Collaborative Research: Stochastic and Dynamic Chemotherapy Planning and Dosing

合作研究：随机和动态化疗规划和剂量

• 批准号: 1933369
• 负责人: Clifton Fuller
• 金额: $ 9.53万
• 依托单位: University of Texas, M.D. Anderson Cancer Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933369&HistoricalAwards=false
• 关键词: Collaborative; Research; Stochastic; Dynamic; Chemotherapy

This award will contribute to the advancement of national prosperity and economic welfare by developing novel methods for scheduling and dosing chemotherapy over multiple time periods under uncertainty. Chemotherapy is one of the three most common cancer therapies, yet few methods have considered its optimal use. This award will develop several innovative techniques as it studies dynamic methods of characterizing response and dosage. The research integrates both continuous and discrete decisions. One such aspect considers time: cancer and chemotherapy drugs change continuously over time, but dosing decisions occur at discrete time periods (i.e. doctor appointments). Another such aspect considers dosing decisions; chemotherapy drugs are often given via pills, which limits dosing options. These novel models present formidable modeling and computational challenges. The goals of this award are in line with the NSF mission goal of promoting the enhancement of national health. This award will involve students from under-represented groups through various programs at Rice University.This research will develop a novel framework to build next-generation models for chemotherapy scheduling and dosing decisions over time and under uncertainty. A major challenge is in determining how to blend continuous and discrete aspects of the problem in terms of time and decisions. The project's framework blends ordinary differential equations and stochastic mixed-integer programming. Unlike previous approaches, this framework allows the inclusion of various dosing and toxicity restrictions as linear constraints within the stochastic mixed-integer program. Many of the differential equations governing tumor growth can only be solved approximately, so a major goal of this research is to understand how approximations and error bounds can be integrated across the models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过开发在不确定情况下在多个时间段内安排和剂量化疗的新方法，为促进国家繁荣和经济福利做出贡献。化疗是三种最常见的癌症治疗方法之一，但很少有方法考虑到它的最佳使用。该奖项将在研究表征反应和剂量的动态方法时开发几种创新技术。这项研究综合了连续决策和离散决策。其中一个方面是考虑时间：癌症和化疗药物随着时间的推移不断变化，但剂量决定发生在不同的时间段(即医生预约)。另一个这样的方面是考虑剂量决定；化疗药物通常通过药片给药，这限制了剂量选择。这些新的模型带来了巨大的建模和计算挑战。该奖项的目标与NSF促进国民健康的使命目标是一致的。这个奖项将通过莱斯大学的各种项目让来自代表性不足群体的学生参与进来。这项研究将开发一个新的框架，以建立下一代模型，用于随着时间和不确定性的变化而做出化疗计划和剂量决策。一个主要的挑战是如何在时间和决策方面确定如何将问题的连续和离散方面混合在一起。该项目的框架融合了常微分方程式和随机混合整数规划。与以前的方法不同，该框架允许将各种剂量和毒性限制作为线性约束包含在随机混合整数规划中。许多控制肿瘤生长的微分方程式只能近似求解，因此这项研究的一个主要目标是了解如何在模型中整合近似和误差范围。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Combination Chemotherapy Optimization with Discrete Dosing

• DOI: 10.1287/ijoc.2022.0207
• 发表时间: 2023-11-09
• 期刊: INFORMS JOURNAL ON COMPUTING
• 影响因子: 2.1
• 作者: Ajayi，Temitayo;Hosseinian，Seyedmohammadhossein;Fuller，Clifton D.
• 通讯作者: Fuller，Clifton D.