| Abstract: |
| Human epidermal growth factor receptor 2 positive (HER2+) breast cancer is often treated with drugs targeting the HER2 receptor and chemotherapy, but identifying the optimal regimen is challenging. Using data from a murine model of HER2+ breast cancer, treated with trastuzumab and doxorubicin, we developed a framework for model development, calibration, selection, and treatment optimization. We proposed ten different models to characterize the dynamic relationship between tumor volume and drug availability, as well as the drug-drug interaction, and used a Bayesian framework to calibrate each model. We selected the model with the highest Bayesian information criterion weight to represent the biological system. Applying optimal control theory to this model, we identified two optimal treatment protocols. In the first protocol, using the same experimental doses for both drugs, the model predicts a 45% reduction in tumor burden compared to the experimentally delivered regimen. In the second protocol, using the same experimental trastuzumab dose but only 43% of the doxorubicin dose used experimentally, the model predicts the same tumor control as achieved experimentally. Our results suggest that mathematical modeling and optimal control theory can be effective tools for identifying therapeutic regimens that maximize efficacy and minimize toxicity in HER2+ breast cancer treatment. |
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