基于色谱保留多元统计模型的复方中药HPLC分析方法高效构建模式研究

The redevelopment of HPLC analytical methods for traditional Chinese medicine (TCM) formulae is usually complex and time-consuming, which is one of the technological problems frequently encountered during the single component determination to multi-component determination upgrade of the quality standards of TCMs. More efficient development approaches based on the characteristics of TCM formulae are worthy of study. Therefore, this project aims to explore a rapid development approach of HPLC methods for TCM formulae, which takes advantage of the mutual regularities of chromatographic retention behaviors between TCM formulae and their constituting single TCMs (i.e. the quantitative relationships between the analytical parameters and the chromatographic parameters of TCM components). Four TCMs including Salviae Miltiorrhizae Radix et Rhizoma, Chuanxiong Rhizoma, Carthami Flos, Paeoniae Radix Rubra and various TCM formulae derived from them will be taken for case studies in this project. The quantitative regularities of the chromatographic retention behaviors of the components in the four TCMs will be studied using design of experiments methodology, multivariate curve resolution and multivariate statistical modeling methods, and then represented in the form of multivariate statistical models. Based on these models and Monte-Carlo simulations, the HPLC methods for different TCM formulae derived from the four TCMs will be optimized rapidly using feedforward control strategy. Meanwhile, the methods for transferring the statistical models between different chromatographic columns will be studied. Based on the results of these studies, a rapid development approach of HPLC methods for TCM formulae based on multivariate statistical modeling of chromatographic retention behaviors will be sufficiently developed, which will provide a new pattern of HPLC method development. The approach is expected to effectively summarize and utilize the experiences from the development of analytical methods of single TCMs to significantly facilitate the development of HPLC methods for TCM formulae, and be able to efficiently adjust the analytical methods for the determination of more different components. Therefore, the approach proposed will provide a good support for the quality standard upgrade of TCMs and also for the study of material basis transformation during the concerting of single TCMs.

针对中药质量标准从单一成分测定向多指标成分测定转变过程中HPLC分析方法重建繁琐费时的难题，围绕如何根据复方中药特点高效地建立HPLC分析方法的问题，本课题选择丹参、川芎、红花、赤芍4味中药及其配伍而成的多种复方中药为研究案例，拟利用实验设计、多元曲线分辨、多元统计建模、Monte-Carlo仿真等方法及前馈控制策略，重点研究单味中药与复方中药共有的定量色谱保留规律（即分析参数与中药成分色谱参数间的定量关系），建立多元统计模型对该规律进行表征，并研究不同色谱柱之间的模型转移方法，从而形成基于色谱保留多元统计模型的复方中药分析方法高效构建模式，为色谱分析方法的建立探索新途径。这不仅能有效地总结和利用单味中药分析方法开发经验，显著提高复方中药分析方法开发效率，而且能在含测指标增加或改变时高效地调整得到相适应的分析方法，从而为中药质量标准提升和中药配伍物质基础变化规律研究提供更好的支持。

复方中药由两味或两味以上药味配伍而成，通过多个药味配伍后作用于人体多个靶点而发挥疗效。高效液相色谱（HPLC），尤其是液相色谱-紫外联用（LC-UV）和液相色谱-质谱联用（LC-MS），是目前最常用的复方中药分析技术。HPLC分析方法建立过程中的难点在于寻找合适的分析条件，使待测成分和干扰成分在合理的分析时间内得到良好的分离。传统的分析方法开发模式主要依赖研究者的经验，不能充分利用分析数据有效地总结规律，当分析的复方或成分发生变化，原有分析方法不符合要求时，只能彻底从头开始建立新分析方法。因此，对各种中药复方逐一建立分析方法需要耗费大量人力物力。本课题根据复方中药的特点，研究了如何以从单味中药分析获取的色谱保留规律指导复方中药HPLC分析方法的高效开发，最终形成基于色谱保留多元统计模型的复方中药HPLC分析方法高效构建模式。研究内容与结果分为3个方面：（1）提出了基于色谱保留多元统计模型的复方中药LC-UV分析方法构建模式，并以丹参、川芎、赤芍、红花4味药材组合得到的8种复方中药为研究案例对其进行了应用，研究结果表明，所提出的分析方法构建模式可实现根据复方和待测成分的变化快速调整分析参数的目的。（2）提出了基于色谱保留多元统计模型的复方中药LC-MS分析方法构建模式，并以白术、白芍、麦冬、延胡索4味药材组合得到的8种复方中药为研究案例对其进行了应用，研究结果表明，所提出的分析方法构建模式可在未开展任何预实验的情况下，直接优化得到所需的色谱分离条件。（3）建立了不同C18色谱柱之间色谱保留模型转移方法，有助于将不同色谱柱上获得的定量色谱保留规律转移到目标色谱柱上。综上，本课题建立的数据驱动的分析方法构建模式能系统而高效地开发复方中药分析方法，即能通过研究数量相对较少的单味中药而建立其相互配伍而成的多种复方的分析方法。项目执行期间已发表多篇学术论文（本基金为第一标注），其中1篇论文发表于中科院分区中分析化学小类一区期刊上。