[论文解读] A machine learning approach to drug repositioning based on drug expression profiles: Applications to schizophrenia and depression/anxiety disorders

Kai Zhao, Hon‐Cheong So|arXiv (Cornell University)|Jun 9, 2017

Computational Drug Discovery Methods参考文献 93被引用 7

一句话总结

本研究提出了一种机器学习框架，利用药物诱导的基因表达谱预测新型药物适应症，展示了其在识别精神分裂症及抑郁/焦虑障碍药物再利用候选药物方面的实用性。该方法利用各种监督学习模型（尤其是SVM）对转录组数据进行分析，以优先筛选出预测疗效较高的药物，揭示了具有生物学合理性的候选药物，并得到临床前和临床证据的支持。

ABSTRACT

Development of new medications is a very lengthy and costly process. Finding novel indications for existing drugs, or drug repositioning, can serve as a useful strategy to shorten the development cycle. In this study, we present an approach to drug discovery or repositioning by predicting indication for a particular disease based on expression profiles of drugs, with a focus on applications in psychiatry. Drugs that are not originally indicated for the disease but with high predicted probabilities serve as good candidates for repurposing. This framework is widely applicable to any chemicals or drugs with expression profiles measured, even if the drug targets are unknown. It is also highly flexible as virtually any supervised learning algorithms can be used. We applied this approach to identify repositioning opportunities for schizophrenia as well as depression and anxiety disorders. We applied various state-of-the-art machine learning (ML) approaches for prediction, including deep neural networks, support vector machines (SVM), elastic net, random forest and gradient boosted machines. The performance of the five approaches did not differ substantially, with SVM slightly outperformed the others. However, methods with lower predictive accuracy can still reveal literature-supported candidates that are of different mechanisms of actions. As a further validation, we showed that the repositioning hits are enriched for psychiatric medications considered in clinical trials. Notably, many top repositioning hits are supported by previous preclinical or clinical studies. Finally, we propose that ML approaches may provide a new avenue to explore drug mechanisms via examining the variable importance of gene features.

研究动机与目标

• 通过利用基因表达谱识别现有药物的新适应症，加速药物发现。
• 通过系统性药物再定位，解决传统药物研发成本高、周期长的问题。
• 开发一种灵活、靶点无关的框架，适用于任何具有可测量转录组反应的化合物。
• 评估多种机器学习模型在分类药物-疾病关联中的预测性能。
• 通过评估再定位命中结果在已知精神疾病药物临床试验及先前生物学研究中的富集程度，验证研究发现。

提出的方法

• 该方法使用药物诱导的转录组谱（基因表达变化）作为监督学习的输入特征。
• 应用五种最先进的模型：深度神经网络、支持向量机（SVM）、弹性网络、随机森林和梯度提升机。
• 模型经过训练，以基于其表达谱预测某药物是否对特定疾病（如精神分裂症或抑郁/焦虑障碍）具有适应症。
• 该框架为靶点无关，仅依赖于基因表达模式，无需了解药物靶点或作用机制。
• 进行变量重要性分析，以探索预测药物-疾病关联潜在的生物学机制。
• 使用标准指标评估性能，并通过交叉验证确保结果稳健。

实验结果

研究问题

• RQ1 药物表达谱能否以高准确度预测精神疾病的新适应症？
• RQ2 在使用转录组数据预测药物-疾病关联时，哪种机器学习模型表现最佳？
• RQ3 排名靠前的再利用药物候选是否具有生物学合理性，并得到现有临床前或临床证据的支持？
• RQ4 预测的再定位命中药物集合是否富集于已针对精神疾病开展临床试验的药物？
• RQ5 通过分析预测模型中单个基因的重要性，能否获得关于药物作用机制的深入见解？

主要发现

• 支持向量机（SVM）的预测性能略优于其他模型，尽管各方法之间的差异并不显著。
• 排名靠前的再利用命中药物富集于目前处于临床试验的精神病药物，支持预测结果的生物学相关性。
• 许多排名靠前的候选药物此前已在临床前或临床研究中报道，表明具有强大的外部验证。
• 即使准确率较低的模型也识别出了具有不同作用机制的合理药物候选，凸显了该方法的稳健性。
• 变量重要性分析揭示了可能构成预测药物-疾病关联基础的特定基因特征，为潜在作用机制提供了新见解。