Reaching out to collaborators and crowdsourcing for pharmaceutical research


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Doing more with less resources used to be a situation common just for academic scientists. This is unfortunately still true for academics but we are seeing others facing many of the same challenges. With the squeeze on budgets and cost cutting resulting from recent worldwide economic challenges, the failure of many drugs to make it through the pipeline to the market, and the increasing costs associated with the drug development process, we are now seeing in the pharmaceutical industry a dramatic shift, perhaps belatedly, to have to accommodate similar challenges of doing more with less

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Reaching out to collaborators and crowdsourcing for pharmaceutical research

  1. 1. Reaching Out To Collaborators: Crowdsourcing for Pharmaceutical Research Sean Ekins1, 2, 3, 4 and Antony J. Williams51 Collaborations in Chemistry, 601 Runnymede Avenue, Jenkintown, PA 19046, U.S.A.2 Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA94403.3 Department of Pharmaceutical Sciences, University of Maryland, MD 21201, U.S.A.4 Department of Pharmacology, University of Medicine & Dentistry of New Jersey(UMDNJ)-Robert Wood Johnson Medical School, 675 Hoes lane, Piscataway, NJ 08854.5 Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, NC-27587.Running Head: CrowdSourcingCorresponding Authors: Sean Ekins, Collaborations in Chemistry, 601 RunnymedeAve, Jenkintown, PA 19046, Email, Tel 215-687-1320. Antony J.Williams, Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, 1
  2. 2. Doing more with less resources used to be a situation common just for academicscientists. This is unfortunately still true for academics but we are seeing others facingmany of the same challenges. With the squeeze on budgets and cost cutting resultingfrom recent worldwide economic challenges, the failure of many drugs to make it throughthe pipeline to the market, and the increasing costs associated with the drug developmentprocess, we are now seeing in the pharmaceutical industry a dramatic shift, perhapsbelatedly, to have to accommodate similar challenges of doing more with less. It couldalso represent the further crumbling of a 150 year old plus paradigm of the largecompany being the predominant source for developing therapeutics for profit. We arealso seeing increased discussion about different models of facilitating pharmaceuticalresearch as well as the suggestion of opportunities to collaborate and use tools thatperhaps would not have been considered in the past (1-4). This shift to “openness” incertain areas, specifically the sharing of pre-competitive data and processes, parallels thesocietal shifts we have seen in so many areas of open source software development, thesharing of data, and the utility of free data resources and repositories such as PubChem( and others (see Table 1). From the extreme ofkeeping entire projects in house there is a shift to decentralized research. One view ofpharmaceutical research is to use loose networks of external researchers from companies,academics or consultants, create a community around a shared interest and gather theirideas. We think this comes closest to the ideal of crowdsourcing where the wisdom of themany and their varied perspectives can benefit community-based efforts whether insoftware, knowledge capture etc. The loose definition of crowdsourcing as “outsourcing atask to a group or community of people in an open call” is a relatively new phenomenon, 2
  3. 3. culture or movement which is best summarized in the book “Wikinomics, How MassCollaboration Changes Everything” (5). Living in our connected world, pharmaceuticalresearchers can communicate in a variety of ways (4) to leverage ideas from around theglobe. These ideas do not have to come from within the walls of a single organisation.Taking this further: why limit access to just ideas? Open tools and data could feed anecosystem. It could also breed a new class of researcher without affiliation who hasallegiance to neither company nor research organization. They test their hypotheses withdata from elsewhere, they do their experiments through a network of collaborations, theymay have no physical lab, while a shared cause may not be essential, confidentialityagreements and software may unite them as a loose cooperative etc. Such approachesmay become more commonplace like the Open Innovation efforts represented bycompanies such as NineSigma ( and Innocentive( The One Billion Minds approach for openinnovation ( has already been mapped into the LifeSciences where a million minds in the community has been called to participate incommunity annotation in Wikiproteins ( A recent example of the power of crowdsourcing is the availability of freelyaccessible online resources to enable and support drug discovery. For instance, onlinedatabases including PubChem, Chemical Entities of Biological Interest, or ChEBIdatabase (, DrugBank (, the HumanMetabolome Database ( and ChemSpider ( good examples (6-8) in addition to commercial databases (9) and collaborativesystems like CDD ( These represent either 3
