基于数据挖掘与网络药理学的名中医治疗慢性肾衰竭经验传承与机制研究进展

Progress in Research on the Transmission of Renowned Traditional Chinese Medicine Practitioners’ Clinical Experience and Mechanisms in the Treatment of Chronic Renal Failure Based on Data Mining and Network Pharmacology

摘要: 慢性肾衰竭(CRF)是各种慢性肾脏病持续进展的终末阶段，具有高患病率、低知晓率、治疗难度大的特点。中医药在延缓CRF进展、改善患者生活质量方面具有独特优势，名中医的学术经验是中医药学术发展的高水平代表。然而，传统经验总结方法存在主观性强、难以量化、缺乏机制支撑等局限。近年来，数据挖掘技术与网络药理学方法的引入，为名中医治疗CRF的经验传承与机制研究提供了新的范式。本文系统综述数据挖掘在名中医治疗CRF用药规律研究中的应用现状，总结了网络药理学在阐释核心处方分子机制方面的研究进展，深入探讨了机制预测与中医理论互释的复杂性、潜在误区及整合模式的科学价值与局限性，并对核心挑战进行了批判性分析与对策展望。

Abstract: Chronic Renal Failure (CRF) represents the terminal stage of the progressive course of various chronic kidney diseases, characterized by high prevalence, low awareness and treatment challenges. Traditional Chinese Medicine (TCM) offers unique advantages in slowing the progression of CRF and improving patients’ quality of life, whilst the clinical expertise of renowned TCM practitioners represents the pinnacle of academic development in the field. However, traditional methods of summarizing clinical experience suffer from limitations such as high subjectivity, difficulty in quantification and a lack of mechanistic support. In recent years, the introduction of data mining techniques and network pharmacology methods has provided a new paradigm for the transmission of renowned TCM practitioners’ experience and the study of mechanisms underlying CRF treatment. This paper systematically reviews the current status of data mining applications in studying the prescribing patterns of renowned TCM practitioners for CRF, summarizes research progress in elucidating the molecular mechanisms of core prescriptions through network pharmacology, and critically examines the complexity and potential pitfalls of the interpretation between predicted molecular mechanisms and TCM theory, as well as the scientific value and limitations of integration models. Furthermore, it provides an in-depth critical analysis of core challenges and proposes corresponding countermeasures.