Precision medicine has a bright future for helping patients with diabetes and diabetic kidney disease, but challenges exist in expanding the necessary data pool to bring diagnostic and therapeutic benefits to patients.

Alice Y.Y. Cheng, MD, FRCPC, Associate Professor at the University of Toronto, discussed the challenges of tailoring treatment and classifying patients into subpopulations by susceptibility or response to treatment during Sunday’s Joint ADA/American Society of Nephrology Symposium.

“Are we actually at the point of perfect precision medicine? Of course not. Oncology is far further ahead than we are, but I think certainly in the diabetes space, there’s a big push for that. In the diabetic kidney disease (DKD) space, there are consortiums that have been developed and various symposia. So there’s a great interest in going there, and I think that’s certainly where the future lies,” Dr. Cheng said.

The biggest challenges include access to data, including healthy human tissue, improved technology to process all needed data, redesign of clinical trials, and getting regulatory bodies and payers on board.

Stakeholder engagement is critical, Dr. Cheng said, because precision treatments may be more expensive upfront.

“You can envision a future where the initial diagnosis piece may be more expensive, but if the precision of the therapy is better, there’s long-term cost savings. So having regulatory bodies understand the process and being on the same page is going to be critical for this to have any success,” she said.

Monogenic diabetes is the best example of precision medicine in the diabetes space, said Dr. Cheng, noting that when monogenic diabetes is diagnosed, physicians can prescribe very specific therapies that will work well.

But extending precision medicine to DKD, for example, will require turning to systems biology, the computational and mathematical modeling of complex biological systems. A number of biomarkers for DKD progression have been identified, and systems biology can integrate the big data in omics as well as clinical phenotypes to gain a mechanistic understanding of the disease.

“The idea behind precision medicine is just having far more data than we ever had before to come up with hypotheses and test them out,” Dr. Cheng said. “But to do that, you actually need people’s blood, people’s urine, or people’s tissue. You need healthy people to volunteer their blood, their urine, and their tissue, but what are the ethics around that and what are privacy issues around that?”

Dr. Cheng highlighted three international consortiums—Biomarker Enterprise to Attack Diabetic Kidney Disease (BEAt-DKD), Diabetes in Nephrology and Other Microvascular Complications (DYNAMO), and Transformative Research in Diabetic Nephropathy (TRIDENT)—that are pooling resources to create the big data necessary for the development of precision diagnostics and treatment options.

“We want to be able to capture all sorts of different diagnostics, not just the diagnostics we can measure, but also their environment, patient preferences, of course, and then ultimately fit the right drug for the right person,” Dr. Cheng said.