arXiv cs.AI· Benjamin Maurel, Agathe Guilloux, Sarah Zohar, Moreno Ursino, Jean-Baptiste Woillard·· 1 天前AI 评分31
用结构化神经 ODE 学习药代动力学动态，实现精准给药
Learning pharmacokinetic dynamics with structured neural ODEs for precision dosing
AI 导读
研究者开发了两种无需预设药代动力学方程、直接从数据学习药代动力学动态的神经模型：第一种可预测剂量调整后的药物暴露，第二种还支持给药方案变化，且在模拟中对新给药方案的暴露预测比同类神经模型更准确。在肾移植受者他克莫司真实数据上，两模型预测后续随访暴露的误差均低于贝叶斯估计。
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