Collaborative Drug Discovery: An Alternative Business Model For Drug Discovery

Sean Ekins, M.Sc., Ph.D., D.Sc.Collaborations Director,

Collaborative Drug Discovery, Inc.

Introduction• 2003: Envisioned CDD

• 2004: Spun out of Lilly by Dr. Barry Bunin

• 2005: Eli Lilly co-invested in a syndicate with Omidyar Network and Founders Fund

• 2008: BMGF 2 year grant to support TB research ($1,896,923)

• 2010: STTR phase I with SRI TB – chem-bioinformatics integration ($150K)

• 2011: BMGF 3 year grant to support 3 academia: industry TB Collaborations (~$900,000)

• MM4TB 5 year EU Framework 7 funded project (Euro 249,700)• Bio-IT World Best Practices Award, Editors Choice• SBIR phase I ($150K)• 5 year NIH NIDA contract

Private and profitable

Overview: Sharing data and models to speed up drug discovery

Introduction

Collaboration 1. Pfizer - developing, validating and deploying open source ADME/Tox models

Collaboration 2. Tuberculosis research funded by the Bill & Melinda Gates Foundation,

Collaboration 3. European Commission FP7 funded More Medicines for Tuberculosis project

Collaboration 4. NIH funded STTR with the Stanford Research Institute International - TB drug discovery

A Starting Point For A New Research Era?

How to do it better?Openness

What can we do with software to facilitate it ?Make it Open

The future is more collaborative and Open

We have tools but need integrationOpen interfaces

A core root of the current inefficiencies in drug discovery are due to organizations’ and individual’s barriers to collaborate effectivelyBunin & Ekins DDT 16: 643-645, 2011

• Groups involved traverse the spectrum from pharma, academia, not for profit and government• More free, open technologies to enable biomedical research• Precompetitive organizations, consortia..

How Can Collaborative Software Help?

• CDD Vault – Secure web-based place for private data – private by default

• CDD Collaborate – Selectively share subsets of data

• CDD Public –public data sets - Over 3 Million compounds, with molecular properties, similarity and substructure searching, data plotting etc

• Unique to CDD – simultaneously query your private data, collaborators’ data, & public data, Easy GUI

Overview of CDD

About CDDNetwork

Traction: thousands of leading researchers log into CDD today:

Academic customers: Harvard, Columbia, Johns Hopkins, UCSF (new assays)

Pharmas relationships: Pfizer, GSK, Novartis, Lilly (commercial partners)

Startups

Research institutes, Non profits NIH, BMGF, MM4TB etc

Neutral

Trusted for >7 years in the cloud

Moral high-ground due to years dedicated to neglected disease

Credible position

IP

CDD handles data corresponding to composition of matter & utility patents

Templates for rapid web-based transactions (IP corresponding to data)

CDD does not own IP

Collaboration 1. Needs Challenge..There is limited access to ADME/Tox data and models

needed for R&D

How could a company share data but keep the structures proprietary?

Sharing models means both parties use costly software

What about open source tools?

Collaborators had never considered this - So we proposed a study and Rishi Gupta generated models

Collaboration 1. Strategy Open algorithms, descriptors, closed data – can we unlock it?

Massive datasets 10’s- 100’s of thousands

We found open source = commercial tools

Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

CDK +fragment descriptors MOE 2D +fragment descriptorsKappa 0.65 0.67

sensitivity 0.86 0.86specificity 0.78 0.8

PPV 0.84 0.84

Collaboration 1. Opportunity

ADME/Tox Data crosses diseases

Potential to share models selectively with collaborators e.g. academics, neglected disease researchers

We used the proof of concept to submit an SBIR “Biocomputation across distributed private datasets to enhance drug discovery”

Develop prototype for sharing models securely- collaborate to show how combining data for TB etc could improve models

Phase II- develop a commercial product that leverages CDD

Collaboration 1. Future - Gain more by sharing more

Combining models may give greater coverage of ADME/ Tox chemistry space and improve predictions?

