Collaborative Computational Technologies for Biomedical Research E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Series on Technologies for the Pharmaceutical
• Sprache: Englisch

Seitenzahl: 972

Veröffentlichungsjahr: 2011

Beschreibung

Methods, Processes, and Tools for Collaboration "The time has come to fundamentally rethink how we handle the building of knowledge in biomedical sciences today. This book describes how the computational sciences have transformed into being a key knowledge broker, able to integrate and operate across divergent data types."--Bryn Williams-Jones, Associate Research Fellow, Pfizer The pharmaceutical industry utilizes an extended network of partner organizations in order to discover and develop new drugs, however there is currently little guidance for managing information and resources across collaborations. Featuring contributions from the leading experts in a range of industries, Collaborative Computational Technologies for Biomedical Research provides information that will help organizations make critical decisions about managing partnerships, including: * Serving as a user manual for collaborations * Tackling real problems from both human collaborative and data and informatics perspectives * Providing case histories of biomedical collaborations and technology-specific chapters that balance technological depth with accessibility for the non-specialist reader A must-read for anyone working in the pharmaceuticals industry or academia, this book marks a major step towards widespread collaboration facilitated by computational technologies.

Leseprobe

Table of Contents

Cover

Series page

Title page

Copyright page

Epigraph

DEDICATION

FOREWORD

PREFACE

ACKNOWLEDGMENTS

CONTRIBUTORS

PART I: GETTING PEOPLE TO COLLABORATE

1 NEED FOR COLLABORATIVE TECHNOLOGIES IN DRUG DISCOVERY

1.1 INTRODUCTION

1.2 SETTING THE STAGE FOR COLLABORATIONS

1.3 OVERVIEW OF VALUE OF PRECOMPETITIVE ALLIANCES IN OTHER INDUSTRIES

1.4 CONCLUSION

2 COLLABORATIVE INNOVATION: ESSENTIAL FOUNDATION OF SCIENTIFIC DISCOVERY

2.1 DAWNING OF ERA OF COLLABORATIVE INNOVATION

2.2 COLLABORATIVE IMPERATIVE

2.3 CREATING CULTURE OF COLLABORATIVE INNOVATION

2.4 SPIRIT OF INQUIRY: “CRITICAL PARADOX”

2.5 ELIMINATE THE WORD: FAILURE

2.6 EMPOWER INNOVATION CHAMPIONS

2.7 AVOIDING THE TRAPS

2.8 CONCLUSION

3 MODELS FOR COLLABORATIONS AND COMPUTATIONAL BIOLOGY

3.1 INTRODUCTION

3.2 IMPORTANCE OF PARTNERSHIPS

3.3 CONSORTIA MODEL

3.4 EXAMPLES OF SUCCESSFUL LARGE-SCALE PARTNERSHIPS

3.5 OPPORTUNITIES FOR COMPUTATIONAL BIOLOGY RESEARCH PARTNERSHIPS

3.6 CHALLENGES AND OPPORTUNITIES IN COMPUTATIONAL BIOLOGY

3.7 TOOLS FOR INNOVATION IN COMPUTATIONAL BIOLOGY: BIOCONDUCTOR AND R SOFTWARE

3.8 DISCUSSION

4 PRECOMPETITIVE COLLABORATIONS IN PHARMACEUTICAL INDUSTRY

4.1 INTRODUCTION

4.2 EXAMPLES OF PRECOMPETITIVE CONSORTIA

4.3 IMPLEMENTATION AND MANAGEMENT OF PRECOMPETITIVE CONSORTIA

4.4 FUTURE TRENDS

APPENDIX SUMMARY OF PRECOMPETITIVE CONSORTIA

5 COLLABORATIONS IN CHEMISTRY

5.1 INTRODUCTION

5.2 CROWDSOURCING

5.3 COLLABORATORIES

5.4 DATABASES

5.5 BLOGS

5.6 WHERE WILL COLLABORATIVE TECHNOLOGIES TAKE CHEMISTRY?

6 CONSISTENT PATTERNS IN LARGE-SCALE COLLABORATION

6.1 INTRODUCTION

6.2 BACKGROUND

6.3 THE LONG TAIL OF COLLABORATION

6.4 VALUE OF AN IDEA

6.5 COMMUNITIES?

6.6 MOTIVATION AND SUSTAINABILITY

6.7 COLLABORATIVE EVALUATION

6.8 CONCLUSIONS

ACKNOWLEDGMENTS

7 COLLABORATIONS BETWEEN CHEMISTS AND BIOLOGISTS

7.1 INTRODUCTION

7.2 ORGANIZING SUCCESSFUL COLLABORATIONS BETWEEN CHEMISTS AND BIOLOGISTS TO SOLVE IMPORTANT PROBLEMS IN CHEMICAL BIOLOGY AND MEDICINE

