Symbolic Regression for Mycophenolic Acid Dosage Prediction in Kidney Transplant Recipients

Background: Chronic kidney disease (CKD) affects millions worldwide and often progresses to end-stage renal disease (ESRD), for which kidney transplantation remains the standard-of-care. Achieving optimal post-transplant immunosuppression—in particular, precise mycophenolic acid (MPA) dosing—is critical for long-term graft survival. However, in practice, dose personalization remains difficult, especially in underserved and rural populations where access to transplant pharmacology expertise is limited. Methods: We developed an interpretable symbolic regression model trained on retrospective, multi-center kidney transplant datasets. Input variables included patient demographics, anthropometrics, initial MPA loading dose, primary immunosuppressant, and induction therapy regimen. For benchmarking, we also evaluated a suite of state-of-the-art machine learning models, including random forests and gradient-boosted trees. Results: The symbolic regression model identified clinically intuitive patterns: taller patients with lower initial MPA doses often require higher maintenance dosing; overly high starting doses are penalized; and dosing adjustments are strongly influenced by induction regimen parameters. While slightly less accurate than the best-performing black-box models with a mean-absolute error of 320 mg in dosage, the symbolic model maintains clinically acceptable error levels and offers full transparency of decision logic. Conclusions: Our explainable machine learning model delivers transparent, patient-specific MPA dosing recommendations that maintain clinically acceptable accuracy while revealing the underlying decision logic. By bridging the gap between complex pharmacologic modeling and point-of-care accessibility, this approach offers a viable pathway to improve post-transplant immunosuppression in settings where specialist support is scarce.