Antibody–drug conjugates (ADCs) have revolutionized breast cancer (BC) therapy by combining targeted antibody specificity with potent cytotoxic payloads, thereby enhancing efficacy while minimizing systemic toxicity. This review highlights significant innovations driving ADC development alongside persistent challenges. Recent advancements include novel antibody–drug conjugate (ADC) designs targeting diverse antigens, such as HER2, HER3, and CD276, demonstrating potent anti-tumor activity and improved strategies for drug delivery. For instance, dual-payload ADCs and those leveraging extracellular vesicles offer new dimensions in precision oncology. The integration of ADCs into sequential therapy, such as sacituzumab govitecan with TOP1/PARP inhibitors, further underscores their synergistic potential. Despite these innovations, critical challenges remain, including tumor heterogeneity and acquired drug resistance, which often involve complex molecular alterations. Moreover, optimizing ADC components, including linker chemistry and payload characteristics, is essential for ensuring stability and minimizing off-target toxicity. The burgeoning role of artificial intelligence and machine learning is pivotal in accelerating the design of ADCs, target identification, and personalized patient stratification. This review aims to comprehensively explore the cutting-edge innovations and inherent challenges in ADC development for BC, providing a holistic perspective on their current impact and future trajectory.
Sabit et al. (2025) studied this question.