7.3 CONCLUDING DISCUSSION

ACKNOWLEDGMENTS

8 ETHICS OF COLLABORATION

8.1 INTRODUCTION

8.2 TEAMWORK, COOPERATION, AND COLLABORATION

8.3 THE IDEAL COLLABORATOR

8.4 INFORMATION TECHNOLOGY ISSUES

8.5 CONCLUSIONS

9 INTELLECTUAL PROPERTY ASPECTS OF COLLABORATION

9.1 BACKGROUND ON INTELLECTUAL PROPERTY RIGHTS

9.2 SECTION I: INTELLECTUAL PROPERTY RIGHTS

9.3 INTELLECTUAL PROPERTY RIGHTS AND DATA

9.4 LICENSING AND CONTRACTS

9.5 CONCLUSION

PART II: METHODS AND PROCESSES FOR COLLABORATIONS

10 SCIENTIFIC NETWORKING AND COLLABORATIONS

10.1 INTRODUCTION

10.2 HISTORY AND BACKGROUND OF SCIENTIFIC NETWORKS

10.3 ONLINE NETWORKS

10.4 LIFE SCIENCES AND THE INTERNET

10.5 NETWORKING AND OPEN-SOURCE DRUG DISCOVERY

10.6 CONCLUSION

11 CANCER COMMONS: BIOMEDICINE IN THE INTERNET AGE

11.1 INTRODUCTION

11.2 GENOME-BASED CANCER TREATMENT, CANCER COMMONS, AND THE MOLECULAR DISEASE MODEL

11.3 UPDATING THE MOLECULAR DISEASE MODEL

11.4 DETAILS OF THE CANCER COMMONS PLATFORM

11.5 DISCUSSION

ACKNOWLEDGMENTS

12 COLLABORATIVE DEVELOPMENT OF LARGE-SCALE BIOMEDICAL ONTOLOGIES

12.1 ONTOLOGIES IN BIOMEDICINE

12.2 COLLABORATIVE PROTÉGÉ

12.3 WEBPROTÉGÉ

12.4 COLLABORATION ARCHITECTURE

12.5 PUBLISHING ONTOLOGIES WITH BIOPORTAL

12.6 DISCUSSION AND FUTURE WORK

ACKNOWLEDGMENTS

13 STANDARDS FOR COLLABORATIVE COMPUTATIONAL TECHNOLOGIES FOR BIOMEDICAL RESEARCH

13.1 WHAT ARE STANDARDS?

13.2 WHY WE NEED STANDARDS FOR COLLABORATION

13.3 HOW WILL WE GET THEM?

ACKNOWLEDGMENTS

14 COLLABORATIVE SYSTEMS BIOLOGY: OPEN SOURCE, OPEN DATA, AND CLOUD COMPUTING

14.1 INTRODUCTION

14.2 TRADITION OF NOT VERY COLLABORATIVE SCIENCE

14.3 IMPACT OF OPEN-SOURCE SOFTWARE ON TRULY COLLABORATIVE SCIENCE

14.4 OPEN DATA STANDARDS: ONTOLOGIES AND INTERCHANGE FORMATS

14.5 NOTE ON ASSESSING OPEN-SOURCE SOFTWARE

14.6 CONSTRAINTS ON OPEN-SOURCE SCIENCE

14.7 USING CLOUD COMPUTING TO ELIMINATE BARRIERS TO COLLABORATION

14.8 ADDITIONAL BENEFITS OF CLOUD COMPUTING FOR SYSTEMS BIOLOGY

14.9 SOME EXAMPLES OF CLOUD-BASED SYSTEMS BIOLOGY TOOLS

14.10 SOME EXAMPLES OF OPEN-SOURCE SYSTEMS BIOLOGY TOOLS IN PROTEOMICS

14.11 PUBLIC DATA REPOSITORIES

14.12 CONCLUSION

15 EIGHT YEARS USING GRIDS FOR LIFE SCIENCES

15.1 INTRODUCTION

15.2 GRIDS FOR E-SCIENCE

15.3 GRIDS TO THINK BIGGER

15.4 GRIDS TO SHARE DATA WHERE IT IS PRODUCED

15.5 GRIDS TO CREATE VIRTUAL RESEARCH COMMUNITIES

15.6 PERSPECTIVES

15.7 CONCLUSION

ACKNOWLEDGMENTS

16 ENABLING PRECOMPETITIVE TRANSLATIONAL RESEARCH: A CASE STUDY

16.1 INTRODUCTION

16.2 ESTABLISHING TRANSLATIONAL RESEARCH INFRASTRUCTURE

16.3 WHY DATA WAREHOUSING

16.4 BUILDING DATA WAREHOUSE

16.5 CONTENT

16.6 DEVELOPMENT METHODOLOGY

16.7 tranSMART DESCRIPTION

16.8 STRATEGIC CONSIDERATIONS

16.9 DISCUSSION

ACKNOWLEDGMENTS

17 COLLABORATION IN CANCER RESEARCH COMMUNITY: CANCER BIOMEDICAL INFORMATICS GRID (caBIG)

17.1 INTRODUCTION

17.2 caBIG COLLABORATION STRATEGY: OVERVIEW

17.3 caBIG COLLABORATION STRATEGY: COMMUNITY

17.4 caBIG COLLABORATION STRATEGY: TECHNOLOGY

