Accurate estimation of tacrolimus exposure, quantified by the area under the concentration-time curve (AUC), is essential for precision dosing after renal transplantation. Current practice relies on population pharmacokinetic (PopPK) models based on nonlinear mixed-effects (NLME) methods. However, these models depend on rigid, pre-specified assumptions and may struggle to capture complex, patient-specific dynamics, leading to model misspecification. In this study, we introduce a novel data-driven alternative based on Latent Ordinary Differential Equations (Latent ODEs) for tacrolimus AUC prediction. This deep learning approach learns individualized pharmacokinetic dynamics directly from sparse clinical data, enabling greater flexibility in modeling complex biological behavior. The model was evaluated through extensive simulations across multiple scenarios and benchmarked against two standard approaches: NLME-based estimation and the iterative two-stage Bayesian (it2B) method. We further performed a rigorous clinical validation using a development dataset (n = 178) and a completely independent external dataset (n = 75). In simulation, the Latent ODE model demonstrated superior robustness, maintaining high accuracy even when underlying biological mechanisms deviated from standard assumptions. Regarding experiments on clinical datasets, in internal validation, it achieved significantly higher precision with a mean RMSPE of 7.99% compared with 9.24% for it2B (p < 0.001). On the external cohort, it achieved an RMSPE of 10.82%, comparable to the two standard estimators (11.48% and 11.54%). These results establish the Latent ODE as a powerful and reliable tool for AUC prediction. Its flexible architecture provides a promising foundation for next-generation, multi-modal models in personalized medicine.

翻译：准确估算他克莫司暴露量（以浓度-时间曲线下面积量化）对于肾移植后的精准给药至关重要。当前实践依赖于基于非线性混合效应方法的人群药代动力学模型。然而，这些模型依赖于僵化的、预先设定的假设，可能难以捕捉复杂的、患者特异性的动态变化，从而导致模型设定错误。在本研究中，我们引入了一种基于潜在常微分方程的新型数据驱动替代方法，用于他克莫司AUC预测。这种深度学习方法直接从稀疏的临床数据中学习个体化的药代动力学动态，从而在建模复杂生物行为时具有更大的灵活性。该模型通过在多种场景下的广泛模拟进行了评估，并与两种标准方法进行了基准比较：基于NLME的估计和迭代两阶段贝叶斯方法。我们进一步使用一个开发数据集和一个完全独立的外部数据集进行了严格的临床验证。在模拟中，潜在常微分方程模型表现出卓越的鲁棒性，即使当潜在的生物学机制偏离标准假设时，仍能保持高精度。关于临床数据集的实验，在内部验证中，其达到了显著更高的精度，平均RMSPE为7.99%，而it2B为9.24%。在外部队列中，其RMSPE为10.82%，与两种标准估计器相当。这些结果确立了潜在常微分方程作为一种强大且可靠的AUC预测工具。其灵活的架构为个性化医疗中下一代多模态模型奠定了有前景的基础。