Abstract
The promising properties of lipid nanoparticles (LNPs) as drug carriers have been attracting significant attention in the field of drug delivery. However, further research is still required for a better understanding of their integration in the pharmaceutical industry. The Quality by Design (QbD) approach aims at ensuring the safety and efficiency in the development of new drugs, through an holistic, risk-based approach that gathers all sources of knowledge available about the system under analysis. One key resource of the QbD framework is the rich toolkit of Design of Experiments (DOE), to deepen the understanding of how the synthesis of LNPs by microfluidics can be effectively conducted and controlled. This study aimed to explore and understand the effectiveness of different DOE strategies, through an in silico study focused on the impact of factors related to the LNPs synthesis, namely the molar ratio of each lipid component in the lipidic mixture and the N/P ratio, while also considering potential economic constraints without disregarding the need for a statistically valid analysis. A simulation model of the LNP synthesis derived from real experiments was adopted as a basis to assess the potential efficacy of estimated models with different levels of complexity, to extract useful insights in future DOEs in these types of systems, given the high cost of each experimental run. The statistical metrics used were the coefficient of determination (R2) and the Root Mean Squared Error (RMSE). With the results obtained, it was possible to verify that different responses from the same system could require quite different model structures, namely, the models developed for potency and for size of the LNPs differed significantly in their complexity. Furthermore, a number of experiments of the order of 30 can be anticipated as necessary for a DOE in a real process, involving similar factors.