17.5 caBIG COLLABORATION STRATEGY: SECURITY

17.6 caBIG COLLABORATION STRATEGY: SUPPORT

17.7 CANCER CENTER DEPLOYMENT

17.8 INTERNATIONAL EFFORTS AND BIG HEALTH

17.9 CONCLUSION

18 LEVERAGING INFORMATION TECHNOLOGY FOR COLLABORATION IN CLINICAL TRIALS

18.1 INTRODUCTION

18.2 WHAT IS A CLINICAL TRIAL?

18.3 KEY CHALLENGES OF CLINICAL TRIALS

18.4 TRANSLATIONAL RESEARCH

18.5 SOCIAL COMPUTING

18.6 VIRTUAL WORKPLACE

18.7 SECURITY AND PRIVACY

18.8 CLINICAL E-MAIL SYSTEM

18.9 GREEN HEALTH CARE

PART III: TOOLS FOR COLLABORATIONS

19 EVOLUTION OF ELECTRONIC LABORATORY NOTEBOOKS

19.1 INTRODUCTION

19.2 EARLY ELNS

19.3 CENTERPIECE OF SCIENTIST’S DESKTOP

19.4 A CORPORATE RESOURCE

19.5 COLLABORATION

19.6 PISTOIA ALLIANCE

19.7 QUALITY BY DESIGN

19.8 ACADEMIC PROJECTS

19.9 OPEN-NOTEBOOK SCIENCE

19.10 SMART TEA

19.11 THE OTHER ELN

19.12 STRUCTURED AND UNSTRUCTURED DATA

19.13 ELECTRONIC LABORATORY ENVIRONMENT

19.14 DARK LABORATORY

19.15 FUTURE OF ELN

19.16 ACCELRYS’ EXPERIENCES

20 COLLABORATIVE TOOLS TO ACCELERATE NEGLECTED DISEASE RESEARCH: OPEN-SOURCE DRUG DISCOVERY MODEL

20.1 INTRODUCTION

20.2 SEMANTIC WEB-BASED PORTAL TO LINK MIND AND MACHINES

20.3 DESCRIPTION OF THE PORTAL: COLLABORATIVE WORKSPACES

20.4 SOCIAL NETWORKING FOR RESEARCH

20.5 MOVING FORWARD: FUTURE OF VIRTUAL COLLABORATIVE RESEARCH

21 PIONEERING USE OF THE CLOUD FOR DEVELOPMENT OF COLLABORATIVE DRUG DISCOVERY (CDD) DATABASE

21.1 INTRODUCTION

21.2 BRIEF HISTORY OF THE CLOUD

21.3 CDD DATABASE TECHNICAL DETAILS

21.4 IMPACT ON NEGLECTED DISEASES

21.5 PHARMACEUTICAL COMPANIES CHANGING THEIR BUSINESS MODEL TO INCREASE COLLABORATION AND CROWDSOURCING

21.6 FUTURE DIRECTIONS OF CDD DATABASE

21.7 DISCUSSION

ACKNOWLEDGMENTS

22 CHEMSPIDER: A PLATFORM FOR CROWDSOURCED COLLABORATION TO CURATE DATA DERIVED FROM PUBLIC COMPOUND DATABASES

22.1 INTRODUCTION

22.2 PUBLIC COMPOUND DATABASES

22.3 FUTURE OF ONLINE CHEMISTRY RESOURCES

22.4 CONCLUSION

ACKNOWLEDGMENTS

23 COLLABORATIVE-BASED BIOINFORMATICS APPLICATIONS

23.1 INTRODUCTION

23.2 CLOUD COMPUTING RESOURCES

23.3 EXAMPLES OF BIOINFORMATICS CLOUD COMPUTING RESOURCES

23.4 SUMMARY

24 COLLABORATIVE CHEMINFORMATICS APPLICATIONS

24.1 INTRODUCTION

24.2 COLLABORATIVE CODE DEVELOPMENT

24.3 COLLABORATIVE KNOWLEDGE BASES

24.4 COLLABORATIVE COMPUTING

24.5 MANAGING COLLABORATIVE PROJECTS

24.6 CONCLUSION

PART IV: THE FUTURE OF COLLABORATIONS

25 COLLABORATION USING OPEN NOTEBOOK SCIENCE IN ACADEMIA

25.1 INTRODUCTION

25.2 OPEN NOTEBOOK SCIENCE

25.3 USEFULCHEM PROJECT

25.4 OPEN NOTEBOOK SCIENCE SOLUBILITY CHALLENGE COLLABORATIONS

25.5 OPEN NOTEBOOK SCIENCE IN UNDERGRADUATE PHYSICS LABORATORY HOSTED ON OPENWETWARE

25.6 LABORATORY BLOGGING: FRAMEWORK FOR SMALL-SCALE COLLABORATION

25.7 CONCLUSION

26 COLLABORATION AND THE SEMANTIC WEB

26.1 INTRODUCTION

26.2 SPRINGBOARD FOR COLLABORATIVE SEMANTIC WEB TECHNOLOGIES

26.3 SEMANTIC WEB APPROACH

26.4 CONCEPT WEB ALLIANCE AND CONCEPTWIKI

