背景:作为一个“多组分和多功效”的复杂系统,传统中药在临床疾病治疗中分布广泛,应用广泛。然而,由于生产过程中的不确定因素造成的成分的影响和含量不同,导致疗效偏差和临床定位不明确。它进一步限制了资源分配和临床用药,阻碍了现代化和全球化。在这项研究中,创新性地提出了一种精确的疗效确定策略,旨在定量预测草药的功效并获得精确的药材。表征功效的质量标记(Q-标记)有助于实现精确的功效测定。
目的:以黄芪(AR)的抗癌疗效为例,本研究旨在建立一种基于表征特定功效的Q标记的精确功效测定方法。
方法:以Q标记的基本原理为指导,通过分子模拟和网络药理学筛选了表征AR抗癌功效的潜在Q标记。在MDA-MB-231细胞上评估Q标记的活性,用高效液相色谱法测定Q标记物的含量。通过效应成分指数(ECI)和机器学习,进一步构建了影响因素与抗癌疗效关系的定量疗效预测模型,并通过生物技术验证,可以直接应用于预测大量样品的功效。
结果:黄芪甲苷,黄芪甲苷Ⅱ,黄芪甲苷抑制MDA-MB-231细胞的增殖,并在AR样品中成功定量,反映Q标记的有效性和可测量性。梯度提升回归在EVtest=0.815,R2test=0.802的定量疗效预测模型中表现最佳。精确药效测定结果表明,1-2-3(乌寨,山西,两年,C段)样品在54批具有偏倚抗癌功效的AR样品中表现最好。此外,具有较高ECI的AR样品具有较高的抗癌功效,反之亦然。
结论:本研究中建立的精确疗效确定策略是可靠的,并在AR病例中得到了证明。有望支持资源分配优化,功效稳定性改善,和精确的临床用药成就。
BACKGROUND: As a \"multi-components and multi-efficacy\" complex system, traditional Chinese herbs are universally distributed and applied in treating clinical diseases. However, the efficacy deviation and ambiguous clinical location are affected by different effects and content of components caused by uncertain factors in the production process. It further restricts resource allocation and clinical medication and hinders modernization and globalization. In this study, a precise efficacy determination strategy was innovatively proposed, aiming to quantitatively predict the efficacy of herbs and obtain precise medicinal materials. Quality-markers (Q-markers) characterizing the efficacy are conducive to achieving precise efficacy determination.
OBJECTIVE: With the anticancer efficacy of Astragali radix (AR) as a
case, the present study was designed to establish a methodology for precise efficacy determination based on Q-markers characterizing specific efficacy.
METHODS: Guided by the basic principles of Q-markers, the potential Q-markers characterizing the anticancer efficacy of AR were screened through molecular simulation and network pharmacology. The activity of Q-markers was evaluated on MDA-MB-231 cells, and the content of Q-markers was determined by HPLC. A quantitative efficacy prediction model of the relationship between the influencing factors and anticancer efficacy was further constructed through the effect-constituents index (ECI) and machine learning and verified by biotechnology, which can be directly applied to predict the efficacy in numerous samples.
RESULTS: Astragaloside I, astragaloside II, and astragaloside III inhibited the proliferation of MDA-MB-231 cells and were successfully quantified in AR samples, reflecting the effectiveness and measurability of Q-markers. Gradient Boost Regression showed the best performance in the quantitative efficacy prediction model with EVtest= 0.815, R2test= 0.802. The results of precise efficacy determination indicated that 1-2-3 (Wuzhai, Shanxi, two years, C segment) sample performed best in 54 batches of AR samples with biased anticancer efficacy. Furthermore, AR samples with higher ECI had higher anticancer efficacy and vice versa.
CONCLUSIONS: The precise efficacy determination strategy established in the present study is reliable and proved in the AR
case, which is expected to support resource allocation optimization, efficacy stability improvement, and precise clinical medication achievement.