用分子通路和真实世界数据预测新药疗效，提前评估治疗效果。
Predicting effect of novel treatments using molecular pathways and real-world data
- 基于药物-通路权重评分与患者数据构建可扩展的机器学习模型。
- 在真实世界数据集上验证，对未测试药物疗效预测准确率高。
- 适合药物研发人员快速筛选潜在有效疗法，支持临床前决策。
在药物研发中，提前预测新药对特定疾病的疗效一直存在挑战。本文提出一种灵活、模块化的机器学习方法，利用药物-通路权重影响得分与患者数据（包括患者特征和临床结果）训练模型，分析未测试药物在人类生物分子-蛋白质通路中的加权影响，生成疗效预测值。我们在包含患者治疗与结局的真实世界数据集上进行了验证，采用两种不同的权重影响得分算法。模型具备评估泛化性能的能力，并可识别预测效果最佳的应用场景。本文还讨论了该框架的迭代方向，为未来利用真实世界数据和药物嵌入技术预测未测试药物效果提供了初始方法支持。
原文摘要 · Abstract (English)
In pharmaceutical R&D, predicting the efficacy of a pharmaceutical in treating a particular disease prior to clinical testing or any real-world use has been challenging. In this paper, we propose a flexible and modular machine learning-based approach for predicting the efficacy of an untested pharmaceutical for treating a disease. We train a machine learning model using sets of pharmaceutical-pathway weight impact scores and patient data, which can include patient characteristics and observed clinical outcomes. The resulting model then analyses weighted impact scores of an untested pharmaceutical across human biological molecule-protein pathways to generate a predicted efficacy value. We demonstrate how the method works on a real-world dataset with patient treatments and outcomes, with two different weight impact score algorithms We include methods for evaluating the generalisation performance on unseen treatments, and to characterise conditions under which the approach can be expected to be most predictive. We discuss specific ways in which our approach can be iterated on, making it an initial framework to support future work on predicting the effect of untested drugs, leveraging RWD clinical data and drug embeddings.
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