Bridging the 'Omics Gap
A new personalized test aims to predict side effects
Trying to assess a patient’s potential for side effects is currently more guesswork than science; several different patients can be given the same drug but exhibit a different set of side effects because of variations at the ‘omics level.
A team of scientists led by Neema Jamshidi from the Palsson’s Systems Biology Research Group at the University of California, have developed a model that could predict which set of side effects a patient will experience (1). The method combines genomics, metabolomics and proteomics to predict how variations in different people's genes impact drug metabolism. For example, the model was used to try to understand why some people experience side effects to ribavirin, a drug used to treat hepatitis C (8 to 10 percent of patients receiving ribavirin experience anemia). We spoke with Jamshidi to find out more.
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