching with a pharmacophore or other model Suggest approved drugs for testing - may also indicate other uses if it is present in more than one database Suggest in silico hits for in vitro screening Key databases of structures and bioactivity data FDA drugs database Repurpose FDA drugs in silico Ekins S, Williams AJ, Krasowski MD and Freundlich JS, Drug Disc Today, In press 2011

36. RELEVANT PAPERS Ekins S, Williams AJ, Krasowski MD and Freundlich JS, In silico repositioning of approved drugs for rare and neglected diseases, Drug Disc Today, In press 2011. Ekins S and Freundlich JS, Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets, Pharm Res, In press 2011. Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Essential Metabolites of M. tuberculosis and their small molecule mimics, Mbio, 2: e00301-10, 2011. Ekins S , Freundlich JS, Choi I, Sarker M and Talcott C, Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery, Trends In Microbiology, 19: 65-74, 2011. Ekins S and Williams AJ, Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs, MedChemComm, 1: 325-330, 2010. Ekins S , Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Yang J, Goncharoff N, Hohman M and Bunin BA, Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis, Mol Biosyst, 6: 2316-2324, 2010. Rishi R. Gupta, Gifford, EM, Liston T, Waller CL, Hohman M, Bunin BA and Ekins S , Using open source computational tools for predicting human metabolic stability and additional ADME/Tox properties, Drug Metab Dispos, 38: 2083-2090, 2010. Ekins S and Williams AJ, When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool’s gold? Drug Disc Today, 15; 812-815, 2010. Ekins S , Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, 27: 2035-2039, 2010. Ekins S . and Williams AJ, Reaching out to collaborators: crowdsourcing for pharmaceutical research, Pharm Res, 27: 393-395, 2010. Ekins S and Williams AJ, Precompetitive Preclinical ADME/Tox Data: Set It Free on The Web to Facilitate Computational Model Building to Assist Drug Development. Lab On A Chip, 10: 13-22, 2010. Ekins S , Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, A Collaborative Database and Computational Models for Tuberculosis Drug Discovery, Mol BioSyst, 6: 840-851, 2010. Williams AJ, Tkachenko V, Lipinski C, Tropsha A and Ekins S , Free online resources enabling crowdsourced drug discovery, Drug Discovery World, Winter 2009/10, 33-39. Louise-May S, Bunin B and Ekins S , Towards integrated web-based tools in drug discovery, Touch Briefings - Drug Discovery, 6: 17-21, 2009. Hohman M, Gregory K, Chibale K, Smith PJ, Ekins S and Bunin B, Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery, Drug Disc Today, 14: 261-270, 2009.

38. Searching for TB molecular mimics Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Mbio, 2: e00301-10, 2011

39. >10 fold Enrichment with TB Bayesian model Filtering a 100K compound library Ekins et al., Mol Biosyst, 6: 2316-2324, 2010 82 (4.82) 107 (6.29) 9.95 (0.58) 600 70 (4.11) 92 (5.41) 8.29 (0.49) 500 58 (3.41) 77 (4.52) 6.63 (0.39) 400 54 (3.17) 64 (3.76) 4.98 (0.29) 300 42 (2.47) 48 (2.82) 3.32 (0.19) 200 24 (1.41) 23 (1.35) 1.66 (0.10) 100 0 0 0 0 dose response Bayesian model (%) single point screening (200k) Bayesian model (%) Random hit rate (%) Number of compounds screened