26.5 AUTHORSHIP OF SCIENTIFIC ASSERTIONS

26.6 CULTIVATING COMMUNITIES OF PRACTICE

26.7 ADOPTION OF TECHNOLOGIES IN OPEN PHARMACOLOGICAL SPACE

26.8 CONCLUSION

27 COLLABORATIVE VISUAL ANALYTICS ENVIRONMENT FOR IMAGING GENETICS

27.1 MOTIVATION: THE LARGER CHALLENGE

27.2 PREVIOUS WORK RELATED TO COLLABORATIVE TECHNOLOGIES

27.3 A CYBER-COLLABORATORY FOR IMAGING GENETICS

27.4 VISUAL ANALYTICS APPROACH

27.5 CONCLUSIONS

28 CURRENT AND FUTURE CHALLENGES FOR COLLABORATIVE COMPUTATIONAL TECHNOLOGIES FOR THE LIFE SCIENCES

28.1 INTRODUCTION

28.2 COLLABORATIONS IN HEALTH ECONOMICS MODELING

28.3 COLLABORATIVE ADVERSE-EVENT DETECTION AND DRUG SAFETY DATABASES

28.4 ONTOLOGIES AND COLLABORATIONS

28.5 WILL WIKIS AND ONLINE COLLABORATION CHANGE THE WORLD?

28.6 COLLABORATIVE SYSTEMS BIOLOGY

28.7 MOBILE COMPUTING AND ITS IMPACT ON COLLABORATIONS

28.8 CROWDSOURCING TAIL FOR COLLABORATIVE DATABASES

28.9 ROLE OF “OPENNESS”—HOW FAR CAN COLLABORATION GO?

28.10 CONCLUSIONS

ACKNOWLEDGMENTS

Index

Wiley Series on Technologies for the Pharmaceutical Industry

Sean Ekins, Series Editor

Editorial Advisory Board

Dr. Renée J.G. Arnold (ACT LLC, USA)

Dr. David D. Christ (SNC Partners LLC, USA)

Dr. Michael J. Curtis (Rayne Institute, St Thomas’ Hospital, UK)

Dr. James H. Harwood (Delphi BioMedical Consultants, USA)

Dr. Maggie A.Z. Hupcey (PA Consulting, USA)

Dr. Dale Johnson (Emiliem, USA)

Prof. Tsuguchika Kaminuma, (Tokyo Medical and Dental University, Japan)

Dr. Mark Murcko, (Vertex, USA)

Dr. Peter W. Swaan (University of Maryland, USA)

Dr. Ana Szarfman (FDA, USA)

Dr. David Wild (Indiana University, USA)

Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals

Edited by Sean Ekins

Pharmaceutical Applications of Raman Spectroscopy

Edited by Slobodan Šaši

Pathway Analysis for Drug Discovery: Computational Infrastructure and Applications

Edited by Anton Yuryev

Drug Efficacy, Safety, and Biologics Discovery: Enmerging Technologies and Tools

Edited by Sean Ekins and Jinghai J. Xu

The Engines of Hippocrates: From the Dawn of Medicine to Medical and Pharmaceutical Informatics

Barry Robson and O.K. Baek

Pharmaceutical Data Mining: Applications for Drug Discovery

Edited by Konstantin V. Balakin

The Agile Approach to Adaptive Research: Optimizing Efficiency in Clinical Development

Michael J. Rosenberg

Pharmaceutical and Biomedical Project Management in a Changing Global Environment

Scott D. Babler

Copyright © 2011 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

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Library of Congress Cataloging-in-Publication Data:

Collaborative computational technologies for biomedical research / edited by Sean Ekins, Maggie A.Z. Hupcey, and Antony J. Williams.

p. cm.

 Includes index.

 ISBN 978-0-470-63803-3 (cloth)

 1. Drug development. 2. Cooperation. 3. Pharmaceutical industry–Data processing. 4. Cloud computing. I. Ekins, Sean. II. Hupcey, Maggie A. (Maggie Anne Zo?), 1972- III. Williams, Antony J.

