Abstract
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
in silico
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
in silico
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.