Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Department of Pharmacy, Zibo Central Hospital, Zibo, China.
Ann Pharmacother. 2024 May;58(5):469-479. doi: 10.1177/10600280231190943. Epub 2023 Aug 9.
The efficacy and toxicity of tacrolimus are closely related to its trough blood concentrations. Identifying the influencing factors of pharmacokinetics of tacrolimus in the early postoperative period is conducive to the optimization of the individualized tacrolimus administration protocol and to help liver transplant (LT) recipients achieve the target blood concentrations.
This study aimed to develop an artificial neural network (ANN) for predicting the blood concentration of tacrolimus soon after liver transplantation and for identifying determinants of the concentration based on Shapley additive explanation (SHAP).
In this retrospective study, we enrolled 31 recipients who were first treated with liver transplantation from the Department of Liver Transplantation and Hepatic Surgery, the First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) from November 2020 to May 2021. The basic information, biochemical indexes, use of concomitant drugs, and genetic factors of organ donors and recipients were used for the ANN model inputs, and the output was the steady-state trough concentration (C) of tacrolimus after oral administration in LT recipients. The ANN model was established to predict C of tacrolimus, SHAP was applied to the trained model, and the SHAP value of each input was calculated to analyze quantitatively the influencing factors for the output C.
A back-propagation ANN model with 3 hidden layers was established using deep learning. The mean prediction error was 0.27 ± 0.75 ng/mL; mean absolute error, 0.60 ± 0.52 ng/mL; correlation coefficient between predicted and actual C values, 0.9677; and absolute prediction error of all blood concentrations obtained by the ANN model, ≤3.0 ng/mL. The results indicated that the following factors had the most significant effect on C: age, daily drug dose, genotype at polymorphism rs776746 in both recipient and donor, and concomitant use of caspofungin. The predicted C value of tacrolimus in LT recipients increased in a dose-dependent manner when the daily dose exceeded 3 mg, whereas it decreased with age when LT recipients were older than 48 years. The predicted C was higher when recipients and donors had the genotype than when they had the genotype . The predicted C value also increased with the use of caspofungin or Wuzhi capsule.
The established ANN model can be used to predict the C value of tacrolimus in LT recipients with high accuracy and good predictive ability, serving as a reference for personalized treatment in the early stage after liver transplantation.
他克莫司的疗效和毒性与其谷浓度密切相关。确定术后早期他克莫司药代动力学的影响因素，有利于优化个体化他克莫司给药方案，帮助肝移植（LT）受者达到目标血药浓度。
本研究旨在建立一个人工神经网络（ANN），用于预测 LT 受者肝移植后不久的他克莫司血药浓度，并基于 Shapley 加性解释（SHAP）识别浓度的决定因素。
本回顾性研究纳入了 2020 年 11 月至 2021 年 5 月期间，在山东第一医科大学第一附属医院（山东省千佛山医院）肝移植与肝外科首次接受肝移植治疗的 31 例受者。将受者的基本信息、生化指标、合用药物以及供体和受体的遗传因素作为 ANN 模型的输入，输出为 LT 受者口服后他克莫司的稳态谷浓度（C）。建立 ANN 模型预测他克莫司的 C，应用 SHAP 对训练后的模型进行分析，计算每个输入的 SHAP 值，定量分析输出 C 的影响因素。
采用深度学习方法建立了一个具有 3 个隐藏层的反向传播 ANN 模型。平均预测误差为 0.27±0.75ng/ml；平均绝对误差为 0.60±0.52ng/ml；预测值与实测 C 值之间的相关系数为 0.9677；ANN 模型预测的所有血药浓度的绝对预测误差均≤3.0ng/ml。结果表明，对 C 影响最大的因素有：年龄、每日药物剂量、受者和供者 rs776746 多态性的基因型以及合用卡泊芬净。当每日剂量超过 3mg 时，LT 受者的他克莫司 C 呈剂量依赖性增加，而当 LT 受者年龄超过 48 岁时，C 则呈年龄依赖性下降。受者和供者基因型为 时，预测的 C 高于基因型 时。合用卡泊芬净或五酯胶囊时，预测的 C 值也会增加。
本研究建立的 ANN 模型可以准确、良好地预测 LT 受者他克莫司的 C 值，为肝移植后早期的个体化治疗提供参考。
