Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers,...

Breast cancer is a biologically heterogeneous disease in which tumor-intrinsic diversity and the tumor immune microenvironment jointly shape therapeutic resistance and variable clinical outcomes. Although nanomedicine has improved the safety and pharmacokinetic profiles of several anticancer agents, clinically approved nanocarriers have produced limited efficacy gains, partly because of heterogeneous tumor accumulation, restricted penetration, and empirical formulation design. Polymeric lipid nanoparticles (PLNs), also known as lipid-polymer hybrid nanoparticles, provide a tunable core-shell platform that combines the structural stability of polymeric systems with the biomimetic and functional versatility of lipid-based carriers. These properties enable controlled drug loading, adjustable release kinetics, and surface engineering for targeting or immune modulation. Artificial intelligence (AI) may support PLN development by organizing complex formulation variables and prioritizing experimentally testable designs rather than replacing mechanistic nanobiology. Machine learning, graph-based models, generative approaches, and predictive pharmacokinetic frameworks can help connect biological barriers, including receptor heterogeneity, stromal restriction, immune contexture, and delivery variability, with modifiable formulation parameters such as particle size, lipid-polymer composition, ligand density, and release behavior. Microfluidic manufacturing may further improve reproducibility by translating computationally prioritized formulations into controlled physical nanoparticles. This review summarizes the structural rationale and functional advantages of PLNs in breast cancer, evaluates barrier-oriented PLN design strategies, and examines the role of AI in formulation optimization, biological fate prediction, drug-release modeling, and translational workflow design. We also discuss current limitations, including data scarcity, limited PLN-specific validation, clinical delivery heterogeneity, and regulatory challenges. Overall, AI-guided PLN development should be viewed as a biology-informed and manufacturing-aware framework for improving formulation prioritization and reproducibility, rather than as an immediate clinical solution.