Shukla Rammohan, Henkel Nicholas D, Alganem Khaled, Hamoud Abdul-Rizaq, Reigle James, Alnafisah Rawan S, Eby Hunter M, Imami Ali S, Creeden Justin F, Miruzzi Scott A, Meller Jaroslaw, Mccullumsmith Robert E
Department of Neurosciences, University of Toledo, Toledo, OH, USA.
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Neuropsychopharmacology. 2021 Jan;46(1):116-130. doi: 10.1038/s41386-020-0752-6. Epub 2020 Jun 30.
CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.
中枢神经系统疾病,尤其是精神疾病,缺乏能改变疾病进程的确定性治疗方法。对驱动这些疾病的机制了解有限,加上药物开发的缓慢速度和高昂成本,使这一问题更加严重。出于这些原因,与从头开发新药相比,药物再利用——一种成本更低且效率更高的做法——一直是克服这些使人衰弱的疾病可用治疗方法匮乏的一种有前景的策略。虽然经验性药物再利用在临床精神病学中一直是常规做法,但利用大数据(“组学”)进行创新、明智且具有成本效益的再利用努力旨在通过结构和转录组特征来表征药物。这些策略与本体整合相结合,为应对中枢神经系统疾病药物开发中基于知识的挑战提供了重要机会。在本综述中,我们讨论了各种基于特征的计算机辅助药物再利用方法、其与多个组学平台的整合,以及这些数据如何用于临床相关的、基于证据的药物再利用。这些工具提供了一条令人兴奋的转化途径,可将基于组学的药物发现平台与患者特异性疾病特征相结合,最终促进针对多种精神疾病的新疗法的识别。