Sarah Lindsay

Background/Objectives: The development of lipid nanoparticles (LNPs) as delivery platforms for nucleic acids has revolutionised possibilities for both therapeutic and vaccine applications. However, emerging studies highlight challenges in achieving reliable in vitro–in vivo correlation (IVIVC), which delays the translation of experimental findings into clinical applications. This study investigates these potential discrepancies by evaluating the physicochemical properties, in vitro efficacy (across three commonly used cell lines), and in vivo performance (mRNA expression and vaccine efficacy) of four LNP formulations. Methods: LNPs composed of DSPC, cholesterol, a PEGylated lipid, and one of four ionizable lipids (SM-102, ALC-0315, MC3, or C12-200) were manufactured using microfluidics. Results: All formulations exhibited comparable physicochemical properties, as expected (size 70–100 nm, low PDI, near-neutral zeta potential, and high mRNA encapsulation). In vitro studies demonstrated variable LNP-mediated mRNA expression in both immortalised and immune cells, with SM-102 inducing significantly higher protein expression (p < 0.05) than the other formulations in immortalised and immune cells. However, in vivo results revealed that ALC-0315 and SM-102-based LNPs achieved significantly (p < 0.05) higher protein expression without a significant difference between them, while MC3- and C12-200-based LNPs exhibited lower expression levels. As vaccine formulations, all LNPs elicited strong immune responses with no significant differences among them. Conclusions: These findings highlight the complexities of correlating in vitro and in vivo outcomes in LNP development and demonstrate the importance of holistic evaluation strategies to optimise their clinical translation.

Microfluidic liposome production presents a streamlined pathway for expediting the translation of liposomal formulations from the laboratory setting to clinical applications. Using this production method, resultant liposome characteristics can be tuned through the control of both the formulation parameters (including the lipids and solvents used) and production parameters (including the production speed and mixing ratio). Therefore, the aim of this study was to investigate the relationship between not only total flow rate (TFR), the fraction of the aqueous flow rate over the organic flow rate (flow rate ratio (FRR)), and the lipid concentration, but also the solvent selection, aqueous buffer, and production temperature. To achieve this, we used temperature, applying a design of experiment (DoE) combined with machine learning. This study demonstrated that liposome size and polydispersity were influenced by manipulation of not only the total flow rate and flow rate ratio but also through the lipids, lipid concentration, and solvent selection, such that liposome attributes can be in-process controlled, and all factors should be considered within a manufacturing process as impacting on liposome critical quality attributes.