  4. 4. government or privately funded initiatives with vastly differing resources and scopes.Chemistry (and with it biology) information on the internet has thus become moreaccessible just as we are seeing a massive increase in screening data coming fromindividual laboratories. Sometimes there are synergistic benefits of crowdsourcing forexample, the efforts behind the ChemSpider platform, originally a hobby project housedfrom a basement, and recently acquired by the Royal Society of Chemistry, has beenacknowledged to have greatly enriched the content in the NIH’s PubChem (9). We arealso seeing crowdsourcing applied to get more perspectives on a problem, for examplethe annotation of 64 putative tools and probes from the NIH Roadmap MLSCN effort byscientists from different groups, using multiple filtering methods or molecule qualitymetrics (10). What does the future hold for such databases and other crowdsourcing efforts andwhat are some of the challenges to be aware of? While access to very large datasets as astarting point for biological information and modeling may be of value there should beconcerns regarding the quality of the compounds used for screening e.g. will there be ahigh percentage of false positives? What about the fidelity of the data? Is the same batchof compound used by different groups? Are there experimental differences that result inlarge inter-lab differences in the manner in which they use technologies (11)? Do cellpassage numbers differ? Are the internal standards the same? What is the diet of theanimals used? What is the impact of dissolution variance (12) ? Will the naïve useractually be able to dissect out the false positives or issues with data curation (13) whichmay represent a potential pressure point? What about issues with data protection,anonymity, ethics and tissue handling (14). There are a myriad of other related questions. 4
  5. 5. On the opportunity side there may be some obvious value in the smaller scaleexperiments from individual laboratories being stored in a single location. Perhaps wecan learn from the systems biology or network building software community that haveeither manually or automatically annotated large databases (instances of object Xinteracts with object Y, either directly or indirectly) from individual experiments (15).Rarely is there kinetic data captured in these efforts, and yet if a database of suchinformation could be created this would become accessible. We can therefore see a needdriven by the academic community predominantly for the curation of their singleexperiments in biology with benefits for preventing repetition and possible decrease ofanimal and reagent usage. This drive to curate biology can be encouraged by publishersand funding agencies but once annotated in a desirable format (e.g. there would be a needto capture the experimental protocol and an ontology would be essential (16)) andlocation, the information could be freely available for other efforts whether in datamining, SAR, software development or network building. The goal should be to bringscientists to a point were their data is shared and useful. It is one thing to provide largesupplemental files with publications, but it is another to put the data in a location andformat so that others can potentially learn from it. Perhaps Pharmaceutical Research (andfor that matter other pharmaceutical journals) could play a role in ensuring that datawithin articles in the journal is deposited with freely accessible databases such asPubChem and ChemSpider and beyond. Ultimately we foresee there will be a highlynetworked structure linking the many crowdsourced database efforts to reduceredundancy. While we have already seen a dramatic growth in accessible databases, theinnovation around computational methods for data analysis and mining have really not 5
  6. 6. kept pace (13). There is an opportunity here for the scientific community to address theseneeds and we may see a new wave of informatics company innovation. This could becatalyzed by public or private funding or even crowdsourcing X-prize type awards( Perhaps there also needs to be some degree of focus initially to such an open drugdiscovery model to increase the probability of success, maybe around a neglected diseaselike Malaria or Tuberculosis (TB), or even rapidly emerging diseases (like swine flu), todemonstrate that it is more than a utopian concept. The incentive here could be that thesediseases are rapidly becoming of more concern globally, and could increase demand onhealthcare resources (e.g. the reemergence of TB and ease of transmission). Targetedquestions could be posed to the crowd regarding approaches to surmounting TB drugresistance, latency, target identification or developing novel delivery mechanisms (17,18). As individuals we are continually challenged by demands on our time andresources, both financial and intellectual, and participating in such efforts would surelybe a big motivating factor for many. Some companies allow their employees to pursuepersonal projects as a small percentage of their time to foster creativity. Why not allowthem to give back in the way by contributing to open-source science? Perhapsgovernments can recognize the potential benefits and provide participating companies taxcredits or other incentives. For many the motivation to participate in open drug discoverymay not be financial but purely philanthropic in nature or simply the pursuit of anintellectual challenge. Think of it as the ultimate challenge where scientists collaboratewith thousands of people to help global health. We would welcome suggestions from the 6
