The Personalized/Precision Medicine Blog
Syapse has been a constant presence in the precision medicine industry, and has recently furthered its involvement in oncology through a partnership with Henry Ford Health Systems. Henry Ford Health System, one of the largest national integrated health systems, will provide cancer outcomes data to Syapse. In return, Syapse will use this data in its software platform, which will enable faster, global learning gained from real-world experiences. Through this agreement, there will be a launch of an oncology precision medicine program, with the hope of providing precision medicine to patients in the greater Midwest.
Jonathan Hirsch, president and founder of Syapse, will be highlighting his company’s efforts at revolutionizing precision cancer care at the 8th Annual Personalized and Precision Medicine Conference. To learn more information, visit: http://personalizedmedicinepartnerships.com/.
In the next few months, Henry Ford Health System will launch the Syapse Precision Medicine Platform software across its cancer care facilities, including the new $10 million destination cancer center in Detroit. Both companies are optimistic that this partnership will advance innovative cancer clinical care, especially for patients in the Midwest. Jonathan Hirsch stated, “We believe that precision medicine will be a core enabling technology for health systems to transform to value-based care.” For more information on this partnership, visit: http://syapse.com/blog/henry-ford-press-release-june-2016/.
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.