Modeling breast cancer proliferation, drug synergies, and alternating therapies

Wei He; Diane M Demas; Ayesha N Shajahan-Haq; William T Baumann

doi:10.1016/j.isci.2023.106714

Modeling breast cancer proliferation, drug synergies, and alternating therapies

iScience. 2023 Apr 23;26(5):106714. doi: 10.1016/j.isci.2023.106714. eCollection 2023 May 19.

Authors

Wei He¹, Diane M Demas², Ayesha N Shajahan-Haq², William T Baumann³

Affiliations

¹ Program in Genetics, Bioinformatics, and Computational Biology, VT BIOTRANS, Virginia Tech, Blacksburg, VA 24061, USA.
² Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
³ Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA.

Abstract

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Keywords: Cancer; Cancer systems biology; Computational bioinformatics; Mathematical biosciences; Pharmacoinformatics.

Abstract

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