Novel Markers for Monitoring Kidney Transplants

Summary

This project focuses on developing personalized tools to monitor kidney transplant patients, aiming to predict and reduce the risk of transplant rejection. By using computer algorithms and identifying specific immune markers, the goal is to help tailor immunosuppressant therapy for each patient, improving transplant outcomes while minimizing side effects like infections or cancer.

The Challenge

Although physicians have been successfully performing kidney transplants for nearly seven decades, almost all kidney recipients will eventually experience a degree of rejection and subsequent failure of their transplant. A fundamental challenge in this field is achieving balance between suppressing the immune system to avoid rejection while mitigating the risk of infection and cancer that are adverse effects of common immunosuppressant medications.

The Approach

We are attempting to develop methods by which markers personalized to a kidney transplant recipient and donor can be used to monitor a recipient's risk of rejection and guide immunosuppressant therapy. Initially, we are validating computational algorithms that predict cellular immunity between donors and recipients by measuring cellular (T cell) reactivity in the lab. Then, we will identify blood or urine markers specific to a kidney donor that can be monitored in a recipient over time. Our ultimate goal is to create a tool that can personalize treatment, ensuring the right balance of medication to prevent rejection while minimizing side effects.

The Impact

We have demonstrated that computer-based methods can predict immune responses between kidney transplant recipients and donors, which affect the success of the transplant. The main goal of this project is to develop a tool that can act as a personalized marker to help adjust medication levels, ensuring the right amount of immunosuppression to prevent rejection while minimizing side effects for organ transplant patients. This tool could help improve the recipient's medication plan and increase the lifespan of both the transplant and the patient.

Key Benefits

		Biomedical Technology- Demonstrated We have shown that certain computer-based methods (like "molecular mismatch" algorithms) can predict kidney transplant outcomes, such as rejection, and we are currently testing the underling mechanisms of these methods in the lab.
		Diagnostic Procedures- Potential These results could lead to a future clinical trials using personalized biomarkers.
		Guidelines- Potential These results can lead to more accurate practices of predicting kidney transplant outcomes and can potentially inform future clinical practice guidelines.
		Life Expectancy & Quality of Life- Potential A personalized biomarker would enable precise dosing of immunosuppressant medications, helping to prevent rejection while minimizing the risks of cancer or infection.
		Healthcare Delivery- Potential Higher or lower predicted risks of adverse outcomes can help determine if kidney transplant recipients need additional tests or medication adjustments.
		Policies- Potential By showing that 'high resolution' HLA typing can impact accuracy of kidney transplant outcome prediction, this may inform laboratory policies on a local or national level.