Biomedical Engineering Reference
In-Depth Information
21.1
INTRODUCTION
Biomedical scientists in academia and industry are rarely collaborative and
open with their data until publication or patenting, resulting in considerable
redundancy, wasted expenditures, and unnecessary delays. While collaboration
may be diffi cult, the pathway can be negotiated [1] and some have even
devised simple rules to make them successful [2]. While chemists and biologists
may not always see eye to eye, they bring different perspectives and their col-
laboration is essential if progress is to be made in biomedical research [3]. It
takes a tight collaboration between biologists and chemists in order to effec-
tively translate molecules into potential drug candidates. Until recently there
has been limited discussion of how such collaborations could be initiated [4],
negotiated [1], or successfully enabled [2]. We are also seeing a number of
initiatives such as precompetitive collaboration [5 - 8] , competitive collabora-
tion [9], crowdsourcing [10], and open innovation [11-13], which strongly sug-
gests collaborative drug discovery will be the future paradigm of biomedical
research [14-16]. There is also a growing list of publicly accessible databases
and Internet-based collaborative tools for chemistry [6, 9, 16-19] that make
data more accessible and may increase scientifi c research effi ciency and col-
laboration. These databases can be used for computational modeling such as
quantitative structure-activity relationships [17] and relatively rapid lead
identifi cation [18] .
It is clear to us and others that such collaborations will be facilitated by
computational tools and databases such that data could be shared and stored
securely and when desired published. Currently available computational data-
base tools for drug discovery and chemistry in particular are not collaborative
and are of limited application for drug development [19]. Recent studies have
suggested that there are productivity benefi ts of collaboration [20, 21] and the
formation of collaboration networks [22]. We have previously described novel
Web-based tools that combine chemistry informatics, biology, and social net-
works for drug discovery [19]. Building networks of researchers is important
as the impact of these tools would be expected to increase in an exponential
manner as a function of the number of interconnected users, for example, like
telephones and the Internet. Several examples of network-based technologies
exist for business and social environments such as LinkedIn and Facebook.
Only recently have these types of technologies begun to impact drug discovery
as it becomes more fragmented, with large drug companies relying more on
outsourcing and collaborations. Open-access chemistry databases and Internet-
based collaborative tools such as LabMeeting, myExperiment, DIYbio, Open
wetware, Open Notebook Science, Laboratree, and Science Commons are now
available for the science community, but they have limited or no capability to
mine the data based on chemical structure and they do not have collaboration
features or enable data sharing (open-source public data exchange). In addi-
tion, these commercially available tools do not foster community-based models
for drug discovery and are relatively costly to maintain and support [9, 10, 19,
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