Open Source : The Future of Drugs

April 11, 2011

The Pharmaceutical industry faces a difficult challenge:

  • It takes 10 to 15 years to develop a new drug
  • It cost between $800M and $1,000M to do so

Very few compounds make it through the research pipeline
to become products that can have a clinical impact.

The time has come for bold ideas and disruptive technologies.

In this case,
to embrace the practices that Open Source
communities have been used for many years.

The Science Translational Medicine Journal published this week
a commentary about the recent: 

Toronto Summit

Where 43 research, pharma, funder and policy thought-leaders
gathered on 16 February 2011 for a summit on pre-competitive
disease biology and innovative strategies for drug discovery. 

The summit started with the concerning statistics that
90% of drug candidates are rejected before they get
through Phase II Clinical Trials.

The goal was to explore the need and feasibility to create a
Public-Private Partnership (PPP) that enables pre-competitive
drug development spanning target identification to clinical
proof of concept

Participants then converged to exploring the bold idea of
joining forces between pharmaceutical companies, research
universities and government agencies to work together on
identifying new drug candidates. 
Interestingly, everyone concurred that the initiative
must be structured such that:

  • all resulting data are made publicly available 
  • with no intellectual property (IP) 

generated through the stage of proof of clinical mechanism.

such an open-access model would unleash

  • Truly translational, 
  • Mechanism-based research 

and would foster rapid clinical validation of pioneer targets in a manner that

  • Maximizes patient safety and 
  • Rapidly informs the drug-development industry about promising targets

There was an emphasis on the fact that one of the
mechanism for ameliorating this situation is to

In other words, to publish “when a drug doesn’t work”.
In this way, others will be able to avoid investing in
redundant efforts.

The commentary in Science Translational Medicine:

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