Li Yao, Yue Zhiqiang, Yu Hua, Liu Xiaojian, Tao Li, Zhu Zhijie, Fan Fangtian, Shen Cunsi, Wang Aiyun, Chen Wenxing, Lu Yin
Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, PR China; Center for ADR Monitoring of Jiangsu, Nanjing 210002, Jiangsu, PR China.
Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, PR China.
J Pharmacol Toxicol Methods. 2018 Jul-Aug;92:57-66. doi: 10.1016/j.vascn.2018.03.001. Epub 2018 Mar 14.
A computational model based on clinical data from pancreatic cancer patients has been successfully created and used for predicting tumor sizes in primary and metastasis sites and survival time from kinetics of tumor cells, such as growth rate, metastasis rate and mutation rate, etc. Whether this computational model could be fitted or necessary modification of some parameters for fitting in mice is unknown. Here, we developed a computational model in mice for spontaneous metastasis to simulate the process of tumor metastasis based on the mathematical frameworks.
The spontaneous melanoma metastasis model in mice was used for assessing the fitting accuracy between the mathematical model and the experimental data and evaluating the efficacy of anticancer agents, as well as the invasion assay.
According to the modified model, most of parameters including growth rate, mutation rate and metastasis rate, which were used to describe the whole metastatic course in mice were calculated based on the experimental analysis. Furthermore, only measurement of the growth rate of cancer in the primary site was required to predict the survival time. Our predicted results of the overall survival (OS) extension of mice were close to the clinical outcomes after treated with four clinical intervention strategies of CVD, Paclitaxel, Dartmouth and Temozolomide. And predictive efficacy of anticancer drug using the model matches well the factual experimental data in mice.
The mathematical model is more economical and efficient for evaluating the tumor metastasis and could be used to screen the anti-cancer and anti-metastatic medicine by shortening the periods of assessment of OS extension in preclinical trials.
基于胰腺癌患者临床数据成功创建了一个计算模型，并用于根据肿瘤细胞动力学（如生长速率、转移速率和突变率等）预测原发部位和转移部位的肿瘤大小以及生存时间。该计算模型是否适用于小鼠或是否需要对某些参数进行调整以适用于小鼠尚不清楚。在此，我们基于数学框架开发了一种小鼠自发转移的计算模型，以模拟肿瘤转移过程。
使用小鼠自发黑色素瘤转移模型评估数学模型与实验数据之间的拟合准确性，评估抗癌药物的疗效以及侵袭试验。
根据修改后的模型，基于实验分析计算了用于描述小鼠整个转移过程的大多数参数，包括生长速率、突变速率和转移速率。此外，仅需测量原发部位癌症的生长速率即可预测生存时间。我们对小鼠总生存期（OS）延长的预测结果与采用CVD、紫杉醇、达特茅斯和替莫唑胺四种临床干预策略治疗后的临床结果相近。并且使用该模型预测抗癌药物的疗效与小鼠实际实验数据匹配良好。
该数学模型在评估肿瘤转移方面更经济高效，可通过缩短临床前试验中OS延长评估周期来筛选抗癌和抗转移药物。
