Collaborative Computational Technologies for Biomedical

Research: An Enabler of More Open Drug Discovery

Sean Ekins, Ph.D., D.Sc.

Collaborations in Chemistry,

Fuquay-Varina, NC.

Antony J. Williams, Ph.D.,

Royal Society of Chemistry,

Wake Forest, NC.

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

Time for Open Drug Discovery? • Pharma Companies spend >$50 billion annually on

R&D

• How much historical data/knowledge/information is in

the public domain? And where is it?

• How much generated data is truly competitive?

• Pre-competitive and public domain data could deliver

high value to drug discovery

– Data mining

– Model-building

– Integrating into in-house and online systems

Is Open Drug Discovery a better way?

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

integration

Open interfaces

• Groups involved traverse the spectrum from pharma, academia, not for

profit and government

• More free, open technologies to enable biomedical research

• Pre-competitive organizations, consortia..

A Starting Point For a New Era?

Open Innovation Open innovation is a paradigm that assumes that firms can and should use external ideas as well as

internal ideas, and internal and external paths to market, as the firms look to

advance their technology

Chesbrough, H.W. (2003).

Open Innovation: The new imperative for creating and profiting from technology.

Boston: Harvard Business School Press, p. xxiv

Collaborative Innovation A strategy in which groups partner to create a product - drive the efficient allocation of R&D

resources. Collaborating with outsiders-including customers, vendors and even competitors-a

company is able to import lower-cost, higher-quality ideas from the best sources in the world.

Open Source While open source and open innovation might conflict on patent issues,

they are not mutually exclusive, as participating companies can donate their patents

to an independent organization, put them in a common pool or grant

unlimited license use to anybody. Hence some open source initiatives

can merge the two concepts

Some Definitions

• All pharmas have similar high level business processes efforts

• Is there any competitive advantage?

• In informatics?

• www.pistoiaalliance.org - companies and vendors

• Agree on the pre-competitive space

• Shift from software to services: e.g. sequence services

• Sequence Squeeze Competition for next generation

sequencing compression algorithm with $15K prize

Major collaborative grants in EU: Framework, Innovative Medicines Initiative…NIH

moving in same direction

Cross-continent collaboration CROs in China, India etc. – Pharma’s in US / Europe

More industry – academia collaboration and ‘not invented here’ a thing of the past

More efforts to go after rare and neglected diseases -Globalization and

connectivity of scientists will be key

Current pace of change in pharma may not be enough.

Need to rethink how we use all technologies & resources…

Collaboration and Openness is Key

Improved Quality of data is essential

Open PHACTS : IMI funded public-private partnership between European Community and EFPIA

Freely accessible for knowledge discovery and verification.

Data on small molecules

Pharmacological profiles

ADMET data

Biological targets and pathways

Proprietary and public data sources.

Where Should We Draw The Pre-competitive Boundary?

Usually on tools

and

technologies for

early drug

discovery

Jackie Hunter has suggested

after Target ID and Validation

Chapter 4 of book..

Why not make everything upto

development precompetitive

e.g. share ADME/Tox data so

everyone understands failures for

a class of compounds?

Share ADME/Tox Models

Gupta RR, et al., Drug Metab Dispos,

38: 2083-2090, 2010

Could All Pharmas Share Their Data and Models?

Pfizer

Merck

GSK

Novartis

Lilly

BMS

Lundbeck

Allergan Bayer

AZ

Roche BI

Merk KGaA

Could combining

models give

greater coverage

of ADME/ Tox

chemistry space

and improve

predictions?

Data, Models and Software Becoming More Accessible- Free, Pre-competitive and Open Efforts - Collaboration

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

Shared

IP

Collaborative platform/s Bunin & Ekins DDT 16: 643-645, 2011

A Complex Ecosystem Of Collaborations A New Business Model?

Example ; Collaborative Drug Discovery Platform

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

• CDD Collaborate – Selectively share subsets of data

• CDD Public – Public data sets

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

www.collaborativedrug.com

Tools for Open Science

• Blogs

• Wikis

• Databases

• Journals

• What about Twitter, Facebook, could these be

used for social collaboration in science?

