Lipid nanoparticle formulations for optimal RNA-based topical delivery to murine airways.

INTRODUCTION: Lipid nanoparticles (LNP) have been successfully used as a platform technology for delivering nucleic acids to the liver. To broaden the application of LNPs in targeting non-hepatic tissues, we developed LNP-based RNA therapies (siRNA or mRNA) for the respiratory tract. Such optimized LNP systems could offer an early treatment strategy for viral respiratory tract infections such as COVID-19. METHODS: We generated a small library of six LNP formulations with varying helper lipid compositions and characterized their hydrodynamic diameter, size distribution and cargo entrapment properties. Next, we screened these LNP formulations for particle uptake and evaluated their potential for transfecting mRNA encoding green fluorescence protein (GFP) or SARS-CoV2 nucleocapsid-GFP fusion reporter gene in a human airway epithelial cell line in vitro. Following LNP-siGFP delivery, GFP protein knockdown efficiency was assessed by flow cytometry to determine %GFP+ cells and median fluorescence intensity (MFI) for GFP. Finally, lead LNP candidates were validated in Friend leukemia virus B (FVB) male mice via intranasal delivery of an mRNA encoding luciferase, using in vivo bioluminescence imaging. RESULTS: Dynamic light scattering revealed that all LNP formulations contained particles with an average diameter of <100 nm and a polydispersity index of <0.2. Human airway epithelial cell lines in culture internalized LNPs with differential GFP transfection efficiencies (73-97%). The lead formulation LNP6 entrapping GFP or Nuc-GFP mRNA demonstrated the highest transfection efficiency (97%). Administration of LNP-GFP siRNA resulted in a significant reduction of GFP protein expression. For in vivo studies, intranasal delivery of LNPs containing helper lipids (DSPC, DOPC, ESM or DOPS) with luciferase mRNA showed significant increase in luminescence expression in nasal cavity and lungs by at least 10 times above baseline control. CONCLUSION: LNP formulations enable the delivery of RNA payloads into human airway epithelial cells, and in the murine respiratory system; they can be delivered to nasal mucosa and lower respiratory tract via intranasal delivery. The composition of helper lipids in LNPs crucially modulates transfection efficiencies in airway epithelia, highlighting their importance in effective delivery of therapeutic products for airways diseases.

Toward Data Visualization and Data Forecasting with COVID-19 Vaccination Statistics

The world runs on data. Various organizations, businesses, and institutions utilize and generate data. This information is a valuable commodity if availed of in the right way. Big data can be large and incomprehensible on its own, but when analyzed computationally, it can be a powerful tool for revealing patterns and trends, forecasting future values of certain data parameters as well as providing clarity about the metrics in the data. Data visualization and forecasting using such data are fields that have applications in every sector—from information technology, to education, to healthcare. Since the world was hit by the debilitating COVID-19 pandemic in 2019, life has become a blur of statistics—daily new case counts, daily deaths and recoveries, number of people vaccinated, etc. Such data are of paramount importance to everyone affected by the pandemic, and presenting it in a way that is easily understandable to a layperson and using it to glean insights into the spread and curb of the disease as well as the efficacy of the vaccines is necessary. This paper takes COVID vaccination statistics as a use case for the fields of data visualization and data forecasting. It elucidates the methodology and benefits of both interactive visualizations of vaccination data and forecasting future trends in vaccine and case metrics based on data over time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.