标题： 多模态人工智能从临床前数据预测药物组合的临床结果 显示英文标题

标题： Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

摘要： 从临床前数据预测临床结果对于确定安全有效的药物组合、减少晚期临床失败和加速精准治疗的发展至关重要。当前的AI模型依赖于结构或靶点相关的特征，但未能纳入准确且具有临床相关性的多模态数据。在此，我们介绍了Madrigal，这是一种多模态AI模型，它从结构、通路、细胞活力和转录组数据中学习，以预测953个临床结果和21,842种化合物的药物组合效果，包括已批准药物和正在开发的新化合物的组合。Madrigal使用了一个注意力瓶颈模块，在训练和推理过程中处理缺失数据，这是多模态学习中的一个主要挑战。它在预测不良药物相互作用方面优于单模态方法和最先进的模型，消融实验表明，模态对齐和多模态性都是必要的。它捕捉了转运蛋白介导的相互作用，并与中性粒细胞减少症、贫血、脱发和低血糖症的头对头临床试验差异相一致。在2型糖尿病和MASH中，Madrigal支持多种药物治疗决策，并优先选择resmetirom作为更安全的候选药物。扩展到个性化，Madrigal在纵向电子健康记录队列和独立肿瘤学队列中改善了患者级别的不良事件预测，并预测了原发性急性髓系白血病样本和患者来源的异种移植模型的体外疗效。Madrigal将临床前多模态读数与药物组合的安全风险联系起来，并为更安全的组合设计提供了一个可推广的基础。 显示英文摘要

摘要： Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current AI models rely on structural or target-based features but fail to incorporate the multimodal data necessary for accurate, clinically relevant predictions. Here, we introduce Madrigal, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug-combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. Madrigal uses an attention bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference, a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions, and ablations show both modality alignment and multimodality are necessary. It captures transporter-mediated interactions and aligns with head-to-head clinical trial differences for neutropenia, anemia, alopecia, and hypoglycemia. In type 2 diabetes and MASH, Madrigal supports polypharmacy decisions and prioritizes resmetirom among safer candidates. Extending to personalization, Madrigal improves patient-level adverse-event prediction in a longitudinal EHR cohort and an independent oncology cohort, and predicts ex vivo efficacy in primary acute myeloid leukemia samples and patient-derived xenograft models. Madrigal links preclinical multimodal readouts to safety risks of drug combinations and offers a generalizable foundation for safer combination design.