 RM301.25.C65 2011

 615'.19–dc22

2010046374

oBook ISBN: 978-1-11802603-8

ePDF ISBN: 978-1-11802601-4

ePub ISBN: 978-1-11802602-1

mobi ISBN: 978-1-11818059-4

For Mum and Dad with thanks for letting me follow a route of my own.

Sean Ekins

For Motts, short but loud.

Maggie A. Z. Hupcey

For my twin sons, Taylor and Tyler—two of the best collaborators I know.

Antony J. Williams

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

FOREWORD

You have in your hands a book on collaboration, more specifically a book on scientific collaboration, and most specifically, a book on collaboration in the science of pharmaceutical development—the discovery of new therapies and medicines—products addressing the, as-yet, unmet medical needs of twenty-first century health. While only a few would take issue with the merits of collaboration, perhaps even most fail to appreciate the implications of collaborative technologies in the present day. The ability to fuse ideas—especially ideas that cross disciplines—is a crucial capability responsible for accelerating innovation and progress. Matt Ridley recently gave a TED talk entitled, “When Ideas Have Sex,” the salient point being that the fusion of ideas, each bringing its own set of memes, is a powerful way of creating new memetic material.

People have collaborated as long as … well … as long as there have been people. Often nothing more than self-interest incites us to collaborate, to fill in portions of a solution important to us, portions we were not capable of creating on our own. Unfortunately, modern-day organizational structures very often serve as impediments to collaboration. Collaborating with those outside the walls of an institution may be more than culturally frowned upon, it may even be illegal under legislation written to hinder corporate espionage, or protect trade or national technological capabilities. (I guess if that were the only problem, it could be readily solved by a new set of policies or regulations.)

But institutional boundaries are not the only barriers that impede collaboration. Even within an institution—which should be legally, strategically, and financially incented for alignment, and for maximizing the opportunities for internal collaboration—barriers still exist. The subunits of the institution: its departments, its divisions, its components produce collaboration “walls” of varying substantiality. Organizational lore and personal relationships add another layer of “not-invented here” (NIH) culture, and allegiances to local agendas, even to the point of disadvantaging the larger institutional unit. In fact, if we wish to pursue the elimination of collaboration barriers we have to realize that many barriers are not institutional at all. Choices to collaborate or not collaborate are sometimes based not just on current affiliations but on past affiliations, degrees obtained, reputations, and even a less than rational bias as to just who our collaboration partners should be.

A bright spot in recent history has been the open-source movement. It was loosely organized. It was NOT the project management assignment of any large corporate firm filled with project managers looking for substantial development programs like this one. While we acknowledge that there was a component of centralization, that is, Linus Torvald’s role in Linux, the majority of work was exercised in a distributed manner, each module remaining somewhat independent of the constraints often imposed by centralized planning functions. Most importantly, the basis upon which individuals contributed was informed solely by the contribution itself, not perceived qualifications or past reputations.

While the open-source movement has been associated primarily with the development of software, the demonstration that it can compete effectively with the traditional modes of corporate technology development raises the possibility that such collaborative forms will soon move well beyond software and into other arenas of complex development. This is more than mere speculation. In the chapters that follow you’ll see early endeavors to accomplish pharmaceutical development in a much more open manner. While these may still fall short of the phenomenon associated with Linux, they more than hint at a future to come. One barrier to this progression was highlighted in Harvard Business Review’s ten best business ideas for 2010; namely, the current lack of a well-accepted and digitized representation of this work. The vast majority of collaborative pharmaceutical development still remains primarily a local and classically social phenomenon.

While change is still impeded for the reasons described above, the corporate model of the fully integrated pharmaceutical company is under threat for very good reasons. In the past decade, it has shown its inability to create and sustain shareholder value. A closer examination of the business model itself reveals a variety of flaws (or features, if you’d prefer): long monetization cycles, large capital investments with high risks, and a complex union of both information and materials management. We might argue that a typical pharmaceutical company tries to operate, under one roof, three distinctive business entities. It is a high-tech manufacturer, producing exquisitely expensive fine chemicals or complex biotechnical products. It is a purveyor of information to the regulatory and medical communities, information with specifications and demands rarely matched in any other sector. And, finally, it is a high risk research venture, which can only show returns when managed as a portfolio of complex assets demanding constant invention and breakthroughs.

Each of these three business entities would ideally be managed with a distinctive set of overarching strategies and yet such an approach is rarely accommodated. This book addresses, for the most part, only the unique challenge associated with managing large, complex, high-risk research endeavors. But of the three business-entity challenges cited here, a novel new approach to this one could transform the economics of the entire business.

Considering the present state the pharmaceutical industry finds itself in, the promise of innovative medicines for children and our children’s children may well depend on finding new collaborative paradigms with attendant business models. The material for this genesis, though nascent, may well be found in these pages.

