English(EN) Learning pharmacokinetic dynamics with structured neural ODEs for precision dosing

新型神经网络模型有望改善药物精准给药

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员开发了两种新颖的神经网络模型，旨在预测患者的药物暴露量，超越了传统的药代动力学方程。第一个模型侧重于预测单次剂量调整后的暴露量，第二个模型能够预测改变给药方案后的结果。在对接受他克莫司治疗的肾移植受者的模拟和真实世界数据中，这些神经网络模型在预测药物暴露量方面显示出比现有方法更高的准确性，为实现更精准的个体化给药指明了方向。 AI

影响这些模型能够实现更准确、更个性化的药物治疗方案，改善患者的治疗效果。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Benjamin Maurel, Agathe Guilloux, Sarah Zohar, Moreno Ursino, Jean-Baptiste Woillard · 2026-11-14 05:00

利用结构化神经ODE学习药代动力学，实现精准给药

arXiv:2602.03215v2 Announce Type: replace-cross 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 …

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报道来源 [1]

利用结构化神经ODE学习药代动力学，实现精准给药

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