Wu Jiawei, Guo Dianjing
State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Front Pharmacol. 2025 May 8;16:1534129. doi: 10.3389/fphar.2025.1534129. eCollection 2025.
Traditional Chinese Medicine (TCM) prescriptions are complex, multi-botanical drug systems in which dosage critically influences therapeutic efficacy. While network pharmacology is widely used to analyze TCM mechanisms, existing methods ignore the dosage of botanical drugs, a key limitation that may skew predictions. This study investigates how integrating dosage data alters network analysis outputs, addressing a fundamental gap in understanding TCM's dosage-dependent effects.
Our analysis compared dosage-weighted and traditional non-dosage network approaches across 94 traditional Chinese medicine (TCM) prescriptions. We developed four custom indicators to quantify differences throughout the network pipeline: Dedis (input distance difference), DeSD (input standard deviation difference), DeDT (drug target prediction difference), and DePy (pathway prediction difference). The interrelationships among these indicators were examined to indicate when dosage adjustments influence predictions. A detailed case study further demonstrated the impact of dosage modifications on predictive outcomes.
Among the indicators with inputs difference, Dedis, but not DeSD, exhibited a statistically significant relationship with output predictions, with target differences (DeDT) ranging from 0% to 68.9% and pathway differences (DePy) ranging from 0% to 74.6%. The interrelationships between these indicators were visualized using a clock model representation. The case study further demonstrated the impact of dosage on network outputs, revealing dosage refined both the predicted drug targets for individual botanical drugs and the subsequent pathway analysis results.
Our study demonstrated that dosage significantly influences the outcomes of network analysis, with Dedis serving as a reliable indicator of whether such changes would occur. Specifically, changes resulting from dosage-dependent refinement of both drug target prediction and pathway analysis were observed.
中药方剂是复杂的多植物药体系，其中剂量对治疗效果至关重要。虽然网络药理学被广泛用于分析中药作用机制，但现有方法忽略了植物药的剂量，这一关键限制可能会使预测产生偏差。本研究探讨整合剂量数据如何改变网络分析结果，填补了理解中药剂量依赖性效应方面的一个基本空白。
我们的分析比较了94个中药方剂的剂量加权网络方法和传统的非剂量网络方法。我们开发了四个自定义指标来量化整个网络流程中的差异：Dedis（输入距离差异）、DeSD（输入标准差差异）、DeDT（药物靶点预测差异）和DePy（通路预测差异）。研究这些指标之间的相互关系，以表明剂量调整何时会影响预测。一项详细的案例研究进一步证明了剂量调整对预测结果的影响。
在有输入差异的指标中，Dedis与输出预测存在统计学显著关系，而DeSD没有，靶点差异（DeDT）范围为0%至68.9%，通路差异（DePy）范围为0%至74.6%。这些指标之间的相互关系用时钟模型表示进行了可视化。案例研究进一步证明了剂量对网络输出的影响，揭示了剂量细化了单个植物药的预测药物靶点以及随后的通路分析结果。
我们的研究表明，剂量对网络分析结果有显著影响，Dedis可作为此类变化是否会发生的可靠指标。具体而言，观察到了药物靶点预测和通路分析因剂量依赖性细化而产生的变化。
