Rational Design of Combination Therapy Administration Schedules Using Mathematical Modeling

项目摘要 联合治疗已经导致癌症患者的结果大幅改善，并且是常规的一部分。 病人护理。然而，尽管有令人鼓舞的初步证据，联合治疗的结果已经被证明是有效的。 失望一个主要的挑战是决定如何最佳地管理联合治疗，目前， 大多数联合疗法是基于用作药物的单个药物的经验来施用的 单一疗法几项临床前和临床研究表明，改变治疗给药 时间表可以显着改善生存结果，这表明目前的管理方法， 联合治疗是次优的。然而，系统地测试所有可能的管理是不可行的 由于搜索空间很大，因此实验性地调度。然而，数学建模非常适合 系统地搜索可能的给药方案和联合治疗。这 该项目旨在开发两种新型组合疗法治疗头颈癌的数学模型 和雌激素受体阳性（ER+）乳腺癌，以确定最佳的治疗策略。 第一个目标将寻求建立一个数学模型的辐射共济失调毛细血管扩张和Rad 3相关 (ATR)用于治疗头颈癌的抑制剂组合疗法。第二个目标是发展一个 内分泌治疗-细胞周期蛋白依赖性激酶（CDK）4/6抑制剂联合治疗的数学模型 治疗ER+乳腺癌。尚未对任一联合治疗进行方案优化。的 这两种模式的发展遵循类似的战略。首先，对于给定的药物组合，体内实验 其测量在临床上可接受的施用的整个空间下的动态治疗响应 时间表将用于开发和参数化的数学模型。第二，临床试验数据将 用于估计药代动力学参数和药物毒性。第三，将优化模式，最大化 治疗效果，使用毒性限制作为约束。最后，将使用体内试验验证最佳时间表。 临床前研究。如果模型未经确认，则将使用体内结果更新模型。新模式 将被重新优化和验证。这个迭代过程将继续下去，直到模型成功 验证.这些目标将导致新的，临床前验证的时间表，可以在临床试验中进行评估。 这项研究将发生的环境是一个非常有影响力的计算生物学研究 由Franziska Michor博士领导的小组。该集团优先考虑每个学员的个性化指导，包括 Michor博士和每位学员每周一次的一对一会议，富有成效的合作， 每周举行Michor实验室会议，每月与实验合作者举行联合实验室会议， 具有明确临床影响的研究。这个高度合作的项目将导致新的，临床前验证 联合治疗给药方案被预测优于当前的护理标准。

Project Summary Combination therapies have led to drastic improvements in cancer patient outcomes, and are a routine part of patient care. However, despite promising initial evidence, results from combination therapies have been disappointing. A major challenge is deciding how to optimally administer combination therapies, and currently, most combination therapies are administered based on empirical experience of the individual drugs used as monotherapies. Several preclinical and clinical studies have shown that altering therapy administration schedules can significantly improve survival outcomes, suggesting the current methods of administering combination therapies is suboptimal. However, it is infeasible to systematically test all possible administration schedules experimentally, due to the large search space. Mathematical modeling, however, is perfectly suited to systematically search through the possible dose administration schedules and combination therapies. This project aims to develop mathematical models of two novel combination therapies to treat head and neck cancer and estrogen receptor positive (ER+) breast cancer, respectively, to identify optimal treatment strategies. The first aim will seek to develop a mathematical model of radiation-Ataxia telangiectasia and Rad3 related (ATR) inhibitor combination therapy for treating head and neck cancer. The second aim will seek to develop a mathematical model of endocrine therapy-cyclin dependent kinases (CDK) 4/6 inhibitor combination therapy for treating ER+ breast cancer. Schedule optimization has not been performed for either combination therapy. The development of both models follows a similar strategy. First, for a given drug combination, in vivo experiments that measure dynamic treatment response under the entire space of clinically acceptable administration schedules will be used to develop and parameterize the mathematical model. Second, clinical trial data will be used to estimate pharmacokinetic parameters and drug toxicity. Third, the model will be optimized to maximize treatment efficacy, using toxicity limits as constraints. Lastly, the optimal schedules will be validated using in vivo preclinical studies. If the model is not validated, in vivo results will be used to update the model. The new model will then be re-optimized and re-validated. This iterative process will continue until the model is successfully validated. These aims will lead to novel, preclinically validated schedules that can be evaluated in clinical trials. The environment in which this research will take place is a highly impactful computational biology research group led by Dr. Franziska Michor. The group prioritizes individualized mentorship of each trainee, including weekly one-on-one meetings between Dr. Michor and each trainee, productive collaborations, fostered by twice per week Michor Lab meetings and monthly joint lab meetings with experimental collaborators, and innovative research with clear clinical impact. This highly collaborative project will lead to novel, preclinically validated combination therapy administration schedules that are predicted to outperform current standards of care.