PDF(Pubmed)
Abstract：
Alzheimer\'s disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer\'s disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.
摘要：
阿尔茨海默病(AD)是年龄相关性痴呆的主要病因,影响美国500多万人，并导致大量的全球医疗费用。不幸的是,目前的治疗仅是姑息性的，不能治愈AD。迫切需要开发新的抗AD疗法；然而，药物发现是非常耗时的,贵,和高风险的过程。药物重新定位，另一方面,是鉴定用于AD治疗的药物的有吸引力的方法。因此，我们开发了一种新的深度学习方法，称为DOTA（使用阿尔茨海默病最佳运输的药物重新定位方法），以重新利用FDA批准的有效药物来治疗AD。具体来说,DOTA由两个主要的自动编码器组成：（1）用于集成异质药物信息的多模态自动编码器，以及（2）用于识别有效AD药物的Wasserstein变分自动编码器。用我们的方法，我们预测抗精神病药物具有昼夜节律作用，如喹硫平，阿立哌唑,利培酮,suvorexant,布立哌唑,奥氮平,还有曲氮酮,将对AD患者产生有效影响。这些药物靶向参与记忆的重要脑受体，学习,和认知，包括5-羟色胺5-HT2A,多巴胺D2和食欲素受体.总之,DOTA重新定位靶向重要生物学途径的有希望的药物，并有望改善患者的认知，昼夜节律,和AD发病机制。
