Cancer Drug Delivery with Nanoparticles and Biomolecules: Stimuli-Responsive, Theranostic, and AI-Guided Approaches
TL;DR
This review examines the main classes of nanoplatforms currently explored for cancer therapy, including inorganic, polymeric, lipid-based, and hybrid organic–inorganic systems, and considers emerging directions in AI-guided nanoparticle engineerization design and natural-compound-based nanomedicines, both of which are expanding the therapeutic landscape.
Abstract
Highlights Critically compares nanoplatforms through structure–property–function relationships. Identifies the engineering trade-offs limiting clinical translation of cancer nanomedicines. Unifies smart, stimuli-responsive, and theranostic strategies for precision cancer therapy. Evaluates advanced 3D tumor models as predictive tools for nanoparticle performance. Explores AI-guided nanocarrier design and biomolecule-based therapeutics for next-generation cancer drug delivery. Abstract Cancer remains a major global health challenge, and the limitations of conventional therapies, including systemic toxicity, drug resistance, and poor tumor selectivity, continue to drive the development of advanced nanomedicine strategies. In this context, nanocarriers offer promising opportunities to improve pharmacokinetics, enhance tumor accumulation, and enable controlled or stimuli-responsive drug release. Among them, inorganic nanoparticles (NPs) have gained considerable attention because of their structural stability, tunable surface chemistry, and multifunctional capabilities. Their performance depends on a structure–property–function relationship in which composition, morphology, porosity, degradability, and surface characteristics strongly influence interactions at the nano–bio interface. This review examines the main classes of nanoplatforms currently explored for cancer therapy, including inorganic, polymeric, lipid-based, and hybrid organic–inorganic systems. Particular attention is given to the trade-offs that define each platform in terms of loading capacity, biodegradability, multifunctionality, and translational potential. The discussion also highlights the role of predictive biological models, emphasizing that 3D spheroids, organoids, and organ-on-chip systems provide more realistic insights than conventional 2D assays for evaluating tumor penetration and microenvironment-responsive delivery. In addition, the review considers emerging directions in AI-guided nanoparticle engineerization design and natural-compound-based nanomedicines, both of which are expanding the therapeutic landscape. Overall, the field is moving toward more integrated, application-specific, and clinically translatable nanomedicine platforms capable of addressing the complex biological barriers of cancer treatment.