Kim Young Woo, Bak Seon Been, Song Yu Rim, Kim Chang-Eop, Lee Won-Yung
School of Korean Medicine, Dongguk University, Gyeongju, Republic of Korea.
Department of Computer Science, Kyungpook National University, Daegu, Republic of Korea.
J Ginseng Res. 2024 Jul;48(4):373-383. doi: 10.1016/j.jgr.2024.01.005. Epub 2024 Jan 24.
Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms.
We systematically explore the capabilities of network-based approaches on natural products, using as a case study. is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood.
Our study compiled and constructed a network focusing on by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model.
We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms.
our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.
网络药理学已成为理解天然产物治疗作用和机制的有力工具。然而，对于基于网络的天然产物方法在识别治疗作用和关键机制方面缺乏全面评估。
我们以[具体天然产物名称未给出]为例，系统地探索基于网络的天然产物方法的能力。[具体天然产物名称未给出]是一种广泛使用的草药，具有多种治疗益处，但其活性成分和对慢性病的作用机制尚未完全了解。
我们的研究通过收集和整合有关成分、蛋白质靶点和已知适应症的数据，编制并构建了一个以[具体天然产物名称未给出]为重点的网络。然后，我们评估了不同基于网络的方法在总结已知和未知疾病关联方面的性能。预测结果在肝星状细胞模型中得到验证。
我们发现基于多尺度相互作用的方法实现了0.697的曲线下面积（AUROC）和0.026的精确率均值（AUPR），优于其他基于网络的方法。作为案例研究，我们进一步测试了基于多尺度相互作用组的方法识别乳腺癌和肝硬化活性成分及其合理机制的能力。我们还在肝硬化和胃肠道肿瘤病例中验证了未报告和预测排名靠前的成分的有益效果。
我们的研究提供了一个有前景的框架，用于系统地探索天然产物的治疗作用和关键机制，并突出了基于网络的方法在天然产物研究中的潜力。
