Optimizing combination therapy in a murine model of HER2+breast cancer?
Optimizing combination therapy in a murine model of HER2+breast cancer?
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DOI：
10.1016/j.cma.2022.115484
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发表时间：
2022-11-25
影响因子：
7.2
通讯作者：
Yankeelov, Thomas E.
中科院分区：
文献类型：
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作者：
Lima, Ernesto A. B. F.;Wyde, Reid A. F.;Yankeelov, Thomas E.
人表皮生长因子受体2阳性(HER2+)乳腺癌通常采用靶向HER2受体的药物(如曲妥珠单抗)和化疗(如阿霉素)相结合的方法治疗。然而，治疗设计中的一个悬而未决的问题是确定最佳结合这两种治疗方法的治疗方案，以产生最佳的肿瘤控制。在人类HER2+乳腺癌的小鼠模型中，我们通过量化不同曲妥珠单抗和阿霉素治疗方案引起的肿瘤体积的时间变化的数据，提出了一个完整的框架，用于模型开发、校准、选择和治疗优化，以找到最优的治疗方案。通过对药物-肿瘤相互作用的不同假设，我们提出了10种不同的模型来描述肿瘤体积与药物可利用度之间的动态关系，以及药物-药物相互作用。使用贝叶斯框架，将这些模型中的每一个校准到数据集，并选择具有最高贝叶斯信息准则权重的模型来表示生物系统。选定的模型捕捉到了曲妥珠单抗因用多柔比星预治疗而抑制，以及因曲妥珠单抗预治疗而增加阿霉素疗效的情况。然后，我们将最优控制理论(OCT)应用于该模型，以识别两种最优处理方案。在第一个优化方案中，我们将阿霉素和曲妥珠单抗的最大剂量固定为与实验中提供的最大剂量相同，同时试图将肿瘤负担降至最低。在这一约束下，最优控制理论表明，最优方案是首先在第35天和36天给药两剂曲妥珠单抗，然后在第37天和38天给药两剂阿霉素。该方案预测，与实验提供的方案相比，肿瘤负担额外减少了45%。在第二个优化方案中，我们将肿瘤控制固定为与实验获得的相同，并尝试减少阿霉素的剂量。在这一限制下，最佳方案与第一个优化方案相同，但仅使用实验中使用的阿霉素剂量的43%。该协议预测的肿瘤控制与实验中实现的控制相当。这些结果有力地表明，数学建模和最优控制理论在确定治疗方案、最大化疗效和最小化毒性方面是有用的。(C)2022爱思唯尔B.V.保留所有权利。
Human epidermal growth factor receptor 2 positive (HER2+) breast cancer is frequently treated with drugs that target the HER2 receptor, such as trastuzumab, in combination with chemotherapy, such as doxorubicin. However, an open problem in treatment design is to determine the therapeutic regimen that optimally combines these two treatments to yield optimal tumor control. Working with data quantifying temporal changes in tumor volume due to different trastuzumab and doxorubicin treatment protocols in a murine model of human HER2+ breast cancer, we propose a complete framework for model development, calibration, selection, and treatment optimization to find the optimal treatment protocol. Through different assumptions for the drug-tumor interactions, we propose ten different models to characterize the dynamic relationship between tumor volume and drug availability, as well as the drug-drug interaction. Using a Bayesian framework, each of these models are calibrated to the dataset and the model with the highest Bayesian information criterion weight is selected to represent the biological system. The selected model captures the inhibition of trastuzumab due to pre-treatment with doxorubicin, as well as the increase in doxorubicin efficacy due to pre-treatment with trastuzumab. We then apply optimal control theory (OCT) to this model to identify two optimal treatment protocols. In the first optimized protocol, we fix the maximum dosage for doxorubicin and trastuzumab to be the same as the maximum dose delivered experimentally, while trying to minimize tumor burden. Within this constraint, optimal control theory indicates the optimal regimen is to first deliver two doses of trastuzumab on days 35 and 36, followed by two doses of doxorubicin on days 37 and 38. This protocol predicts an additional 45% reduction in tumor burden compared to that achieved with the experimentally delivered regimen. In the second optimized protocol we fix the tumor control to be the same as that obtained experimentally, and attempt to reduce the doxorubicin dose. Within this constraint, the optimal regimen is the same as the first optimized protocol but uses only 43% of the doxorubicin dose used experimentally. This protocol predicts tumor control equivalent to that achieved experimentally. These results strongly suggest the utility of mathematical modeling and optimal control theory for identifying therapeutic regimens maximizing efficacy and minimizing toxicity. (c) 2022 Elsevier B.V. All rights reserved.