无需调参即可生成药代动力学虚拟人群并预测患者轨迹。
Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
- 基于稀疏不规则数据学习函数向量场，实现零样本群体合成。
- 在真实数据集上达到当前最优预测精度，且能输出校准的不确定性。
- 适合药物研发人员快速构建虚拟临床试验数据集。
我们提出先验适配函数流（Prior-Fitted Functional Flow），一种用于药代动力学的生成基础模型，可在无需人工调参的情况下实现零样本群体合成与个体轨迹预测。该模型显式地基于整个研究群体的稀疏、不规则数据学习函数向量场，从而生成连贯的虚拟队列，并对部分观测的患者轨迹进行预测，同时提供校准的不确定性估计。我们构建了一个新的开放获取文献语料库以建立先验知识，并在多个真实世界数据集上验证了其优于现有方法的预测性能。
原文摘要 · Abstract (English)
We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent virtual cohorts as well as forecasting of partially observed patient trajectories with calibrated uncertainty. We construct a new open-access literature corpus to inform our priors, and demonstrate state-of-the-art predictive accuracy on extensive real-world datasets.
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