基于数学建模研究中药联合化疗干预下肿瘤微环境多类细胞生长动力学的体内外相关性

Traditional Chinese medicines (TCMs) combined with chemotherapeutics for anti-tumor effects have been widely used in clinic. Since TCMs are of a great variety with complex composition, the combination usage most rely on clinical experiences without scientific theory. In scientific study, as the drugs acting on the cells in vitro but on the animals in vivo with different tumor microenvironment (TME), the valid correlations could be hardly established between the in vitro and in vivo pharmacodynamic action (PD). Thus the combination effects of TCMs with chemotherapeutics always depend on pharmacodynamic trial on animals which is low efficiency and difficult for large-scale screening. To solve the above problems, the key technology, which using bioengineered tumor model in vitro based on three-dimensional cell printing technique coupling with multiple cell types marking by bioluminescence imaging technology in vitro and in vivo, would be taken into account in this project. With the non-small cell lung cancer chosed as tumor type and elemene with cisplatin as the model drugs, in the guidance of holistic concept of TCM, the growth kinetics parameters of tumor cells and tumor associated fibroblast (CAFs) in TME would be used as PD index and effective linker between in vitro and in vivo. Then mixed-effects model repeated measures (MMRM) or nonlinear model based on support vector regression (GA-SVR) would be seleted for establishsing the correlation model between in vitro PD and in vivo PD. In order to describe the correlation better, the in vivo and in vitro data would be subsumed in the same mathematical model. Employing the optimal mathematical model, it would be possible for systemic screening of the combination usage depending on in vitro trial with high efficiency, and thus providing theoretical reference for clinical medication. This would open up a new method for the screening and research of combination therapy by chemotherapeutics with TCMs.

中药联合化疗抗肿瘤已广泛应用于临床，但中药品种繁多且成分复杂，其联合配伍及用药方案等多根据经验。科研试验中由于体内外药物作用环境、药效评价指标不同难以有效关联，其联合效果多通过动物试验评价，效率低且无法大规模筛选，很难为临床用药提供理论依据。针对以上问题，本项目拟通过“细胞3D打印体外肿瘤模型-体内外多类细胞生物发光标记”相偶联的关键技术，选择榄香烯联合顺铂治疗非小细胞肺癌为代表，在中医药整体观指导下，以药物干预下体内外肿瘤微环境中肿瘤细胞和肿瘤相关成纤维细胞的生长动力学参数为药效指标（PD）及体内外相关性纽带，建立显式混合效应模型或隐式的基于遗传算法的支撑向量回归的非线性模型，并创新性的将体内外数据纳入同一模型以更好地描述体内外相关性，达到从体外PD仿真预测体内PD的目的，从而系统高效地筛选中药联合化疗的配伍及给药方案，以为临床用药提供参考。该方法开启了中药联合化疗用药筛选与研究新模式。

中药联合化疗抗肿瘤已广泛应用于临床，但中药品种繁多且成分复杂，其联合配伍及用药方案等多根据经验。科研试验中由于体内外药物作用环境、药效评价指标不同难以有效关联，其联合效果多通过动物试验评价，效率低且无法大规模筛选，很难为临床用药提供理论依据。针对以上问题，本项目拟通过“细胞3D打印体外肿瘤模型-体内外多类细胞生物发光标记”相偶联的关键技术，选择榄香烯联合顺铂治疗非小细胞肺癌为代表，在中医药整体观指导下，以药物干预下体内外肿瘤微环境中肿瘤细胞和肿瘤相关成纤维细胞的生长动力学参数为药效指标（PD）及体内外相关性纽带，建立显式混合效应模型或隐式的基于遗传算法的支撑向量回归的非线性模型，并创新性的将体内外数据纳入同一模型以更好地描述体内外相关性，达到从体外PD仿真预测体内PD的目的，从而系统高效地筛选中药联合化疗的配伍及给药方案，以为临床用药提供参考。该方法开启了中药联合化疗用药筛选与研究新模式。