Jung Seunghwan, Kim Kwansoo, Wang Seunghyun, Han Manyoung, Lee Doheon
Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
Comput Struct Biotechnol J. 2024 Oct 29;23:3869-3877. doi: 10.1016/j.csbj.2024.10.035. eCollection 2024 Dec.
The drug discovery pipelines require enormous time and cost, albeit their infamously high risk of failures. Reducing such risk has therefore been the utmost goal in the process. Recently, natural products (NPs) in traditional oriental medicine (TOM) have come into the spotlight for their efficacy and safety supported throughout the history. Not only that, with the ever-increasing repository of various biological datasets, many data-driven approaches have also been extensively studied for better efficient search and testing. However, TOM-based datasets lack information on recently prevalent diseases, while experimental datasets are prone to provide target spaces that are too large. Adequate combination of both approaches can therefore fill in each other's blanks. In this study, we introduce NaCTR, an discovery pipeline that achieves such integration to suggest NPs-derived drug candidates for a given disease. First, phenotypes and disease genes for the disease are identified in literature and public databases. Secondly, a pool of potentially therapeutic NPs are identified based on both TOM-based phenotype records and compound-gene interaction datasets. Lastly, the compounds contained in the NPs are further screened for toxicity and pharmacokinetic properties. We use the Parkinson's disease as the case study to test the NaCTR pipeline. Through the pipeline, we propose glutathione and four other compounds as novel drug candidates. We further highlight the finding with literature support. As the first to effectively combine data from ancient and recent repositories, the NaCTR pipeline can be a novel pipeline that can be applied successfully to any other diseases.
药物研发流程需要大量的时间和成本，尽管其失败风险高得臭名昭著。因此，降低这种风险一直是该过程中的首要目标。最近，传统东方医学（TOM）中的天然产物（NPs）因其贯穿历史的疗效和安全性而备受关注。不仅如此，随着各种生物数据集的不断增加，许多数据驱动的方法也被广泛研究，以实现更高效的搜索和测试。然而，基于TOM的数据集缺乏关于近期流行疾病的信息，而实验数据集往往提供过大的目标空间。因此，将这两种方法充分结合可以相互弥补不足。在本研究中，我们介绍了NaCTR，这是一种实现这种整合的发现流程，可为给定疾病推荐源自NPs的候选药物。首先，在文献和公共数据库中识别该疾病的表型和疾病基因。其次，基于基于TOM的表型记录和化合物-基因相互作用数据集，识别出一批潜在的治疗性NPs。最后，对NPs中包含的化合物进一步筛选其毒性和药代动力学特性。我们以帕金森病为例来测试NaCTR流程。通过该流程，我们提出谷胱甘肽和其他四种化合物作为新型候选药物。我们还通过文献支持进一步突出了这一发现。作为首个有效整合古代和近期数据库数据的流程，NaCTR流程可以成为一种能够成功应用于任何其他疾病的新型流程。
