Improving risk stratification and precision immunosuppression in kidney transplantation

Abstract

Kidney transplantation is the optimal treatment for end-stage kidney disease, offering superior survival and quality of life compared to dialysis. Despite major progress in short-term graft survival, long-term outcomes remain suboptimal, mainly due to challenges in balancing immunosuppression to prevent rejection while minimizing drug toxicity. Current “one-size-fits-all” immunosuppressive regimens, largely based on early post-transplant studies, fail to account for individual alloimmune risk and cumulative drug exposure. In this thesis, we emphasize precision immunosuppression, using pharmacokinetic monitoring and molecular HLA mismatch analyses to personalize therapy. We showed that area-under-the-curve (AUC)-based monitoring of tacrolimus and mycophenolic acid (MPA) more accurately predicts rejection risk than traditional trough levels. High MPA exposure was also linked to reduced COVID-19 vaccine responses, underscoring the need to tailor doses to individual immune profiles. Granular HLA amino acid mismatch scoring provides a more accurate assessment of alloimmune risk than conventional HLA typing, enabling better treatment stratification. Future strategies should integrate patient characteristics, pharmacokinetics, and molecular compatibility to balance efficacy and toxicity. Large-scale observational studies, dynamic prediction models, and artificial intelligence are expected to advance personalized transplant medicine. Ultimately, individualized immunosuppression could improve long-term graft and patient survival while reducing variability in clinical practice.