Rational Design of Combination Therapy Administration Schedules Using Mathematical Modeling

使用数学模型合理设计联合治疗给药方案

• 批准号: 9760838
• 负责人: Shayna Renee Stein
• 金额: $ 3.65万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9760838
• 关键词: ATR gene; Biological Assay; CDK4 gene; Cancer Patient; Caring; Cell Cycle Arrest; Cells; Clinical; Clinical Research; Clinical Trials; Collaborations; Combined Modality Therapy; Computational Biology; Cyclin-Dependent Kinases; DNA Repair; DNA lesion; Dana-Farber Cancer Institute; Data; Development; Doctor of Philosophy; Dose; Drug Combinations; Drug Kinetics; Drug toxicity; Drug usage; Endocrine; Environment; Estrogen Therapy; Estrogen receptor positive; Fostering; Goals; Head and Neck Cancer; In Vitro; Individual; Institutes; Ionizing radiation; Joints; Lead; Malignant Neoplasms; Measures; Mentorship; Methods; Modeling; Patient Care; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Process; Radiation; Radio; Research; Research Personnel; Resistance; Schedule; Testing; Therapeutic; Time; Toxic effect; Translating; Treatment Efficacy; Treatment Protocols; Universities; Update; Validation; Work; anticancer research; base; clinically relevant; design; drug testing; experience; experimental study; flexibility; hormone therapy; improved; in vitro Assay; in vivo; in vivo evaluation; inhibitor/antagonist; innovation; malignant breast neoplasm; mathematical model; meetings; novel; optimal treatments; pre-clinical; preclinical study; prevent; radiation response; response; skills; success; survival outcome; targeted treatment; therapy resistant; treatment response; treatment strategy; tumor

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.

项目概要 联合疗法已使癌症患者的治疗效果得到显着改善，并且已成为常规治疗的一部分 的病人护理。然而，尽管初步证据很有希望，但联合疗法的结果仍然 令人失望。一个主要挑战是决定如何最佳地进行联合疗法，目前， 大多数联合疗法是根据所使用的单个药物的经验来进行的 单一疗法。多项临床前和临床研究表明，改变治疗给药方式 时间表可以显着改善生存结果，表明当前的管理方法 联合疗法不是最理想的。然而，系统地测试所有可能的管理是不可行的。 由于搜索空间较大，因此可以进行实验性安排。然而，数学建模非常适合 系统地搜索可能的剂量给药方案和联合疗法。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，分别以确定最佳治疗策略。 第一个目标将寻求开发辐射共济失调毛细血管扩张和 Rad3 相关的数学模型 （ATR）抑制剂联合疗法用于治疗头颈癌。第二个目标将寻求发展一个 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。尚未对任一联合疗法进行时间表优化。这 两种模型的开发遵循相似的策略。首先，对于给定的药物组合，体内实验 测量整个临床可接受给药空间下的动态治疗反应 时间表将用于开发和参数化数学模型。二、临床试验数据 用于估计药代动力学参数和药物毒性。三、模型将进行优化最大化 治疗效果，使用毒性限度作为约束。最后，最佳方案将使用体内验证 临床前研究。如果模型未经过验证，体内结果将用于更新模型。新型号 然后将被重新优化和重新验证。这个迭代过程将持续下去，直到模型成功 已验证。这些目标将带来新颖的、经过临床前验证的时间表，并可以在临床试验中进行评估。 这项研究进行的环境是一个极具影响力的计算生物学研究 由 Franziska Michor 博士领导的小组。该小组优先考虑对每位学员的个性化指导，包括 Michor 博士与每位学员每周举行一对一会议，两次会议促进了富有成效的合作 每周 Michor 实验室会议以及每月与实验合作者举行的联合实验室会议，以及创新 具有明确临床影响的研究。这个高度协作的项目将带来新颖的、经过临床前验证的 预计联合治疗的给药方案将优于当前的护理标准。