Method to predict polypharmacology developed in Shoichet Lab

A computational method developed in the UCSF School of Pharmacy under the direction of faculty member Brian Shoichet, PhD, has the potential to predict new target diseases for existing drugs as well as unexpected side effects of approved drugs.

While the development of a specific drug to affect a specific disease process has long been the gold standard for drug development, some drugs can have several targets, which helps explain side effects and toxicity. Shoichet and UCSF colleagues developed a complex method to analyze these possible relationships. The method was applied and tested by scientists at the University of North Carolina at Chapel Hill School of Medicine. Results of their work were published November 1, 2009 in Nature.

3,665 U.S. Food and Drug Administration-approved and investigational drugs were tested against hundreds of targets. Thousands of chemical similarities were predicted, and 30 were tested experimentally. Overall, 23 new drug–target associations were confirmed, 5 of which were potent. The authors conclude that application of this method of analysis might suggest side-effects and new indications for many drugs.

In addition to Shoichet, who is co-senior author of the paper, UCSF authors are: lead author and postdoctoral scholar in the Shoichet lab Michael Keiser, PhD; Adjunct Associate Professor John Irwin, PhD; postdoctoral scholar Christian Laggner, PhD; Jerome Hert, PhD; Kelan Thomas, PharmD; and Douglas Edwards, PharmD.

University of North Carolina authors are: co-senior author Brian Roth, PhD; Vincent Setola, research associate professor; Atheir Abbas, former graduate student; Sandra J. Hufeisen, senior research assistant; Niels H. Jensen, research associate; research technicians Michael B. Kuijer, Roberto C. Matos, Thuy B. Tran, and Ryan Whaley; and Richard A. Glennon.

Research paper

Predicting new molecular targets for known drugs, Nature, November 1, 2009



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