The treatment of human epidermal growth factor receptor 2 positive (HER2+) breast cancer often involves a combination of drugs targeting the HER2 receptor and chemotherapy. Determining the optimal therapeutic regimen that maximizes tumor control while minimizing toxicity remains a challenge. In this study, we utilized data from a murine model of HER2+ breast cancer, quantifying temporal changes in tumor volume under various trastuzumab and doxorubicin treatment protocols.
Employing a Bayesian framework, we developed, calibrated, and selected ten mathematical models characterizing the dynamic relationship between tumor volume and drug availability, as well as drug-drug interactions. Optimal control theory was then applied to identify two optimal treatment protocols. The first protocol, using the same experimental doses for both drugs, predicted an additional 45% reduction in tumor burden compared to the experimental regimen. The second protocol, utilizing the same trastuzumab dose, achieved equivalent tumor control with only 43% of the doxorubicin dose.
Our results highlight the effectiveness of mathematical modeling and optimal control theory in identifying therapeutic regimens that enhance efficacy and minimize toxicity in HER2+ breast cancer treatment.
Ernesto Lima received his Doctor of Science in Computational Modeling from the National Laboratory for Scientific Computing (LNCC) in Brazil in 2014. During his doctoral studies, he was a visitor at the University of Texas at Austin. This experience inspired him to return to the University of Texas, where he is currently a Research Associate at the Center for Computational Oncology at the Oden Institute for Computational Engineering and Sciences and a member of the Life Sciences group at the Texas Advanced Computing Center (TACC).
His research focuses on the development of numerical methods, innovative tumor growth models, treatment optimization, and model selection and calibration.
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