Optimizing combination therapy in a murine model of HER2+ breast cancer

• Published in: Computer methods in applied mechanics and engineering Vol. 402; p. 115484
• Main Authors: Lima, Ernesto A.B.F., Wyde, Reid A.F., Sorace, Anna G., Yankeelov, Thomas E.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2022; Elsevier BV
• Subjects: Bayesian analysis; Biological models (mathematics); Breast cancer; Cancer therapies; Chemotherapy model; Control theory; Doxorubicin; Drug dosages; Effectiveness; Growth factors; Model calibration; Model selection; Optimal control; Optimal control theory; Optimization; Pretreatment; Protocol; Receptors; Toxicity; Tumor growth model; Tumors; Uncertainty quantification; Uncertainty quantification; Optimal control theory; Model calibration; Model selection; Tumor growth model; Chemotherapy model
• ISSN: 0045-7825; 1879-2138
• DOI: 10.1016/j.cma.2022.115484

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.