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Advances in the design and discovery of small-molecule tyrosine kinase inhibitors for breast cancer

Jul 2026 · RSC Advances · Vol 16, pp. 31815 - 31855 · 1 citation · 268 references
Medicine

TL;DR

These efforts underscore the importance of integrating scaffolds from promising candidate and clinically approved drugs to advance next-generation kinase inhibitors targeting breast cancer subtypes, thereby overcoming challenges and improving structural optimization outcomes.

Abstract

Breast cancer remains a leading cause of morbidity and mortality among women worldwide, necessitating innovative therapeutic strategies to overcome drug resistance and tumor heterogeneity. Breast cancer is a heterogeneous disease in which dysregulated tyrosine kinase signaling drives proliferation, survival, metastasis, and resistance. Tyrosine kinase inhibitors (TKIs) are pivotal targeted agents in breast cancer therapy, owing to their ability to modulate critical signaling pathways implicated in cancer progression. This review summarizes medicinal-chemistry strategies for designing small-molecule TKIs, integrating binding-mode classifications with scaffold selection and structure-guided optimization. Representative advances include HER2-selective, dual-RTK approaches such as EGFR/VEGFR-2 and EGFR/HER2 designs that enhance activity and overcome resistance. Emerging strategies, such as lapatinib-based PROTACs, demonstrate effective EGFR/HER2 degradation and strong antiproliferative activity in HER2-driven models, and have expanded beyond the HER family to include targets such as c-MET, FGFR, BTK, FAK, SRC, and JAK, highlighting their potential against the aggressive triple-negative breast cancer subtype. Studies featuring reversible and irreversible inhibitors, multi-targeted ligands, and covalent kinase inhibitors targeting BC are presented. These efforts underscore the importance of integrating scaffolds from promising candidate and clinically approved drugs to advance next-generation kinase inhibitors targeting breast cancer subtypes, thereby overcoming challenges and improving structural optimization outcomes.

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