arXiv cs.AI· Benjamin Maurel, Agathe Guilloux, Sarah Zohar, Moreno Ursino, Jean-Baptiste Woillard·· 3 小时前AI 评分33
结构化神经 ODE 学习药代动力学，实现精准给药剂量预测
Learning pharmacokinetic dynamics with structured neural ODEs for precision dosing
AI 导读
研究者开发两种无需预设药代动力学方程的神经模型，用于预测剂量或给药方案改变后的药物暴露变化。在肾移植受者他克莫司真实数据上，两个模型预测后续访视时暴露量的误差均低于贝叶斯估计，其中第二 种模型在新给药方案模拟中比竞争神经模型更准确。结果表明将药代动力学原理融入神经模型有助于个体化给药。
正文 · 原文
Abstract:Individualized dosing requires predicting how a patient's drug exposure would change under a different dose or schedule. However, a model that accurately predicts exposure at the current dose may not reliably predict what happens when treatment changes. We developed two neural models that learn pharmacokinetic dynamics from data without predefined pharmacokinetic equations. The first predicts exposure after a dose change, while the second also allows changes in dosing schedules. In simulations, the second model predicted exposure under new dosing schedules more accurately than competing neural models. On real data of kidney transplant recipients treated with tacrolimus, both models showed lower prediction errors than Bayesian estimation when predicting exposure at a later visit. These findings suggest that incorporating pharmacokinetic principles into neural models can improve predictions needed for individualized dosing.
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.03215 [stat.ML] |
| (or arXiv:2602.03215v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2602.03215 arXiv-issued DOI via DataCite |
Submission history
From: Benjamin Maurel [view email]
[v1]
Tue, 3 Feb 2026 07:30:48 UTC (6,994 KB)
[v2]
Thu, 12 Nov 2026 10:43:40 UTC (1,414 KB)
来源：arXiv cs.AI · arxiv.org