ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Renal Pharmacology
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1656197
Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest
Provisionally accepted- 1The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- 2Jiaying University, Meizhou, China
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The immunosuppressant tacrolimus (TAC) plays a crucial role in preventing rejection reactions after organ transplant. Due to a narrow therapeutic window, it is one of the long-term challenges in postoperative care, increasingly requiring a precise management due to individual variability. To alleviate the burden on clinicians and achieve an automatic and precise drug dosing, the AI-assisted personalized dosing of TAC is a promising predictive method. This study presents a clinical-oriented TAC dosing algorithm that integrates genetic algorithm (GA) with deep forest (DF) to predict both initial and follow-up doses for kidney transplant recipients. The optimized candidate variables were first conducted from numerous clinical factors by GA using support vector regression based on radial basis function. Then a smaller number of key clinical variables were confirmed for clinical relevance and ease of use by an exhaustive feature selection method. Validated in a cohort of 288 recipients, the DF model combined with a few clinical variables ultimately achieved an average accuracy of 84.5% and 91.7% in the initial and follow-up dosage prediction. The proposed approach can provide a potential reference to algorithm-based automatic pipeline methods for drug dosing prediction and analysis in clinical practice.
Keywords: Kidney transplant, Tacrolimus (TAC), FK506, Genetic algorithm (GA), Deep forest (DF), Personalized dosing, machine learning
Received: 29 Jun 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Min, Li, Lai and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jianliang Min, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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