10.3390/polym8030084

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Lipid nanoparticles (LNPs) are now the leading delivery platform for nucleic acid therapeutics, but progress in the field is measured almost entirely by physicochemical and computational proxies rather than by functional properties that determine therapeutic outcomes. This review examines six interconnected areas of LNP development: microfluidic manufacturing, lyophilization, transdermal microneedle delivery, machine learning-guided formulation design, endosomal escape biology, and protein corona-mediated organ targeting. Although these areas are often discussed separately, they are linked by a common gap between routinely measured physicochemical or computational endpoints and the biological outcomes that determine therapeutic performance. A recently developed antifouling coating substantially reduced microfluidic channel fouling under the tested conditions, although its scalability remains to be validated. Lyophilization, by contrast, still requires formulation specific re-optimization for each new lipid composition, which remains an important barrier to clinical translation. In microneedle-based delivery, physicochemical integrity after fabrication is routinely treated as a proxy for therapeutic function, although, to our knowledge, no published study has directly compared endosomal escape capacity before and after microneedle fabrication. In machine learning, model accuracy is limited primarily by fragmented, outcome-biased training data rather than by algorithm design. Independent measurements of endosomal escape efficiency converge on a low ceiling whose biological origin, whether lipid-specific or inherent to the mechanism, remains unknown. For organ-selective targeting, one mechanistic account rests on a hypothesis tested in advance; another, equally prominent, has not been shown to have been anticipated rather than reconstructed after the fact. Closing this gap is now the field's central methodologically priority.

Abstract: PubMed · Datensatz