Biomedical Engineering Reference
In-Depth Information
Applying collaborative approaches to drug discovery and development
2-3 years
0.5-1 years
1-3 years
1-2 years
5-6 years
1-2 years
Registration
&
Post-
approval
Target
Discovery
Lead
Optimization
Lead ID
ADMET
Development
Collaborative
sharing health
economics
models
detection of
idiosyncratic
toxicity
Share biology
and chemistry
data between
groups using
collaborative
technologies
Share
screening
data to identify
promiscuous
inhibitors and
false positives
Develop
collaborative
tools for
adverse-event
detection
Develop open
databases of
ADME/Tox data
Leverage
pharma data
Precompetitive
efforts
in target ID
across industry
Lab
assistants,
calculators,
database
access
Database
searching
Property
predictions
Database
searching
Property
predictions
Clinical data
capture tools
Real-time
analysis
Adverse-event
tracking, cost
effectiveness
calculators
Chemistry
sketchers
Idea generation
Mobile computing tools
Figure 28.1
Applying collaborative approaches and mobile computing to drug dis-
covery and development. The schematic shows the linear process of drug discovery and
development alongside areas where we think collaboration could be useful. We have
also indicated where mobile computing tools could be implemented.
computing is certainly the “wave of the future” but in reality is arriving so fast
that by the time this volume is printed it is likely to be established and in place
in many organizations that will be embracing the newfound capabilities and
advantages of tablets, slates, and Hypertext Markup Language (HTML 5).
A major limitation of drug discovery for those outside major pharmaceuti-
cal companies is the availability of biological information related to chemical
structures. This is already starting to change via precompetitive collaborations
between biomedical organizations (both industrial and academic) which may
cover areas such as cheminformatics, toxicology, preclinical toxicology, and
beyond. We have previously argued that absorption, distribution, metabolism,
excretion, and toxicity (ADME/Tox) data are also precompetitive data and
should be made freely available on the Web for all scientists [5]. Others such
as the nonprofi t and associated community SAGE Bionetworks (http://
www.sagebase.org/) aim to make the whole of the biology of drug discovery
a precompetitive space and they have initially focused on the systems biology
of cancer. As public hosts of data continue to expand their content, for
example, PubChem, ChEMBL, and ChemSpider, and as data-mining tools
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