The Personalized/Precision Medicine Blog
Researchers at the University of California, San Diego have discovered proof of concept in determining whether a drug will produce adverse side effects based on assessing blood samples. This predictive model predicts how variations in an individual’s genetic backgrounds will affect how the drug is metabolized, and therefore if a side effect is probable.
This UCSD study focused specifically on red blood cells, as they are the simplest human cells. The study was conducted amongst 24 individuals to determine why some individuals experience side effects from ribavirin, a drug used to treat hepatitis C, and others did not. It was discovered that a side effect of ribavirin is that it can cause anemia, or a decrease in red blood cell levels, which occurred in around 8 to 10 percent of patients.
“A goal of our predictive model is to pinpoint specific regions in the red blood cell that might increase susceptibility to this side effect and predict what will potentially happen to any particular patient on this drug over time,” said UC San Diego alumnus Aarash Bordbar, who was part of the research team as a Ph.D. student.
Aarash Bordbar will be presenting this research at the 8th Annual Personalized & Precision Medicine Conference, taking place on October 12-13, 2016 in San Francisco, CA. For more information about conference, visit: http://personalizedmedicinepartnerships.com/.
By predicting how variations in patients’ genes impact how they metabolize the drug, pharmaceutical companies could soon have the ability to conduct predictive screenings before beginning clinical trials. Therefore, this model has the potential to revolutionize what is known about the variance in metabolizing drugs.
In the future, the UCSD research team strives to develop a predictive model for platelet cells, which are vastly more complex than red blood cells, as well as a predictive liver cell model. As this organ is where the majority of drugs are metabolized, and where many drug side effects are manifested, this finding would be revolutionary to pharmaceutical companies in clinical trials. To learn more about this study, visit: http://bit.ly/20oo7rp.