  7. 7. various stakeholders with an interest in all aspects of the pharmaceutical R&D valuechain on how open pharmaceutical collaborations could be facilitated. This is certainly anunsettling time in the industry but after the storm has settled we may be in a uniqueposition to do further aspects of R&D differently and more cost-effectively, withimplications for the whole scientific community and global healthcare. Less may indeedbe more.AcknowledgmentsWe are grateful to many discussions with colleagues on this topic as well as the reviewerssuggestions.Conflicts of InterestSE consults for Collaborative Drug Discovery, Inc on a Bill and Melinda GatesFoundation Grant#49852 “Collaborative drug discovery for TB through a novel databaseof SAR data optimized to promote data archiving and sharing”. He is also on the advisoryboard for ChemSpider. AJW is employed by the Royal Society of Chemistry which ownsChemSpider and associated technologies. 7
  8. 8. References1. A. Bingham and S. Ekins. Competitive Collaboration in the Pharmaceutical and Biotechnology Industry. Drug Disc Today. In Press: (2009).2. A.J. Hunter. The Innovative Medicines Initiative: a pre-competitive initiative to enhance the biomedical science base of Europe to expedite the development of new medicines for patients. Drug Discov Today. 13:371-373 (2008).3. M.R. Barnes, L. Harland, S.M. Foord, M.D. Hall, I. Dix, S. Thomas, B.I. Williams-Jones, and C.R. Brouwer. Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery. Nat Rev Drug Discov. 8:701-708 (2009).4. D.S. Bailey and E.D. Zanders. Drug discovery in the era of Facebook--new tools for scientific networking. Drug Discov Today. 13:863-868 (2008).5. D. Tapscott and A.J. Williams. Wikinomics: How Mass Collaboration Changes Everything Portfolio, New York, 2006.6. A.J. Williams. A perspective of publicly accessible/open-access chemistry databases. Drug Discov Today. 13:495-501 (2008).7. A.J. Williams. Internet-based tools for communication and collaboration in chemistry. Drug Discov Today. 13:502-506 (2008).8. S. Louise-May, B. Bunin, and S. Ekins. Towards integrated web-based tools in drug discovery. Touch Briefings - Drug Discovery. in Press: (2009). 8
  9. 9. 9. C. Southan, P. Varkonyi, and S. Muresan. Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds. J Cheminformatics. 1:10 (2009).10. T.I. Oprea, C.G. Bologa, S. Boyer, R.F. Curpan, R.C. Glen, A.L. Hopkins, C.A. Lipinski, G.R. Marshall, Y.C. Martin, L. Ostopovici-Halip, G. Rishton, O. Ursu, R.J. Vaz, C. Waller, H. Waldmann, and L.A. Sklar. A crowdsourcing evaluation of the NIH chemical probes. Nat Chem Biol. 5:441-447 (2009).11. H. Wang, X. He, M. Band, C. Wilson, and L. Liu. A study of inter-lab and inter- platform agreement of DNA microarray data. BMC Genomics. 6:71 (2005).12. G. Deng, A.J. Ashley, W.E. Brown, J.W. Eaton, W.W. Hauck, L.C. Kikwai, M.R. Liddell, R.G. Manning, J.M. Munoz, P. Nithyanandan, M.J. Glasgow, E. Stippler, S.Z. Wahab, and R.L. Williams. The USP Performance Verification Test, Part I: USP Lot P Prednisone Tablets: quality attributes and experimental variables contributing to dissolution variance. Pharm Res. 25:1100-1109 (2008).13. A.J. Williams, V. Tkachenko, C. Lipinski, A. Tropsha, and S. Ekins. Free Online Resources Enabling Crowdsourced Drug Discovery. Drug Discovery World. Submitted: (2009).14. E.B. van Veen. Obstacles to European research projects with data and tissue: solutions and further challenges. Eur J Cancer. 44:1438-1450 (2008).15. S. Ekins. Systems-ADME/Tox: Resources and network approaches. J Pharmacol Toxicol Methods. 53:38-66 (2006). 9
  10. 10. 16. R. Hoehndorf, J. Bacher, M. Backhaus, S.E. Gregorio, Jr., F. Loebe, K. Prufer, A. Uciteli, J. Visagie, H. Herre, and J. Kelso. BOWiki: an ontology-based wiki for annotation of data and integration of knowledge in biology. BMC bioinformatics. 10 Suppl 5:S5 (2009).17. T.S. Balganesh, P.M. Alzari, and S.T. Cole. Rising standards for tuberculosis drug development. Trends Pharmacol Sci. 29:576-581 (2008).18. J.C. Sacchettini, E.J. Rubin, and J.S. Freundlich. Drugs versus bugs: in pursuit of the persistent predator Mycobacterium tuberculosis. Nature reviews. 6:41-52 (2008). 10
  11. 11. Table 1. Examples of crowdsourcing resources for pharmaceutical research. Name Website Function myExperiment Workflows, communities DIYbio Community for do it yourself biologists Protocol online Biology protocols Open wetware Materials, protocols and resources _Page Open Notebook Crowdsourced science challenge – science m/ initially on solubility measurement challenge UsefulChem An example of one scientist’s open project notebook Laboratree Science networking site Science Strategies and tools faster, efficient Commons web-enabled scientific research WikiPathways Curated biological pathways x.php/WikiPathways Open Source Collaboration around genomics and Drug Discovery computational technologies 11