发表机构
Imperial College London; Stanford University（帝国理工学院; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对转化医学中动物与人体实验的差距，提出多保真高斯过程框架，可整合多源数据、实现跨物种外推，经模拟与临床数据验证，能高效预测新药并支持动物实验优化。
AI 中文摘要
弥合动物实验与人体实验之间的差距仍是转化医学，尤其是早期药物开发面临的重大挑战。研究进展受限于经济成本、整合异质体外与体内数据的难度，以及在减少动物实验使用与最小化人体参与者风险之间的权衡。我们提出一种基于多保真高斯过程的统计机器学习框架，将动物研究视为人体实验的低保真但具信息性的近似。该框架可同时学习跨物种相似性与非线性暴露-反应关系，实现跨物种的原则性外推并量化不确定性。通过利用多种实验保真度的信息，我们的方法改进了临床相关感兴趣量的估计，并支持体内测试的替代、减少和优化。我们首先在模拟场景中说明该方法，随后在真实临床数据上进行验证。我们模拟了药物诱导的QT间期延长的数据，这是监管批准所需的关键心脏安全性评估。该框架提供了一种概率替代模型，能够在统一统计模型内整合体外药理学、动物实验和人体数据。至关重要的是，它无需额外实验成本即可实现这一点，同时还支持化合物间的迁移学习。因此，仅从体外发现即可生成新药的预测和不确定性量化，进一步提高效率并加速决策。为进行验证，我们使用了测量自主神经阻断下心率变化的临床数据集，该数据集代表了多物种数据集常见的一些挑战。
英文摘要
Bridging the gap between animal and human experiments remains a major challenge in translational medicine, particularly in early drug development. Progress is constrained by financial cost, the difficulty of integrating heterogeneous in vitro and in vivo data, and the desire to reduce the use of animal testing balanced against minimising the risk to human participants. We present a statistical machine learning framework using multi-fidelity Gaussian processes, in which animal studies are considered as lower fidelity but informative approximations to human experiments. This allows cross-species similarities and nonlinear exposure-response relationships to be learned simultaneously, enabling principled extrapolation between species while quantifying uncertainty. By leveraging information from multiple experimental fidelities, our method improves estimation of clinically relevant quantities of interest and supports the replacement, reduction, and refinement of in vivo testing. We first illustrate this approach in a simulated scenario, before validating it on real clinical data. We simulate data for drug-induced QT-interval prolongation, a key cardiac safety assessment required for regulatory approval. This framework provides a probabilistic surrogate capable of integrating in vitro pharmacology, animal experiments and human data within a unified statistical model. Crucially, it achieves this at no additional experimental cost while also enabling transfer learning across compounds. As a result, predictions and uncertainty quantification for new drugs can be generated from in vitro findings alone, providing additional efficiency gains and accelerating decision making. For validation, we use a clinical dataset measuring change in heart rate under autonomic blockade, which represents some of the challenges commonly found in multi-species datasets.
