UCSF

Tagged: side effects

Shoichet co-led research develops safer, potentially less addictive painkiller

Research co-led by UCSF School of Pharmacy faculty member Brian Shoichet, PhD, has developed a new opioid drug candidate that blocks pain as effectively as morphine in mice, without triggering dangerous side effects, and also apparently without the addictive properties of current prescription painkillers.

UCSF School of Pharmacy leads in NIH funding for 36th year in a row

For the 36th consecutive year, the UCSF School of Pharmacy has received more funding from the National Institutes of Health (NIH) than any other pharmacy school in the United States.

Posters tracking beta blocker side effects, antibiotic use, blood thinner adherence take top seminar honors

Studies of whether patients are taking a blood thinner as prescribed, whether antibiotic treatment of cancer patients’ fevers matches guidelines, and whether one form of a leading cardiovascular drug increases the risk for a serious side effect took top honors at the Department of Clinical Pharmacy’s 15th annual Spring Research Seminar.

Computer models successfully predict drug side effects

New computer models were able to successfully predict negative side effects for hundreds of currently marketed drugs, report researchers from the UCSF School of Pharmacy, SeaChange Pharmaceuticals, and Novartis Institutes for Biomedical Research in a paper published online this week in the journal Nature.

Shoichet research one of top breakthroughs for 2009

Wired Science has cited a computational model developed in the UCSF School of Pharmacy under the direction of faculty member Brian Shoichet, PhD, and applied and tested by scientists at the University of North Carolina at Chapel Hill School of Medicine, as one of the Top Scientific Breakthroughs for 2009.

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.