Tools for Open Science

Name Website Function

myExperiment http://www.myexperiment.org/ Workflows, communities

DIYbio http://diybio.org/ Community for do it yourself biologists

Protocol online http://protocol-online.org/ Biology protocols

Open wetware http://openwetware.org/wiki/Main_Page Materials, protocols and resources

Open Notebook science

challenge

http://onschallenge.wikispaces.com/ Crowdsourced science challenge – initially

on solubility measurement

UsefulChem project http://usefulchem.wikispaces.com/ One scientist’s open notebook

Laboratree http://laboratree.org/pages/home Science networking site

Science Commons http://sciencecommons.org/ Strategies and tools faster, efficient web-

enabled scientific research

WikiPathways http://www.wikipathways.org/index.php/

WikiPathways

Curated biological pathways

Tools for Open Science The Evolution of the e-lab Notebook

• Blogs - Will more scientists blog about work in the future?

• Wikis - more coming a way to track work and build databases

• Apps become e-lab notebooks

• Journals - will more people create their own “journal” ?

• Combine all content - collaborative lab notebook

Scientists will use apps for science Apps connect to databases for content

Mobile Apps for Drug Discovery: Could They Facilitate Open Science?

Williams et al Chapter 28

Could pharma’s biggest

failing have been giving

everyone a PC?

Get the scientist out of their

office and back to the bench

Appify data – make

cheminformatics tools useful

Tablet better than phone?

Williams et al DDT 16:928-939, 2011

Open Drug Discovery Teams

A free App to collate social media

Saves hash tags on a topic

Chemistry aware

A new way to share links & info.

Access open knowledge

An alternative lab notebook

http://slidesha.re/GzVSPr

See Pfizer open innovation & rare disease vision

http://dl.dropbox.com/u/14511423/VRU.pptx

Crowdsourcing: Power law for ChemSpider

• How can we engage more contributors?

• ChemSpider Rank-

frequency plot

• Curation a = 1.4

• Depositions a = 1.5

• Slope is a measure of

contribution by whom

• Driven by very active

minority

• Power laws vary by

crowdsourcing type

Robin Spencer in Chapter 28

Drug Discovery Network

Could our Pharma R&D look like this

Massive collaboration networks – software

enabled. We are in “Generation App”.

Crowdsourcing will have a role in R&D. Drug

discovery possible by anyone with “app access”

Could apps improve crowdsourcing?

Ekins & Williams, Pharm Res, 27: 393-395, 2010.

Getting Chemists and Biologists to Collaborate?

• “Need them to be open minded for research direction”

• “A collaborator is not a means to their ends”

• “In a good collaboration “hypotheses” are viewed as temporary

starting points”

• “Take ownership and responsibility for research success and failure”

Victor Hruby – Chapter 7

• Ethics: effective communication, clear goals, shared and defined

responsibility for writing and publishing

McGowan et al Chapter 8

• Collaboration can be hampered by materials transfer agreements and

patents – need to standardize – use creative commons

• Wilbanks Chapter 9

The Need for Standards for Collaborative Technologies

• 1270 – standard size for bread loaves – Freiberg Germany

• We need standards for assay descriptions, structure representation, how data is

stored, data cleaning etc.

• 2012 – standard for collaborative software?

• Ekins et al Chapter 13

Standard name Website

The Open Biological and Biomedical Ontologies (OBO) http://www.obofoundry.org/

The Ontology for Biomedical Investigators (OBI) http://obi-ontology.org/page/Main_Page

The Functional Genomics Data Society (MGED) http://www.mged.org/index.html

Minimum Information About a Microarray Experiment (MIAME) http://www.mged.org/Workgroups/MIAME/miame.html

The Minimum Information About a Bioactive Entity (MIABE) http://www.psidev.info/index.php?q=node/394

Minimum Information for Biological and Biomedical Investigators (MIBBI) http://www.mibbi.org/index.php/MIBBI_portal

Minimum Information for Publication of real time QT-PCR data (MIQE) http://www.gene-quantification.de/miqe-press.html

Open Science: What is needed?

