New Mathematical Model Set to Improve Personalized Medicine

We have heard for years that personalized medicine is the wave of the future—tailored drug doses and combination specific to patient’s diseases and body chemistry. Now, a team of bioengineers and surgeons from UCLA schools of dentistry, engineering, and medicine may have taken medicine an important step closer to that reality.

The UCLA researchers developed a novel technology platform called phenotypic personalized medicine, or PPM, which can accurately identify a person’s optimal drug and dose combinations throughout an entire course of treatment.

Interestingly, unlike other approaches to personalized medicine currently being tested, PPM doesn’t require complicated, time-consuming analysis of a patient’s genetic information or the disease’s cellular makeup. Instead, it produces a personalized drug regimen based on information about a person’s phenotype—biological traits that could include anything from blood pressure to the size of a tumor or the health of a particular organ.

“This study demonstrated the ability to use a patient’s phenotype to personalize their treatment in an actionable manner without the need for genome profiling,” explained senior study author Dean Ho, Ph.D., professor and co-director of the Weintraub Center for Reconstructive at the UCLA School of Dentistry. “We also have shown that PPM can be extended to optimize combination therapy for a wide spectrum of diseases.”

The findings from this study were published recently in Science Translational Medicine through an article entitled “Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform.”

The new program plots graphs with drug dose along the horizontal axis and the patient’s response to treatment on the vertical axis. (Data for the patient’s response is dictated by whatever health goal the doctor is trying to achieve: shrinking the size of a tumor, having a certain level of medication in the blood or reducing toxicity level, for example.)

Remarkably, each person produces a graph in the parabola-shaped graph—picture a “U” either right-side up or upside down—and that parabola dictates how doctors should proceed with the treatment. Each patient’s unique curve provides physicians with a visual guide to determine the exact doses of medicine to prescribe as the treatment continues—a key to achieving truly personalized medicine.

To test the new platform the researchers evaluated eight people who had recently received liver transplants. Since most liver transplant patients take an immunosuppressive drug called tacrolimus to prevent their bodies from rejecting the organ, doctors prescribe dosages of the drug based on how other patients have responded in the past, and they adjust those amounts if and when complications arise. For this analysis, the patients received care followed the traditional approach and four received treatment that was guided by PPM.

“Properly managing patients’ immunosuppression can have profound long-term impacts on the survival of the organ and the patient,” noted lead study author Ali Zarrinpar, M.D., Ph.D., assistant professor of surgery in the UCLA division of liver and pancreas transplantation. “This study shows that we can pinpoint drug doses that can substantially improve patient outcomes. The ability to confidently and systematically guide the treatment of each patient is a critical advance in minimizing the chance that transplant recipients will reject their new organs while also avoiding drug side effects.”

To determine whether the PPM approach was successful, researchers wanted to see the amount of tacrolimus in each patient’s body stay within the “ideal” range, as dictated by each patient’s unique parabola-shaped graph. They found that those who were treated following PPM spent as much as 50% less time outside of that range than the patients whose treatment followed the traditional approach.

“Optimizing the drug ratios during combination therapy for a population or a specific patient has, until now, been virtually impossible,” stated co-author Chih-Ming Ho, Ph.D., professor of engineering at UCLA. “Our ability to calibrate how individual patients respond to treatment and to use that information to robustly guide their regimen based on the parabola based approach has made personalized medicine a reality.”

The researchers are currently using the PPM in several other clinical trials including some for treating cancer and infectious diseases.