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Archive, Mine, Collaborate© 2007 Collaborative Drug Discovery, Inc.

Archive, Mine, Collaborate®

Community-Based Collaborative Drug Discovery (CDD) for Neglected Infectious Diseases

Collaborative Drug Discovery, Inc.Barry A. Bunin PhD, CEO & President


Using CDD can save time, money and improve discovery

• Promote discoveries that would not otherwise happen.

• Utilize limited resources more effectively.

• Accelerate the discovery of effective treatments for neglected infectious diseases.

Save time

Save money

Save lives


• Archive– Organize and upload your experimental data

• Mine– Analyze your data to suggest new drug candidates– Helps advance promising drugs

• Collaborate– Keep your data 100% private, – Exchange confidentially with collaborators, or…– Share openly within the CDD community– Publications, collaborations, peer recognition

Collaborative Drug Discovery


CDD Archives data


CDD can be used for Mining data


CDD is Secure & Simple

• Web based (log in securely into to your database from any computer using any common browser – Firefox, IE, Safari)

• Hosted on remote server (lower cost)• Highly secure, all traffic encrypted, server in a secure

professionally hosted environment• Automatically backed up nightly• Export all data to Excel with SMILES, SAR, & png images

https://www.collaborativedrug.com/pages/privacy_and_security

https://www.collaborativedrug.com/pages/privacy_and_security


CDD is facilitating Collaborations

1. Malaria Chemosensitizers (Cape Town, UCSF, Lipinski)

2. Malaria Computational and Experimental around largest set of historical small molecule animal SAR data (UNC, St. Jude)

3. Malaria UGI-4CC Open Collaboration (Drexel-UCSF)

4. Tuberculosis Public Private Partnership Examples (TAACF, Lilly, Cornell)

5. GPCR Ki Database (PDSP, UNC)

6. Many Others (company working on PI3 Kinase for Cancer, vendor with commercial compounds, etc.)


CDD aids Collaborations


CDD Malaria Case Study #1: UCSF/Lipinski/ Cape Town

Testimonial: “One of the biggest barriers for academic drug discovery is the poor access to chemical data represented in an intelligent format. CDD presents data and associated tools that capture the relationship between chemical structure and biological activity. Structure-Activity Relationship (SAR) data substantially improve the distributed drug discovery process.”

— Christopher Lipinski, PhDPfizer, Retired


Prof. McKerrow’s Network:2000 protease inhibitors, >15 collaborations

Roush groupvinyl sulfones

vinyl sulfonatesepoxides

sulfonamidesphosphononamides

phosphonates

Jon EllmanU.C. Berkeley

hydroxymethylketones

Arquleα-ketoamides

Prototekpeptidomimeticswith heterocyclic

trappinggroups

Kelly ChibaleU. of Capetown

thiosemicarbazones,isatins

GSK(Scott Thompson)

2-propanonesacylhydrazines

Celera(Jim Palmer)

vinyl sulfones

Cohen Groupchalconesacylamides

dihydrazides

L. tropica screen withInstitute Pasteur

collaboration

L. major(cat B)(cat L)

T. brucei(cat B)(cat L)

L. mexicana(cpb)(cpc)

(Mottramcollaboration)

P.falciparum(falcipain)

T. cruzi(cruzain)

L. donvani screen

Prof. McKerrow’s Collaborative Network

Walter Reed(WRAIR)quinolines

thiosemicarbazonesWarner Lambert200,000 diverse

Compound library

McKerrow Compounds

Archive, Mine, Collaborate© 2007 Collaborative Drug Discovery, Inc.© 2007 Collaborative Drug Discovery, Inc.

CDD creates a unique R&D Network

Hub Consortium

Neglected Disease Research Community


CDD Malaria Case Study #2: St. Jude CRH, Walter Reed, & UNC


• Topic: Malaria• Connections

Kip Guy, St. JudeMalaria biology, screening

Alex Tropsha Lab, UNCComputational Chemistry, informatics

Malaria data, global

Liying Zhang Hao Zhu

Tropsha-Guy-CDD Malaria Computational-Experimental Collaboration

Goal: Accelerate computational drug discovery for malaria

Bunin, Ekins etc Facilitators


Army Malaria Animal Data from WWII from dozens of collaborations and >12K hand drawn compounds


A Tropsha Group in Silico Protocol Example in CDD


QSAR Models have high prediction accuracy

• 70 compounds in the 429 compounds were randomly selected as external validation set.

• QSAR kNN (k-nearest neighbor) models• Results:

– More than 300 acceptable QSAR classification models.– True negative:83.7% – True positive: 72.4%– Total prediction accuracy: 81.4%

More details see Professor Alex Trophas’ presentations


• Topic: Malaria• Connections

Phil Rosenthal, UCSFMalaria biology, screening

Bradley Laboratory, DrexelSynthetic Chemistry

Malaria data, global

Rajarshi Guha, Indiana UniversityDocking to pre-filter Ugi Library

CDD Case Study #3: Bradley-Guha-Rosenthal-CDD

Goal: Accelerate drug discovery for malaria

Bunin, Ekins etc Community Facilitators


Drexel-UCSF Malaria Data Set within the Context ofOther CDD Community Neglected Disease Data Sets


Useful Chemistry Ugi Compound in CDD (public login: usefulchemistry, Password: cddcdd)


Ugi-4CC Product tested in P. Falciparum Cell Assay is captured in the CDD Database


Open Access Data Easily Exported from CDD Database


CDD Case Study #4: Mining Capabilities between Groups (Ballel vs. TAACF)


Mining Capabilities between Groups (Ballel vs. Known Drugs)


CDD Collaborative Capabilities: Securely View and Analyze Data Sets


CDD Case Study #5: GPCR Ki Database from Roth PDSP site


CDD Archive – Protocols: Roth Ki Database


Mining the Roth data


95% Use Private Groups: My Personal Example


CDD Mining Plugin can show LogD


CDD Mining Plugin can show molecule 3D View


CDD allows data Export


Why is CDD and our collaborators doing this?


