机器学习驱动的他克莫司给药精准医学：肝移植和肾移植中的当前研究与未来展望
Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.
摘要(中文译文)
他克莫司（TAC）是用于预防器官移植后移植排斥反应的核心免疫抑制剂。然而，其治疗窗窄且个体间药代动力学变异性大，限制了临床应用。TAC浓度低于治疗范围与急性或慢性移植排斥反应密切相关，而浓度高于治疗范围则常导致严重的药物毒性。随着精准医学的发展，将患者多维临床特征与机器学习（ML）相结合，为肝、肾移植术后TAC个体化给药调整提供了重要支持。本综述总结了ML在肝、肾移植患者中用于确定TAC初始剂量和推荐早期血药浓度的应用，分析了现有研究中模型外推和临床转化方面的核心挑战，比较了其与传统模型的特点，并展望了长期免疫抑制监测和TAC动态调整模型的构建方向。本叙述性综述总结了ML在肝、肾移植中用于他克莫司给药的益处。其关键优势在于整合患者的多维临床数据以实现个体化给药。我们重点关注ML在他克莫司初始剂量选择、移植术后早期浓度范围确定以及剂型转换中的应用。我们还系统讨论了当前模型的局限性和未来方向。ML已被广泛应用于开发肝、肾移植术后患者TAC免疫抑制给药的精准模型，为个体化治疗提供了参考。然而，此类模型尚未在临床实践中广泛应用，且普遍缺乏长期动态调控药物浓度的能力。如何推动模型真正应用于临床实践仍有待深入探索，但其在个体化治疗中具有显著的应用前景和研究价值。
Abstract
Tacrolimus (TAC) is a core immunosuppressant used to prevent transplant rejection after organ transplantation. However, its clinical use is limited by a narrow therapeutic window and substantial interindividual pharmacokinetic variability. Subtherapeutic TAC concentrations are closely associated with acute or chronic transplant rejection, whereas supratherapeutic concentrations often cause severe drug toxicity. As precision medicine advances, integrating multidimensional patient clinical characteristics with machine learning (ML) provides important support for individualized TAC dosing adjustments after liver and kidney transplantation. This review summarizes the application of ML in determining the initial dose of TAC and in recommending early blood drug concentrations in liver and kidney transplant patients, analyzes the core challenges in model extrapolation and clinical translation in existing research, compares their characteristics with traditional models, and looks ahead to the construction direction of long-term immunosuppression monitoring and TAC dynamic adjustment models. This narrative review summarizes the benefits of ML for tacrolimus dosing in liver and kidney transplantation. Its key strength is integrating patients' multidimensional clinical data to enable personalized dosing. We focus on ML applications in initial tacrolimus dose selection, early post-transplant concentration range determination, and dosage form conversion. We also systematically discuss current model limitations and future directions. ML has been widely applied to develop precise models for TAC immunosuppressive drug administration in patients after liver and kidney transplantation, providing a reference for individualized treatment. However, such models have not yet been widely applied in clinical practice and generally lack the ability to dynamically regulate drug concentrations over the long term. How to promote the model's true application in clinical practice remains to be explored in depth, but it holds significant prospects for application and research value in individualized treatment.
如何引用
AMA
Hanbei Lv, Zhangpeng Feng, Shusen Zheng, Guoping Jiang. Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.. Frontiers in immunology. 2026; doi:10.3389/fimmu.2026.1822904.
APA
Hanbei Lv, Zhangpeng Feng, Shusen Zheng, Guoping Jiang (2026). Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2026.1822904
GB/T 7714
Hanbei Lv, Zhangpeng Feng, Shusen Zheng, Guoping Jiang. Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.[J]. Frontiers in immunology, 2026 doi:10.3389/fimmu.2026.1822904.
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