Personalized Drug Therapy Could Prevent Rejection for Transplant Patients

Rapid computing has advanced gene-based and other medicines that could improve outcomes for transplant patients. Here's how personalized drugs might help you.

Organ transplants feel miraculous. Many seriously ill patients can return to virtually normal health after their surgery. But they live with the knowledge that fewer than half of all transplant patients are alive a decade later.

The usual reason is that their immune systems attack and reject the new organs. But better post-transplant care is on its way.

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Preventing organ rejection involves a complex combination of immunosuppressant drugs in varying, and often changing, doses. Patients are on a merry-go-round of medicines and procedures to make sure their bodies do not reject the new organ.

Physicians use dosing guidelines drawn from experience with many patients, but they also make educated guesses to account for variability in patient response and drug-drug interactions. Too little medication may fail to work. Too much trigger side effects.  

Ideally, doctors would have information that could help tailor treatments to a specific patient, a new field called personalized medicine. That day is approaching, but it hasn’t yet arrived.

Rapid computing has created the possibility of processing a vast amount of data to reveal unknown clinical patterns. Information on specific genes has already yielded results that suggest how dosing can be tailored to a patient, based on race and sex. New genetic biomarkers can monitor the patient’s status while limiting surgeries.

Such information can help doctors minimize toxicity and maximize dosing, leading to more informed decisions that could lower the risk of rejection.

As one example of a personalized approach, a team of researchers at the University of California, Los Angeles (UCLA), found a mathematical approach to remove the guesswork for liver transplant patients.

Personalized treatment and preventing organ rejection

The UCLA researchers used observable clinical data for four liver transplant patients to create graphs using algebra that predicted the effect of different drug doses.

To test their approach, they compared the results when treating four patients according to the old model (guesswork based on clinical experience and dosing regimens derived from other patients) and four using their personalized medicine graph.

The key variable was the level of a common immunosuppressant for transplant patients, tacrolimus, which must stay within a certain narrow range.

The study found that the patients treated using graphs ended up with tacrolimus levels within the ideal range.

A follow-up randomized study divided 62 adults between standard care and the graph model. Patients who received standard care had an average of more than 38 percent of post-transplant days when the tacrolimus level was far from the target, compared to 24 percent in the graph group.

They also needed more care. Half of the patients who received standard care stayed in the hospital for more than 15 days, compared to 10 days for the graph group.  

The future of personalized medicine

Other mathematical models have sprung up, with researchers examining how they could be used not only to prevent organ rejection but also to treat cancer and other chronic diseases.

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