LundbeckPfizer

Merck

GSK

Novartis

Lilly

BMS

AllerganBayer

AZ

Roche BI

Merk KGaA

1. Spend less on data generation, descriptors and algorithms – use more open source – use models to help refine testing, external collaborators test your drugs

2. Selectively share data & models with collaborators and control access3. Have someone else host the models / predictions4. Predicting properties without the need to know the structures

Inside company

Collaborators

Databases, servers

Current investmentsSoftware >$1M/yrData >$10-100’s M/yr

Collaboration 2. 3 Academia/ Govt lab – Industry screening partnerships

CDD used for data sharing / collaboration – along with cheminformatics expertise

Previously supported larger groups of labs – many continued as customers

Collaboration 2. ~20 public datasets for TB

>300,000 cpds

Data used for models

100K library Novartis Data FDA drugs

Suggests models can predict data from the same and independent labs

Initial enrichment – enables screening few compounds to find actives

Ekins and Freundlich, Pharm Res. 2011 Ekins et al., Mol BioSyst, 6: 840-851, 2010

Collaboration 3.

20 groups academia + AZ, Sanofi-Aventis, Tydock Pharma

Goal to discover drugs for TB

Use CDD to share data / collaboration – single vault

Bi annual face to face meetings

Collaboration 4. Phase I STTR - NIAID funded collaboration with Stanford Research International

Combining cheminformatics methods and pathway analysis

Used resources available to both to identify targets and molecules that mimic substrates

Computationally searched >80,000 molecules - test 23 compounds in vitro, lead to 2 proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 mg/ml)

POC took < 6mths

Submitted phase II STTR, Submitted manuscriptEkins et al,Trends in Microbiology Feb 2011

Ekins et al, Trends in Microbiology Feb 2011

a.

A complex ecosystem of collaborations: A new business model

Inside Company

Collaborators

Inside Academia

Collaborators

Molecules, Models, Data Molecules, Models, Data

Inside Foundation

Collaborators

Molecules, Models, Data

Inside Government

Collaborators

Molecules, Models, Data

IP

IP

IP

IP

SharedIP

Collaborative platform/s

Bunin & Ekins DDT 16: 643-645, 2011

Shown how CDD can help collaborations

Shown how Open source software could enable pharmas to share data as models

Develop complete platform for data and model sharing

Increase adoption of CDD

Shift to mobile future – collaboration Apps (MolSync + DropBox + MMDS = Share molecules as SDF files on the cloud = collaborate)

Help more groups discover potential drugs, faster

Current and Future Milestones

Williams et al, Drug Disc Today, 16:928-939, 2011

Off The Shelf Drug Discovery All pharmas have assets on shelf that reached clinic

Get the crowd to help in repurposing / repositioning these assets

How can software help?

- Create communities to test

- Provide informatics tools that are accessible to the crowd - enlarge user base

- Data storage on cloud – integration with public data

- Crowd becomes virtual pharma CROs and the “customer” for enabling services

Key PartnersCollaboration 1. Rishi Gupta, Chris Waller, Eric Gifford, Ted Liston, (Pfizer)

Collaboration 2. Joel Freundlich (Texas A&M), Gyanu Lamichhane (Johns Hopkins) and many others

Collaboration 3. Collaborators at MM4TB

Collaboration 4. Carolyn Talcott, Malabika Sarker, Peter Madrid, Sidharth Chopra (SRI International)

Additional software: ChemAxon, Accelrys

Funding: Bill and Melinda Gates Foundation, NIAID

Development : Colleagues at CDD

Users: CDD Customers

We need you to use collaborative tools and lobby pharma to share data securely – tell everyone!

We need the audience to collaborate – we can help

Summary Companies can share their data securely but rarely do

Open and commercials Tools could facilitate this

Neglected disease researchers could benefit

Discovery of hits and leads and ultimately drugs faster

Online platforms have made transactions easier

Could consortia of organizations allows similar efficiencies in drug discovery?

An ideal, web-based ecosystem would be inclusive and address both scientific and business inefficiencies in a systematic, technology driven manner.