This document discusses the Collaborative Drug Discovery (CDD) platform, which aims to facilitate drug discovery collaborations through secure data sharing. Key points: - CDD provides a secure web-based platform (CDD Vault) for researchers to store private data and selectively share subsets with collaborators. It also hosts over 3 million public compounds. - The platform allows users to simultaneously query private, collaborator, and public data. It has been used by thousands of scientists for projects like accelerating tuberculosis drug discovery. - Analysis of data contributions to the platform found it follows a power law distribution, indicating most users only contribute a small amount but a long tail of more engaged users help maximize data sharing

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.

2. www.collaborativedrug.com 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? www.collaborativedrug.com 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. Jun Y. • .. some people would feel comfortable to share their ideas after some literature search or primary research. If so, is it a good practice for collaboration?

4. Why collaboration is important www.collaborativedrug.com

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

7. www.collaborativedrug.com

10. CDD3.0: Single Click to Key Functionality Copyright © 2009 All Rights Reserved Collaborative Drug Discovery

11. Collaborate Mode 1 with single login to multiple groups Consolidated CDD DB Collaborator 1 Collaborator 2 Collaborator 4 Collaborator 3 www.collaborativedrug.com

12. Collaborate Mode 2 – p2p Individual Labs securely sharing data subsets CDD DB group 1 CDD DB group 2 CDD DB group 3 CDD DB group 4 www.collaborativedrug.com

15. The Long Tail and CDD Collated for each user the number of data uploads–de-identified Rank-frequency plot of contributions to CDD. Solid line: power law with alpha = 2.2; dashed line, alpha =2.7. (20% contribute 80%) suggests a power law with a considerable downward tail which is a signature of "saturation" of the audience, i.e. in a fixed universe of users, a majority of possible people are becoming active contributors. Robin Spencer in Ekins et al, Book chapter 2010

16. 15 public datasets for TB >300,000 cpds Patents, Papers Annotated by CDD Open to browse by anyone http://www.collaborativedrug.com/register Molecules with activity against

18. biomolecules essential for metabolism and survival of Mtb and their structural analogs Freundlich et al., Pub # 258 Tues Aug 24 – 5.30-7.30pm Hall C

19. Simple descriptor analysis 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

20. Bayesian Classification Screen 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 www.collaborativedrug.com

21. Bayesian Classification Dose response Good Bad Ekins et al., Mol BioSyst, 6: 840-851, 2010 www.collaborativedrug.com

22. Bayesian Classification Leave out 50% x 100 Ekins et al., Mol BioSyst, 6: 840-851, 2010 www.collaborativedrug.com 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)

23. >10 fold Enrichment with TB Bayesian model Filtering a further 100K compound library Ekins et al., Mol BioSyst, In Press www.collaborativedrug.com 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

24. GSK data– Malaria hits Gamo et al., Nature , 2010, 465 , 305-310

25. Press

27. GSK vs St Jude vs Novartis antimalarial datasets. Ekins and Williams Drug Disc Today In Press Ekins and Williams submitted (2010) a 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

28. Mtb 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 BioSystems In press www.collaborativedrug.com

29. Antimalarial Compound libraries and filter failures Ekins and Williams., submitted (2010) b www.collaborativedrug.com % Failure

30. The future: Alerts in CDD

31. 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

32. Systems biology Pathways analysis Data bases Docking model Commercial/ combinatorial/ corporate library HTS following reactivity rules and property filtering Hit to lead -efficacy vs. target, -whole-cell, -infected organism Pharmacophore, QSAR, ADME filters, target fishing Docking/Virtual screening, and/or structure based methods Phenotypic screening Target-based screening Use phenotypic data with integrated computational methods to suggest potential target/s and optimize ADME properties in parallel, then verify in vitro For chemical probe selection find new compounds that inhibit a target using tightly integrated computational methods then optimize and feedback data to data bases and pathways Hit to lead -efficacy vs. target, -whole-cell, -infected organism Follow up virtual screening Follow up virtual filtering/screening When target identified could pursue target based screening workflow Ekins et al, submitted 2010 c Pharmacophore, QSAR, ADME filters TB screening molecule data base TB screening molecule data base

34. Possessed by a single person, [the process] would remain stationary for a long time, and perhaps would die away; but being made public, it will thrive and improve through the efforts of all. Joseph Louis Gay-Lussac 1839

36. PAPERS 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, In Press 2010. Ekins S and Williams EJ, When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool’s gold, Drug Disc Today, In Press 2010. Ekins S, Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, In Press 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. 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.