Rational Design of Combination Therapy Administration Schedules Using Mathematical Modeling

项目摘要 联合治疗已导致癌症患者预后的显著改善，并且是常规治疗的一部分。 对病人的关怀。然而，尽管最初的证据很有希望，但联合治疗的结果已经 令人失望。一个主要的挑战是决定如何以最佳方式实施联合疗法，目前， 大多数联合疗法的实施是基于单独使用的药物的经验 单一疗法。一些临床前和临床研究表明，改变治疗用药 时间表可以显著改善生存结果，建议目前的给药方法 联合疗法不是最优的。然而，系统地测试所有可能的给药是不可行的 由于搜索空间很大，计划是实验性的。然而，数学建模非常适合 系统地搜索可能的剂量给药计划和联合治疗。这 该项目旨在开发两种治疗头颈癌的新型联合疗法的数学模型 和雌激素受体阳性(ER+)乳腺癌分别确定最佳治疗策略。 第一个目标将寻求建立一个辐射-毛细血管扩张性共济失调和RAD3相关的数学模型。 (ATR)抑制剂联合疗法治疗头颈癌。第二个目标将寻求开发一种 内分泌治疗-细胞周期蛋白依赖性激酶4/6抑制剂联合治疗的数学模型 治疗ER+乳腺癌。对于任何一种联合治疗，都没有进行时间表优化。这个 这两种模式的开发都遵循类似的战略。首先，对于给定的药物组合，进行活体实验 在整个临床可接受的给药范围内测量动态治疗反应 时间表将被用来开发数学模型并将其参数化。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，模型将进行优化，以最大限度地 治疗效果，以毒性限度为约束条件。最后，将使用活体来验证最优调度 临床前研究。如果模型没有被验证，体内结果将被用来更新模型。新车型 然后将被重新优化和重新验证。此迭代过程将一直持续到模型成功为止 已验证。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。 这项研究的环境将是一项具有高度影响力的计算生物学研究 由Franziska Michor博士领导的小组。该小组优先为每个受训人员提供个性化的指导，包括 Michor博士和每个实习生每周一对一的会面，富有成效的合作，通过两次 每周召开Michor实验室会议和每月与实验合作者的联合实验室会议，以及创新 具有明确临床影响的研究。这一高度协作的项目将带来新的、临床前验证的 联合治疗给药时间表，预计将超过目前的护理标准。

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.