基于药物表达谱的药物重新定位的机器学习方法：在精神分裂症和抑郁/焦虑症中的应用
A machine learning approach to drug repositioning based on drug expression profiles: Applications to schizophrenia and depression/anxiety disorders
摘要：
开发新药是一个非常漫长和昂贵的过程。为现有药物寻找新的适应症，或重新定位药物，可以作为缩短开发周期的有用策略。在这项研究中，我们提出了一种方法，药物发现或重新定位预测的基础上，药物的表达谱的特定疾病的适应症，重点是在精神病学的应用。最初不适用于这种疾病但预测概率高的药物是重新利用的良好候选药物。这个框架广泛适用于任何化学品或药物的表达谱测量，即使药物靶点是未知的。它也非常灵活，因为几乎可以使用任何监督学习算法。我们应用这种方法来确定精神分裂症以及抑郁症和焦虑症的重新定位机会。我们应用了各种最先进的机器学习（ML）方法进行预测，包括深度神经网络、支持向量机（SVM）、弹性网络、随机森林和梯度提升机。五种方法的性能没有实质性差异，SVM略优于其他方法。然而，具有较低预测精度的方法仍然可以揭示具有不同作用机制的文献支持的候选物。作为进一步的验证，我们表明，重新定位的命中是丰富的精神药物在临床试验中考虑。值得注意的是，许多顶级重新定位命中得到了先前临床前或临床研究的支持。最后，我们提出ML方法可以通过检查基因特征的变量重要性来探索药物机制。
英文摘要：
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.
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