• Open tools – need good validation studies many

developed with no support

• Support scientists making data open (e.g. Bradley)

• Support companies/groups promoting software for

data sharing

• Lobby grant providers to require that grantees

deposit data in public domain. Make data quality a

criterion for funding

• Engage the community to help create what they

want. Rewards and recognition? - MORE

collaboration can benefit us all

• Give unemployed chemists another route to

discovery – materials, drugs, technologies

Open

Science

needs

You!

Open Science: The Landscape • Currently few scientists practice ONS – so we need to change this

• Missing an open database system for storing/sharing data globally

• Commercial versions exist

• Currently few Open journals – cost may be prohibitive to many

• How do we measure scientists contributions via Open Science?

• The next generation are more likely to use collaborative software

• BIG DATA is on the way

Disruptive Strategy #1: NIH mandates

minimum data quality standards, strict timeline

for data submission, and open accessibility for

all data generated by publicly funded research.

Disruptive Strategy #2: Reboot the industry by

extending the notion of “pre-competitive”

collaboration to encompass later stages of

research to allow public private partnerships

to flourish. The role of large pharma is late

stage development and branding.

Disruptive Strategy #3: FDA takes a proactive

role in making available relevant clinical data

that will help to bridge the “valley of death”.

Ekins et al: Submitted 2012

Three Disruptive Strategies for Removing Drug Discovery Bottlenecks

Wikipedia vs Encyclopedia

Could open drug discovery

disrupt traditional drug

discovery?

Fund and find the right

researchers with

CollaborationFinder

Ensure quality of molecule structures

and data in ChemSpider

Selectively share with collaborators to

retain IP with CDD Openly share findings with other

researchers and public in ODDT Ekins et al: Submitted 2012

Collaborative Informatics Could Disrupt Pharmaceutical Research

Maybe Darwin would have been a biohacker, citizen scientist, open scientist, collaborative

scientist…

Would he have been able to disrupt drug discovery?

Book chapter Authors

Santosh Adayikkoth, Renée JG Arnold, O.K. Baek, Anshu

Bhardwaj, Alpheus Bingham, Jean-Claude Bradley, Samir

K. Brahmachari, Vincent Breton, A. Bunin, Christine

Chichester, Ramesh V. Durvasula, Gabriela Cohen-Freue,

Rajarshi Guha, Brian D. Zhiyu He, David Hill, Moses M.

Hohman, Zsuzsanna Hollander, Victor J. Hruby, Jackie

Hunter, Maggie A.Z. Hupcey, Steve Koch, George A.

Komatsoulis, Falko Kuester, Andrew S.I.D Lang., Robert

Porter Lynch, Lydia Maigne, Shawnmarie Mayrand-Chung,

Garrett J. McGowan, Matthew K. McGowan, Richard J.

McGowan, Barend Mons, Mark A. Musen, Cameron

Neylon, Christina K. Pikas, Kevin Ponto, Brian Pratt, Nick

Lynch, David Sarramia, Vinod Scaria, Stephan Schürer,

Jeff Shrager, Robin W. Spencer, Ola Spjuth, Sándor

Szalma, Keith Taylor, Marty Tenenbaum, Zakir Thomas,

Tania Tudorache, Michael Travers, Chris L. Waller, John

Wilbanks, Egon Willighagen, Edward D. Zanders

&

Mary P. Bradley, Alex M. Clark

Thank You

Email: ekinssean@yahoo.com

Twitter: collabchem

Blog: http://www.collabchem.com/

Slideshare: http://www.slideshare.net/ekinssean

Email: williamsa@rsc.org

Twitter: ChemConnector

Blog: www.chemconnector.com

Slideshare: www.slideshare.net/AntonyWilliams

Many thanks to our collaborators