Mesoporous silica nanoparticles as an advanced tool for cancer therapy

Abstract

Mesoporous silica nanoparticles (MSNs) are becoming popular for targeted drug delivery because their pore structure can be changed, they have a large surface area, and their surface can be easily modified. This review critically assesses recent advancements in MSN-based drug delivery systems, emphasizing the structure–property–performance relationships that affect drug loading, release kinetics, and targeting efficiency, particularly in cancer therapy. We emphasize the increasing significance of data-driven and machine learning (ML) techniques in enhancing MSN synthesis and functionalization, transcending conventional design methodologies. Machine learning (ML) models have shown promise in improving predictive accuracy, reproducibility, and formulation efficiency by linking multidimensional synthesis parameters with physicochemical properties and therapeutic outcomes. We conduct a thorough examination of comparative studies concerning stimulus-responsive systems, co-delivery platforms, and theranostic MSNs, while pinpointing significant constraints related to biosafety, pharmacokinetics, scalability, and clinical translation. Finally, we talk about the problems that ML-assisted MSN design and getting regulatory approval are having right now and what could happen in the future. We regard MSNs as promising candidates for precision nanomedicine rather than as solutions that have attained clinical maturity.