ReviewDrug design, development and therapy2026
Advances in Drug Delivery Systems for Breast Cancer: From Microenvironment Barriers and Smart Carriers to Clinical Translation Strategies.
Review in Drug design, development and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Breast cancer heterogeneity and its complex tumor microenvironment (TME) are key drivers of therapeutic failure and multidrug resistance (MDR). Drug delivery systems (DDS) provide an important approach, but their clinical translation remains limited by biological variability, formulation complexity, and manufacturing challenges. This review delineates the evolution of breast cancer DDS from passive carriers to increasingly functional and responsive platforms, focusing on three core dimensions. First, regarding decisive barriers, we dissect the constraints imposed by the three-dimensional collagen network, pH/glutathione (GSH) gradients, and tumor-associated macrophages (TAMs) on drug penetration. These factors not only act as physical and biochemical obstacles but can also be harnessed as endogenous triggers for responsive design. Second, in terms of diversified platforms, we critically evaluate liposomes, polymeric nanoparticles, hydrogels, extracellular vesicles, and antibody-drug conjugates (ADCs). We highlight the potential of solid lipid nanoparticles to address drug resistance, the development of carrier-free delivery enabled by prodrug self-assembly, and the clinical significance of ADCs (eg, T-DXd) in expanding treatment options for HER2-low breast cancer. Third, we examine inter- and intratumoral heterogeneity, metastatic site-specific delivery barriers in the brain, bone, and lung, the multilayered resistance network from efflux pumps to cancer stem cells, and the sequential barriers nanomedicines must traverse from systemic circulation to intracellular targets. We propose three strategic directions: personalized precision delivery integrating liquid biopsy, organoids, and functional imaging to support patient stratification and treatment adaptation; AI-assisted formulation optimization using machine learning to develop material-structure-function predictive models and support data-driven formulation design; and multi-responsive systems leveraging endogenous signals such as pH, GSH, and enzymes, together with exogenous stimuli including light, magnetism, and ultrasound, for spatiotemporally controlled combination therapy. This review summarizes key considerations from biological barriers to clinical translation, emphasizing that alignment of tumor subtype, metastatic site, microenvironmental characteristics, and patient-specific factors may improve the clinical applicability of drug delivery strategies.
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