For Drug Discovery, Collaborative software can make a difference

• CDD solves complex drug discovery software and data needs

– Balancing openness with user choice

– Efficiencies of integrated specialization balanced with end-user choice for access to sensitive drug discovery data

• Metcalfe’s law and Moore’s law

• CDD for other discovery data: drug, medical, & clinical research


CDD – Community Open Access Data

1. Dr. Christopher Lipinski (ex-Pfizer)Chemoinformatics data on FDA and Orphan approved drugs

2. Prof. Roos (U. Penn)Modern Malaria literature data linked to genes

3. Prof. Gelb (UW)Modern Malaria literature data linked to assays

4. Prof. Guy (St. Jude CRH)>12,000 Army Malaria screening data dating back to WW-II after escrow

period5. Prof. McKerrow (UCSF)

“Open-Content” Drug discovery – T. Brucei, T. Cruzi, S. Mansoni, Leishmania, P. falciparum

6. Dr. Shoichet (UCSF)Aggregator- non-aggregator data

7. Dr. Ekins (UMDNJ, U. Maryland, ex-Pfizer/Lilly)Toxicology data

CDD collaborators

And growing….


Networks Selectively Sharing “Public-Private Partnerships”

# drug discovery possibilities = # of [compounds][targets][indications][experts]

CDD Leverages a network of drug discovery Experts


CDD Collaborative Road-Map:

CDD can be used to manage public and private collaborators


CDD is a growing community scientific community

1. CDD is executing on Geometric Community Growth2. Doubled # in community in last 12 months3. Doubling # in community in next 6 months4. Applications to adjacent markets to scale

collaborative discovery model Any data from excel files, started with SAR

5. CDD best fit for startups, academics, and Foundations


Acknowledgements

• Moses Hohman, PhD (Director Software Engineering)

• Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard (magna cum laude, Physics)

• Sylvia Ernst, PhD (Director Community Growth)

• Accelrys-Scitegic, MDL-Elsevier-Beilstein

• Kellan Gregory (CDD Community Interests)

• Tufts University

• Peter Cohan (BOD)

• Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author.

• Alpheus Bingham, PhD (BOD)

• Lilly, eLilly, Innocentive

• Omidyar Network, Founders Fund, ChemAxon, ASINEX

• …& 100s of collaborators mostly from academia and foundations (some industry)

• Thank you!

http://www.secondderivative.com/


CDD Current Community & Strategic Relationships

• ASINEX• Broad Institute• Cedars-Sinai Medical Center• Columbia University• Cornell University• Drugs for Neglected Diseases initiative • Eli Lilly & Co.• Fred Hutchinson CRC• Harvard University• Indiana University–Purdue University Indianapolis • Johns Hopkins• Louisiana State University • Marine Biological Laboratory • MIT• Myelin Repair Foundation• San Francisco VA Medical Center• Seattle Biomedical Research Institute• Semafore Pharmaceuticals, Inc.

Contact: Barry Bunin, PhD, [email protected]

• Stanford University• St. Jude Children’s Research Hospital • UCSF General Hospital• University of California, Berkeley • University of California, Los Angeles • University of California, San Francisco • University of California, Santa Cruz • University of Cape Town • University of Mississippi • University of North Carolina• University of Pennsylvania • University of Sydney • University of Texas • University of Washington

mailto:[email protected]


Extra slides


CDD facilitates drug discovery

• CDD currently helps…

– Discover new medicines faster

– Focus on important neglected disease research

– Empower individuals and develop drugs

– Broadly improve human health and life


CDD database system: Security overview

• CDD encrypts all traffic between a user's web browser and our servers once the user has logged in.

• CDD does thorough automated testing of our application code, including security tests to make sure one group cannot access another group's private data.

• CDD uses two levels of firewalling ("defense in depth"), hardware and software, for each sensitive server in our server environment.

• CDD encrypts all database and customer file backups using public key cryptography, and the private keys are backed up securely.

• CDD stores user passwords in our database using strong, one-way encryption hashes. • CDD stays current with all security updates to our system software, firmware and

operating systems. • CDD uses Linux systems in production. • A number of application security enhancements around password security are being

made mandatory. • Access to production servers and superuser accounts is limited and audited within CDD.


CDD chemical and biological data with 3rd party models


Where CDD Community is Today

and Where Is It Going Tomorrow?


QSAR Models Based on anti-P. falciparum Activity

• A library of 3133 compounds screened for anti- P. falciparumactivity from Dr. Guy.

• 158 compounds were selected as active.• QSAR kNN (k-nearest neighbor) models developed based on

158 hits + 271 inactive compounds (selected from the whole library) = 429.

• The selected 271 inactive compounds covered most of the chemical space.

Red: selected inactive; Purple: unselected inactive in the whole library.

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