Mathematical Modeling of the Chemotherapy, Immunotherapy and Vaccine Therapy of Cancer

癌症化疗、免疫治疗和疫苗治疗的数学模型

• 批准号: 0414011
• 负责人: Lisette de Pillis
• 金额: $ 32.83万
• 依托单位: Harvey Mudd College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414011&HistoricalAwards=false
• 关键词: Mathematical; Modeling; Chemotherapy; Immunotherapy; Vaccine

The investigators bring together expertise in the areas ofdifferential equations and numerics, differential geometry, andoptimal control with the goal of developing mathematical modelingand analysis tools applied to the creation and testing of newcombinations of chemo-immunotherapies for treating cancer. Theinvestigators, together with their students, carry out work alongthree complementary paths. First, dynamical systems populationmodels are created and analyzed to determine fundamental systembehavior that helps guide the development of combinationtherapies. Second, differential geometry approaches allow forthe analysis of idealized geometric forms approximating physicalstructures of spherical, cylindrical, elliptic, and harmonicspherical tumors, which can closely approximate certain in vivotumor geometries. Third, optimal control theory is used toreveal paths to new optimal combination therapies withimplementation of a variety of constraints to minimize tumorburden while keeping the patient's immune and normal cellpopulations above a healthy threshold. The results of theseinvestigations have the potential to provide guidance in thestructuring of improved patient-specific treatment protocols. The mathematical modeling of cancer growth and combinationtreatment strategies adds to our basic understanding of cancerresponse mechanisms in fundamental ways. The simulations,geometric visualization, and optimization the investigatorsundertake allow for an array of virtual experiments to be runthat can be performed quickly with no risk to living persons, butthat provide data that can significantly benefit medical decisionmaking. The modeling of cancer growth and treatment requiresskills from multiple disciplines. A major outcome of thisproject is the development of software for simulating andvisualizing cancer growth and treatment pathways, involvingchemotherapy and immunotherapy, which can result in the creationof new mathematically guided patient-specific combinationtreatment strategies. This work lies at the intersection ofinformation technology and biotechnology, with mathematics at thecore. The investigators have the ability to funnel discoveriesfor new treatment strategies suggested by the mathematicsdirectly to collaborating clinical oncologists through theongoing meetings of a California-based Mathematics of MedicineStudy group. In turn, the physicians in this group can enhancethe development of the mathematical models by sharing outcomes ofongoing clinical trials. The investigators directly involveundergraduates in the multiple facets of this cutting-edgeendeavor through research assistantships, courses, andindependent study. A unique aspect of this project is thecollaboration of three women investigators who are attractingmore women students to this area of research. The investigatorsas a team can serve as role models on the forefront of the fightagainst cancer, and in particular are in a good position toaffect breast cancer studies, which are of special importance towomen's health. The results from solving the challengingmathematical, computational and biological problems of thisproject are made publicly accessible through publications andweb-based postings. This collaborative effort has the potentialto help clinicians save lives and reduce the suffering of cancerpatients.

研究人员汇集了微分方程和数值，微分几何和最优控制领域的专业知识，目标是开发数学建模和分析工具，用于创建和测试治疗癌症的化学免疫疗法的新组合。 调查人员和他们的学生一起沿着三条互补的道路开展工作。 首先，创建和分析动力系统群体模型，以确定有助于指导联合疗法发展的基本系统行为。 其次，微分几何方法允许分析理想化的几何形式近似的物理结构的球形，圆柱形，椭圆形和harmonicspherical肿瘤，可以密切接近某些在vivotumor几何形状。 第三，最优控制理论用于揭示新的最优组合疗法的路径，该疗法具有各种约束条件，以最小化肿瘤负荷，同时保持患者的免疫和正常细胞群体高于健康阈值。 这些调查的结果有可能为改进患者特异性治疗方案的结构提供指导。 癌症生长的数学模型和联合治疗策略从根本上增加了我们对癌症反应机制的基本理解。 模拟，几何可视化和优化的模拟器允许一系列虚拟实验运行，可以快速执行，对活人没有风险，但提供的数据可以显着有益于医疗决策。 癌症生长和治疗的建模需要多学科的技能。 该项目的一个主要成果是开发了用于模拟和可视化癌症生长和治疗途径的软件，包括化疗和免疫治疗，这可能导致创建新的数学指导的患者特异性组合治疗策略。 这项工作处于信息技术和生物技术的交叉点，数学是其核心。 研究人员有能力通过正在进行的加州医学数学研究小组会议，直接向合作的临床肿瘤学家提供新治疗策略的建议。反过来，这一组的医生可以通过分享正在进行的临床试验的结果来加强数学模型的开发。 调查人员通过研究助学金、课程和独立学习，直接让本科生参与到这一前沿研究的多个方面。 该项目的一个独特之处是三名女调查员的合作，她们吸引了更多的女学生进入这一研究领域。 作为一个团队，这些研究人员可以作为对抗癌症前沿的榜样，特别是在影响乳腺癌研究方面处于有利地位，这对妇女的健康特别重要。 解决这个项目的挑战性数学、计算和生物问题的结果通过出版物和基于网络的帖子公开。 这种合作努力有可能帮助临床医生挽救生命，减少癌症患者的痛苦。