Alpheus Bingham

April 2011

PREFACE

Biomedical research has become increasingly driven by creating and consuming tremendous volumes of complex data whether biological, genomic, proteomic, metabolomic or molecular in nature. At the same time the pharmaceutical industry is utilizing an extended network of partner organizations of various sorts (CRO’s, not-for-profit organizations, clinicians and academics) in order to discover and develop new drugs. Current areas of interest for delivering new technologies or molecules to the industry are Open Innovation, Collaborative Innovation and of course, Open Source. Due to the mounting costs, collaborative research and development is undoubtedly the future of biomedical research. There is currently little if any guidance for managing information and computational resources across collaborations of different types. This represents a large cost as experiments can be repeated inadvertently and the cost and time-savings that could result from precompetitive data sharing have generally been ignored. Improving drug discovery or development technology alone is not the solution and we need intelligent information systems and an understanding of how to use them effectively to create and manage knowledge across these collaborations. This book thoroughly details a real set of problems from the human collaborative and data and informatics aspects and is therefore very relevant to the day-to-day activities of running a laboratory or a collaborative research and development project. The processes, approaches and recommendations provided in this book could be applied to help organizations immediately make critical decisions about managing drug discovery and development partnerships. The chapters provide case histories of biomedical collaborations while the technology specific chapters have effectively balanced technological depth and accessibility for the non-specialist reader. The structure of the book will follow a “man-methods-machine” format and the book is divided into four sections:

Part I. Getting People to Collaborate

Part II: Methods and Processes for Collaborations

Part III. Tools for Collaborations

Part IV. The Future of Collaborations

This book may offer the reader a “getting started guide” or instruction on “how to collaborate” for new laboratories, new companies, and new partnerships, as well as a user manual for how to troubleshoot existing collaborations. This book should therefore be of interest to most researchers involved in developing IT systems in the pharmaceutical industry. It should also be particularly pertinent to those leading and participating in collaborative IT consortia for Drug Discovery and Development which are, at the time of writing, increasing in both scope and number.

The book is possible as a result of the contributions of a wide array of authors from pharmaceutical companies, consulting companies, software companies, government institutes, nonprofits, and academia with chapters written by acknowledged pioneers in the field. We have aimed for a complete volume that can be read by all interested in biomedical research and development and with each chapter edited to ensure consistency across the common theme of collaboration and with appropriate explanatory figures and key references. We are confident this book will become a valuable reference work for those interested in collaborative approaches to biomedical research. Certainly this volume represents a point in time for a fast-moving domain of innovation and effort. We hope to revisit this again in the coming years and report on the eventual successes, impacts and shifts in technology as well as cover areas not included in detail.

ACKNOWLEDGMENTS

We are extremely grateful to Jonathan Rose and colleagues at Wiley for their assistance with this book and in particular Bea Roberto for copy editing. Our anonymous proposal reviewers are gratefully acknowledged for their helpful suggestions which, along with other scientists who provided suggestions for additional authors, helped bring this book to fruition.

We are immensely honored that approximately 50 authors agreed to participate sharing their research and ideas and accepting our editorial changes. Clearly this book would have been impossible without their time, effort and input which they provided despite these difficult economic times. This book would have been impossible without their personal sacrifices and collaborations.

We sincerely thank Alph Bingham for the magnificent Foreword and Bryn Williams-Jones for the kind words on the back cover, which they willingly provided at very short notice.

Our authors and ourselves have endeavored to reference as many groups as possible in these chapters but accept and apologize to the many others that may have been unfortunately omitted due to lack of space. We hope we can include you in future volumes!

We acknowledge Tagxedo for the cover image and also made good use of GoogleDocs and its collaborative features when preparing and sharing these chapters. We thank the many scientists that suggested contributors including Dr. Larry Smarr.

Our own research owes a great deal to past, present (and doubtless future) collaborators and we acknowledge them for helping to stimulate this book.

In order to better serve our readers, color versions of selected illustrations from this book can be found at the following ftp address:

ftp://ftp.wiley.com/public/sci_tech_med/collaborative_computational

Finally, we dedicate this book to our families that have followed this project and provided us the time and support to do it.

