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.

The authors have declared no competing interest.

This study did not receive any funding.

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The study used ONLY openly available human data that can be located at the following FigShare link: https://doi.org/10.1371/journal.pone.0183826.s001

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The dataset analyzed in this study is publicly available at [15]. Additional data and code used for modeling are available from the corresponding author upon reasonable request.