Lima Ernesto A B F, Wyde Reid A F, Sorace Anna G, Yankeelov Thomas E
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America.
Texas Advanced Computing Center, The University of Texas at Austin, United States of America.
Comput Methods Appl Mech Eng. 2022 Dec 1;402. Epub 2022 Aug 17.
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.
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）联合化疗药物（如多柔比星）进行治疗。然而，治疗方案设计中的一个悬而未决的问题是确定能将这两种治疗方法进行最佳组合以实现最佳肿瘤控制的治疗方案。我们利用在人HER2+乳腺癌小鼠模型中量化不同曲妥珠单抗和多柔比星治疗方案导致的肿瘤体积随时间变化的数据，提出了一个完整的模型开发、校准、选择和治疗优化框架，以找到最佳治疗方案。通过对药物与肿瘤相互作用的不同假设，我们提出了十种不同的模型来描述肿瘤体积与药物可用性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都校准到数据集，并选择具有最高贝叶斯信息准则权重的模型来代表生物系统。所选模型捕捉到了多柔比星预处理对曲妥珠单抗的抑制作用，以及曲妥珠单抗预处理对多柔比星疗效的增强作用。然后，我们将最优控制理论（OCT）应用于该模型以确定两种最佳治疗方案。在第一个优化方案中，我们将多柔比星和曲妥珠单抗的最大剂量固定为与实验给药的最大剂量相同，同时试图最小化肿瘤负担。在此约束条件下，最优控制理论表明最佳方案是在第35天和第36天先给予两剂曲妥珠单抗，然后在第37天和第38天给予两剂多柔比星。与实验给药方案相比，该方案预测肿瘤负担可额外降低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，并试图降低多柔比星剂量。在此约束条件下，最优方案与第一个优化方案相同，但仅使用实验所用多柔比星剂量的43%。该方案预测的肿瘤控制效果与实验实现的相当。这些结果有力地表明了数学建模和最优控制理论在确定使疗效最大化和毒性最小化的治疗方案方面的实用性。
