Two researchers at UC San Diego modeled the H1N1 virus, looking for ways to fight it (and other pandemics) (MIT Tech Review).
Biochemist Andrew McCammon and undergraduate lab member Daniel Dadon used a sophisticated computer program to simulate all possible conformations–27 in all–of the H1N1 virus's flexible neuraminidase protein.
Using "massive" computing power, they simulated how the virus and, in particular, a suface protein could take shape. Witih a set of 27 possible structures, they then looked at a library of FDA-approved drugs and searched for which of these drugs would bind to the protein in one of its possible permutations.
This is a great story of the value of recombinant innovation. By taking another look at the problem (27 other looks, to be exact) they could then go in search of existing solutions that solved one or more of those problems. And in pharma, existing solutions avoid the enormous costs of developing novel solutions.
In this way, the next big thing in Pharma could be the beginning of the end. At least of of the money machine for big Pharma—the development of wholly new drugs (and their patent-protected profits) to treat diseases.
The current model has a catch-22: patents are necessary to support the enormous risk of developing drugs that fail to have the effects, or have worse side-effects, than promised. But because only patent-protected profits can recoup the enormous R&D expenses, only new (read patentable) drugs are developed. This despite the fact that many old drugs have valuable uses "off-label."
The value of a recombinant innovation process comes from the ability to exploit sunk costs, to leverage the value of ideas that have already been well developed and tested. This computer simulation hopefully points to a new and cost-effective way to see how old drugs can be used in new ways. Old drugs that we already know perform and perform safely.
"If you start with compounds that are FDA-approved, it may be a faster way to find good drug leads," says Rommie Amaro,
who specializes in pharmaceutical and computer sciences at the
University of California at Irvine. "There's a long process to get a
drug reviewed, and the molecules have to be metabolically okay for
people to ingest. So instead of starting with random leads from a
chemical library, if you start with compounds that are FDA-approved,
you could already have the more harmful compounds weeded out."