Algorithm-assisted individualized therapy design improves survival in a mouse model of triple-negative breast cancer

Gombos, Balázs and Léner, Violetta and Drexler, Dániel András and Czakó, Bence Géza and Ferenci, Tamás and Kovács, Levente and Kiss, Dániel and Szabó, Pál Tamás and Tóvári, József and Szakács, Gergely and Füredi, András (2026) Algorithm-assisted individualized therapy design improves survival in a mouse model of triple-negative breast cancer. NPJ PRECISION ONCOLOGY, 10 (1). No. 84. ISSN 2397-768X

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Abstract

Chemotherapy remains indispensable in the treatment of malignant tumors but is often limited by the prevailing “one size fits all” approach, which neglects inter-patient variablity in pharmacokinetics and treatment response, often resulting in suboptimal outcomes. In this study, we explored individualized chemotherapy protocols in a clinically relevant mouse model of breast cancer using a novel algorithm-assisted therapy design (AATD). Two strategies were applied: a two-stage computational therapy protocol designed to stabilize blood concentrations of pegylated liposomal doxorubicin (PLD); and a model-predictive approach that optimizes dosing based on individual tumor characteristics. Compared to the standard maximum tolerated dose protocol, AATD-based personalized chemotherapy, guided by real-time monitoring of treatment response, tumor growth, and drug concentrations, significantly improved overall survival. Our findings in a mouse model of triple-negative breast cancer provide compelling evidence that chemotherapy can be personalized and optimized through algorithm-assisted therapy design.

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