Breaking the resistance barrier: emerging nanotherapeutic strategies targeting tumor plasticity, stemness, and microenvironmental protection

Therapeutic resistance is a significant obstacle to sustained responses in solid tumors, as resistant phenotypes are linked to complex and interdependent genetic, epigenetic, metabolic, cellular and microenvironmental adaptations. The plasticity of tumors, the cancer stemness, the rewiring of oncogenic pathways, the targeting of DNA damage responses, drug resistance, metabolic flexibility and apoptosis resistance can all play their part in keeping the tumor-cell alive under therapeutic stress. Hypoxia, cancer-associated fibroblasts, vasculature, extracellular matrix remodeling, tumor-associated macrophages, regulatory T cells, extracellular vesicles signaling and hostile tumor niches are other mechanisms that reinforce these processes. This review explores the development of the use of nanomedicine in overcoming resistance mechanisms other than pharmacokinetic enhancement. Targeted delivery, resistance re-sensitization, modulation of the tumor microenvironment, metabolic intervention, delivery of nucleic acids, combination and sequential therapy, and activation of alternative regulated cell-death pathways are reviewed for polymeric, lipid, biomimetic, inorganic, hybrid and stimuli-responsive nanoplatforms. The dynamic interplay between tumor plasticity, drug-tolerant persister states, stem-like populations and stable resistance is emphasized. The review also delves into precision nanomedicine combining biomarkers, multi-omics, patient-derived organoids, patient-derived models, tumor-on-chip systems, and AI-driven formulation and selection. Finally, translational barriers such as tumor accumulation, protein corona, safety, manufacture reproducibility, scalability, regulatory complexity and patient heterogeneity are taken into account. Finally, mechanism-driven and adaptive nanomedicine may offer a rational approach to enhancing resistance management, which need to be thoroughly validated in relevant models.