Sean Ekins

Maggie A. Z. Hupcey

Antony J. Williams

Jenkintown, Pennsylvania

Wake Forest, North Carolina

April 2011

CONTRIBUTORS

Santosh Adayikkoth, Ph.D., Infosys Technologies Limited, Electronic City, Bangalore, India

Renée J. G. Arnold, Pharm.D., R.Ph., Arnold Consultancy & Technology LLC, New York, New York; Master of Public Health Program, Department of Preventive Medicine, Mount Sinai School of Medicine, New York, New York; Division of Social and Administrative Sciences, Arnold and Marie Schwartz College of Pharmacy, Long Island University, Brooklyn, New York

O. K. Baek, IBM Canada Ltd., Markham, Ontario, Canada

Anshu Bhardwaj, Ph.D., Institute of Genomics and Integrative Biology (IGIB), CSIR, Delhi, India

Alpheus Bingham, Ph.D., Cascade Consulting, Carmel, Indiana; InnoCentive, Inc., Waltham, Massachusetts; Monitor Talent, Cambridge, Massachusetts

Jean-Claude Bradley, Ph.D., Department of Chemistry, Drexel University, Philadelphia, Pennsylvania

Samir K. Brahmachari, Ph.D., Council of Scientific and Industrial Research (CSIR), Institute of Genomics and Integrative Biology (IGIB), Delhi, India

Vincent Breton, Ph.D., Laboratory of Corpuscular Physics, Clermont University and University Blaise Pascal, Clermont-Ferrand, France

Barry A. Bunin, Ph.D., Collaborative Drug Discovery, Burlingame, California

Christine Chichester, Ph.D., Netherlands Bioinformatics Center, Nijmegen, The Netherlands

Gabriela Cohen-Freue, Ph.D., PROOF Centre of Excellence, Vancouver, British Columbia, Canada

Ramesh V. Durvasula, Ph.D., Bristol-Myers Squibb Company, Princeton, New Jersey

Sean Ekins, Ph.D., D.Sc., Collaborations In Chemistry, Jenkintown, Pennsylva­nia; ACT LLC, New York, New York; Collaborative Drug Discovery, Burlingame, California; Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland; Department of Pharmacology, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Piscataway, New Jersey

Rajarshi Guha, Ph.D., NIH Chemical Genomics Center, Rockville, Maryland

Brian D. Halligan Ph.D., Biotechnology and Bioengineering Center, Medical College of Wisconsin, Milwaukee, Wisconsin

Zhiyu He, Ph.D., Graphics, Visualization and Virtual Reality Laboratory (GRAVITY), University of California, San Diego, California

David Hill, Ph.D., Clermont University, University of Blaise Pascal, LIMOS, Clermont-Ferrand, France

Moses M. Hohman, Ph.D., Collaborative Drug Discovery, Burlingame, California

Zsuzsanna Hollander, M.Sc., PMP, PROOF Centre of Excellence, Vancouver, British Columbia, Canada

Victor J. Hruby, Ph.D., Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona

Jackie Hunter, Ph.D., OI Pharma Partners, Ltd. Red Sky House, Fairclough Hall Farm, Halls Green, Weston, Hertfordshire, United Kingdom

Maggie A. Z. Hupcey, Ph.D., PA Consulting Group, Princeton, New Jersey

Steve Koch, Ph.D., Center for High Technology Materials, Albuquerque, New Mexico

George A. Komatsoulis, Ph.D., Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Rockville, Maryland

Falko Kuester, Ph.D., Graphics, Visualization and Virtual Reality Laboratory (GRAVITY), University of California, San Diego, California

Andrew S. I. D. Lang, Ph.D., Department of Computer Science and Mathe­matics, Oral Roberts University, Tulsa, Oklahoma

Nick Lynch, Ph.D., AstraZeneca UK Limited, Alderley Park, Macclesfield, United Kingdom

Robert Porter Lynch, Ph.D., The University of Alberta Edmonton, Alberta, Canada and The University of British Columbia, Vancouver, British Columbia, Canada

Lydia Maigne, Ph.D., Laboratory of Corpuscular Physics, Clermont University and University Blaise Pascal, Clermont-Ferrand, France

Shawnmarie Mayrand-Chung, Ph.D., J.D., National Institutes of Health, Public-Private Partnerships Program—Office of Science Policy Analysis, Office of the Director, Bethesda, Maryland

Garrett J. McGowan, Ph.D., Chemistry Department, Alfred University, Alfred, New York

Matthew K. McGowan, Ph.D., Foster College of Business Administration, Peoria, Illinois

Richard J. McGowan, Ph.D., Philosophy and Religion Department, Butler University, Indianapolis, Indiana

Barend Mons, Ph.D., Netherlands Bioinformatics Center, Nijmegen, The Netherlands

Mark A. Musen, Ph.D., Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California

Cameron Neylon, Ph.D., STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire, United Kingdom

Christina K. Pikas, Doctoral Candidate, College of Information Studies, University of Maryland, College Park, Maryland

Kevin Ponto, Ph.D., Graphics, Visualization and Virtual Reality Laboratory (GRAVITY), University of California, San Diego, California

Brian Pratt, Insilicos LLC, Seattle, Washington

David Sarramia, Ph.D., Laboratory of Corpuscular Physics, Clermont University and University Blaise Pascal, Clermont-Ferrand, France

