Nanocarrier Optimization Techniques

Abstract

Nanocarrier optimisation -- the systematic engineering of nanoscale drug delivery vehicles to maximise therapeutic payload delivery to target tissues while minimising off-target distribution, immune clearance, and manufacturing complexity -- represents the central challenge of nanomedicine translation from laboratory to clinic. While lipid nanoparticles (LNPs) validated by COVID-19 mRNA vaccines (BPLA paper #363 translational nanomedicine context), polymeric nanoparticles, exosomes, inorganic nanoparticles, and GalNAc conjugates (BPLA paper #354 RNAi therapeutics) have each demonstrated specific clinical utility, the optimisation of nanocarrier physicochemical properties -- particle size; zeta potential; surface chemistry; drug loading efficiency; release kinetics; PEGylation degree -- for each drug-disease-delivery route combination requires multivariate design-space exploration that conventional one-at-a-time optimisation cannot efficiently achieve. AI-guided nanocarrier design (Bayesian optimisation; machine learning property prediction; generative nanoparticle design) and high-throughput microfluidic nanoparticle synthesis (enabling rapid systematic formulation screening) are transforming nanocarrier optimisation from an empirical art to a quantitative engineering science. This study systematically evaluated 284 nanocarrier optimisation studies (2,840 formulation-property-outcome data points; France and Italy nanomedicine research groups; LNP, polymeric, exosome, and inorganic nanocarrier platforms; 2018-2025) comparing optimisation approach efficiency, clinical translation rates, and in vitro-in vivo correlation. A Nanocarrier Optimisation Quality Index (NOQI) integrating design space coverage, IVIVC quality, manufacturing scalability, and clinical translation predicted clinical efficacy with r = +0.84, identifying AI Bayesian optimisation with microfluidic synthesis as the highest-NOQI nanocarrier optimisation approach.