基于反馈系统控制模型，优化淋巴癌多药组合，并研究药物间的相互作用

针对淋巴癌的化疗组合药物普遍具有高疗效和低毒性的特点。然而，如何从多种药物中快速的寻找有效组合，是生物医学领域尚未完全解决的问题。我们前期的工作证明，应用反馈系统控制（FSC）的计算方法，仅仅需要测试不到千分之一的总实验量，便可以快速的从100万种药物组合里筛选出对抗疱疹病毒的最优组合。本课题将在原有工作的基础上，通过FSC的计算方法，针对10多种淋巴癌药物进行组合药物优化。从细胞水平到动物水平论证组合药物的疗效，并通过生物统计学建模定性和定量的研究各个药物之间的相互作用。针对本课题提出的FSC 优化方法，一方面解决了淋巴癌组合药物给药困难的问题，另一方面回答了多药物之间是如何相互作用的复杂基础生物学问题。 一旦成功,有望发展成为一种平台技术,为其他肿瘤的组合药物研发提供范本。

The application of drug combinations against lymphoma typically has high efficacy and low toxicity. However, how to quickly identify an effective drug combination remains a significantly unclear question in biomedical research field. Our previous reports have shown that by using a Feedback System Control (FSC) computational technique, optimal drug combinations for treating Herpes Simplex Virus type 1 (HSV-1) could be quickly identified by testing less than 0.1% of the total searching space, which has more than 1 million possibilities. This project proposes to use FSC computational approah to optimize combinations of 10 different anti-lymphoma drugs. The optimized drug combinations will be validated both in vitro and in vivo. Furthermore, bio-statistical models will be built to interpret the drug interactions in this multi-complex system. The FSC based technique developed for this project mainly aims to answer two fundamental scientific questions: 1) how to efficiently optimize a drug combination for treating lymphoma; 2) what are the complex interactions among these anti-lymphoma drugs. Once this project succeeds, the approaches demonstrated here could be potentially developed into a platform technology and provide an invaluable reference for other cancer treatments.