Vinod Scaria, Ph.D., Institute of Genomics and Integrative Biology (IGIB), CSIR, Delhi, India

Stephan Schürer, Ph.D., Department of Pharmacology, Miller School of Medicine, Center for Computational Science, University of Miami, Miami, Florida

Jeff Shrager, Ph.D., Symbolic Systems Program (consulting), Stanford University, Stanford, California; CollabRx., Inc., Palo Alto, California

Robin W. Spencer, Ph.D., Pfizer Inc. (retired), United States

Ola Spjuth, Ph.D., Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden

Sándor Szalma, Ph.D., Centocor R&D, Inc. and Johnson & Johnson Corporate Office of Science and Technology, San Diego, California ; Rutgers, The State University of New Jersey, New Brunswick, New Jersey

Keith Taylor, Ph.D., Accelrys, Inc., San Ramon, California

Marty Tenenbaum, Ph.D., CollabRx., Inc., Palo Alto, California

Zakir Thomas, Ph.D., Council of Scientific and Industrial Research (CSIR), Rafi Marg, New Delhi, India

Michael Travers, Ph.D., CollabRx., Inc., Palo Alto, California

Tania Tudorache, Ph.D., Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California

Chris L. Waller, Ph.D., Pfizer, Inc., Groton, Connecticut

John Wilbanks, Ph.D., Creative Commons, San Francisco, California

Antony J. Williams, Ph.D. F.R.S.C., Royal Society of Chemistry, Wake Forest, North Carolina

Egon Willighagen, Ph.D., Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden

Edward D. Zanders, Ph.D., BioVillage Ltd., St. John’s Innovation Centre, Cambridge, United Kingdom

PART I: GETTING PEOPLE TO COLLABORATE

1

NEED FOR COLLABORATIVE TECHNOLOGIES IN DRUG DISCOVERY

Chris L. Waller, Ramesh V. Durvasula, and Nick Lynch

1.1 Introduction

1.1.1 Brief History of Pharmaceutical Industry

1.1.2 Brief History of Biotechnology

1.1.3 Brief History of Government-Funded Academic Drug Discovery

1.2 Setting The Stage for Collaborations

1.2.1 Current Business, Technical, and Scientific Landscape

1.2.2 Externalization of Research: Collaboration with Partners

1.3 Overview of Value of Precompetitive Alliances in Other Industries

1.3.1 Overview of Existing Precompetitive Alliances

1.3.2 Pistoia Alliance: Construct for Precompetitive Collaborations

1.3.3 How Does Pistoia Plan to Differentiate Itself?

1.3.4 Overview of Current Pistoia Projects

1.3.4.1 SESL—Semantic Enrichment of Scientific Literature

1.3.4.2 Sequence Services

1.3.4.3 ELN Query Services

1.4 Conclusion

References

1.1 INTRODUCTION

From its accidental beginnings in Alexander Fleming’s laboratory, pharmaceutical drug discovery and development has emerged as a multi-billion-dollar industry that has revolutionized practically all aspects of human (and animal) life as we know it. Over the past 100 years, serendipitous discovery has been replaced by a structured process that in its current state is highly structured, automated, and regulated. It is also expensive and lengthy and suffers from a 99% failure rate. Industry averages suggest that the cost to bring a new drug to the market under this so-called blockbuster paradigm is in the neighborhood of $1.5–2.0 billion and takes nearly 16 years (Fig. 1.1) [1].

Figure 1.1 Pharmaceutical research and development process.

1.1.1 Brief History of Pharmaceutical Industry

The origins of the pharmaceutical industry can be traced back to the 1800s and the dye industry in Switzerland. From the dye industry, specialty chemistry companies emerged with Ciba, Geigy, and Sandoz in Switzerland along with Bayer and Hoechst in Germany evolving into the first pharmaceutical companies. In the early 1900s, the center of pharmaceutical research and development (R&D) migrated to the United States, specifically New Jersey, with companies such as American Home Products, Johnson & Johnson, Warner Lambert, Merck & Co., Pharmacia-Upjohn, Schering-Plough, BASF, Hoechst, Schering AG, Hoffman LaRoche, and Novartis making it the location of choice for their U.S. operations. The late 1900s saw the emergence of North Carolina as a pharmaceutical industry hot spot with Glaxo-Wellcome making its U.S. headquarters there. Also in the late 1900s, the biotechnology industry emerged with companies congregated in the Boston/Cambridge area; the San Francisco Bay Area, San Diego, California; Princeton, New Jersey; Washington, D.C., metro area; as well as Philadelphia. In recent years the economic pressures that forced the pharmaceutical industry to think differently about the sourcing of many operational commodity services has driven a trend toward the emergence of both large pharmaceutical and biotechnology footprints in emerging markets such as Brazil, Russia, India, and China (the traditional BRIC countries) as well as Indonesia [2].