Pathway-based prediction of the therapeutic effects and mode of action of custom-made multiherbal medicines.

IF 2.8 4区医学 Q3 CHEMISTRY, MEDICINAL

Molecular Informatics Pub Date : 2024-11-01 Epub Date: 2024-10-15 DOI:10.1002/minf.202400108

Akihiro Ezoe, Yuki Shimada, Ryusuke Sawada, Akihiro Douke, Tomokazu Shibata, Makoto Kadowaki, Yoshihiro Yamanishi

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引用次数: 0

Abstract

Multiherbal medicines are traditionally used as personalized medicines with custom combinations of crude drugs; however, the mechanisms of multiherbal medicines are unclear. In this study, we developed a novel pathway-based method to predict therapeutic effects and the mode of action of custom-made multiherbal medicines using machine learning. This method considers disease-related pathways as therapeutic targets and evaluates the comprehensive influence of constituent compounds on their potential target proteins in the disease-related pathways. Our proposed method enabled us to comprehensively predict new indications of 194 Kampo medicines for 87 diseases. Using Kampo-induced transcriptomic data, we demonstrated that Kampo constituent compounds stimulated the disease-related proteins and a customized Kampo formula enhanced the efficacy compared with an existing Kampo formula. The proposed method will be useful for discovering effective Kampo medicines and optimizing custom-made multiherbal medicines in practice.

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基于途径预测定制多草药的治疗效果和作用模式。

多草药传统上被用作个性化药物，对粗制药物进行定制组合；然而，多草药的作用机制尚不清楚。在本研究中，我们开发了一种基于通路的新方法，利用机器学习预测定制多草药的治疗效果和作用模式。该方法将疾病相关通路视为治疗靶点，并评估组成化合物对疾病相关通路中潜在靶蛋白的综合影响。我们提出的方法使我们能够全面预测 194 种康普药对 87 种疾病的新适应症。我们利用堪布诱导的转录组数据证明，堪布成分化合物刺激了疾病相关蛋白，与现有堪布配方相比，定制的堪布配方提高了疗效。所提出的方法将有助于在实践中发现有效的康普药物和优化定制的多草药。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.30

自引率

2.80%

发文量

审稿时长

3 months

期刊介绍： Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.