mRNA-LNP vaccines tuned for systemic immunization induce strong antitumor immunity by engaging splenic immune cells.

mRNA vaccines have recently proved to be highly effective against SARS-CoV-2. Key to their success is the lipid-based nanoparticle (LNP), which enables efficient mRNA expression and endows the vaccine with adjuvant properties that drive potent antibody responses. Effective cancer vaccines require long-lived, qualitative CD8 T cell responses instead of antibody responses. Systemic vaccination appears to be the most effective route, but necessitates adaptation of LNP composition to deliver mRNA to antigen-presenting cells. Using a design-of-experiments methodology, we tailored mRNA-LNP compositions to achieve high-magnitude tumor-specific CD8 T cell responses within a single round of optimization. Optimized LNP compositions resulted in enhanced mRNA uptake by multiple splenic immune cell populations. Type I interferon and phagocytes were found to be essential for the T cell response. Surprisingly, we also discovered a yet unidentified role of B cells in stimulating the vaccine-elicited CD8 T cell response. Optimized LNPs displayed a similar, spleen-centered biodistribution profile in non-human primates and did not trigger histopathological changes in liver and spleen, warranting their further assessment in clinical studies. Taken together, our study clarifies the relationship between nanoparticle composition and their T cell stimulatory capacity and provides novel insights into the underlying mechanisms of effective mRNA-LNP-based antitumor immunotherapy.

A Bayesian Implementation of Quality-by-Design for the Development of Cationic Nano-Lipid for siRNA Transfection.

Unlike Quality by Testing approach, where products were tested only after drug manufacturing, Quality by Design (QbD) is a proactive control quality paradigm, which handles risks from the early development steps. In QbD, regression models built from experimental data are used to predict a risk mapping called Design Space in which the developers can identify values of critical input factors leading to acceptable probabilities to meet the efficacy and safety specifications for the expected product. These empirical models are often limited to quantitative responses. Moreover, in practice the smallness and incompleteness of datasets degrade the quality of predictions. In this study, a Bayesian approach including variable selection, parameter estimation and model quality assessment is proposed and assessed using a real case study devoted to the development of a Cationic Nano-Lipid Structures for siRNA Transfection. Two original model structures are also included to describe both binary and percentage response variables. The results confirm the practical relevance and applicability of the Bayesian implementation of the QbD analysis.