Prediction of quality markers in Maren Runchang pill for constipation using machine learning and network pharmacology†

IF 3 4区生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular omics Pub Date : 2024-01-30 DOI:10.1039/D3MO00221G

Yunxiao Liu, Lanping Guo, Qi Li, Wencui Yang and Hongjing Dong

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Abstract

Maren Runchang pill (MRRCP) is a Chinese patent medicine used to treat constipation in clinics. It has multi-component and multi-target characteristics, and there is an urgent need to screen markers to ensure its quality. The aim of this study was to screen quality markers of MRRCP based on a “differential compounds-bioactivity” strategy using machine learning and network pharmacology to ensure the effectiveness and stability of MRRCP. In this study, UPLC-Q-TOF-MS/MS was used to identify chemical compounds in MRRCP and machine learning algorithms were applied to screen differential compounds. The quality markers were further screened by network pharmacology. Meanwhile, molecular docking was used to verify the screening results of machine learning and network pharmacology. A total of 28 constituents in MRRCP were identified, and four differential compounds were screened by machine learning algorithms. Subsequently, a total of two quality markers (rutin and rubiadin) in MRRCP. Additionally, the molecular docking results showed that quality markers could spontaneously bind to core targets. This study provides a reference for improving the quality evaluation method of MRRCP to ensure its quality. More importantly, it provided a new approach to screen quality markers in Chinese patent medicines.

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利用机器学习和网络药理学预测马仁润肠丸中有关便秘的质量指标

麻仁润肠丸（MRRCP）是临床治疗便秘的中成药。它具有多成分、多靶点的特点，迫切需要筛选标志物以确保其质量。本研究旨在利用机器学习和网络药理学，基于 "差异化合物-生物活性 "策略筛选MRRCP的质量标记物，以确保MRRCP的有效性和稳定性。本研究采用UPLC-Q-TOF-MS/MS鉴定MRRCP中的化合物，并应用机器学习算法筛选差异化合物。质量标记物通过网络药理学进一步筛选。同时，采用分子对接法对机器学习和网络药理学的筛选结果进行验证。共鉴定出 MRRCP 中的 28 种成分，并通过机器学习算法筛选出 4 种差异化合物。随后，MRRCP 中共有 2 个质量标志物（芦丁和卢比卡丁）。此外，分子对接结果表明，质量标记物可以自发地与核心靶标结合。这项研究为改进 MRRCP 的质量评价方法以确保其质量提供了参考。更重要的是，它为筛选中成药质量标志物提供了一种新方法。

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来源期刊

Molecular omics Biochemistry, Genetics and Molecular Biology-Biochemistry

CiteScore

5.40

自引率

3.40%

发文量

期刊介绍： Molecular Omics publishes high-quality research from across the -omics sciences. Topics include, but are not limited to: -omics studies to gain mechanistic insight into biological processes – for example, determining the mode of action of a drug or the basis of a particular phenotype, such as drought tolerance -omics studies for clinical applications with validation, such as finding biomarkers for diagnostics or potential new drug targets -omics studies looking at the sub-cellular make-up of cells – for example, the subcellular localisation of certain proteins or post-translational modifications or new imaging techniques -studies presenting new methods and tools to support omics studies, including new spectroscopic/chromatographic techniques, chip-based/array technologies and new classification/data analysis techniques. New methods should be proven and demonstrate an advance in the field. Molecular Omics only accepts articles of high importance and interest that provide significant new insight into important chemical or biological problems. This could be fundamental research that significantly increases understanding or research that demonstrates clear functional benefits. Papers reporting new results that could be routinely predicted, do not show a significant improvement over known research, or are of interest only to the specialist in the area are not suitable for publication in Molecular Omics.