In Vitro–In VivoTranslation of Lipid Nanoparticles for Hepatocellular siRNA Delivery

Bibliographic Details
Title: In Vitro–In VivoTranslation of Lipid Nanoparticles for Hepatocellular siRNA Delivery
Authors: Whitehead, Kathryn A., Matthews, Jonathan, Chang, Philip H., Niroui, Farnaz, Dorkin, J. Robert, Severgnini, Mariano, Anderson, Daniel G.
Source: ACS Nano; August 2012, Vol. 6 Issue: 8 p6922-6929, 8p
Abstract: A significant challenge in the development of clinically viable siRNA delivery systems is a lack of in vitro–in vivotranslatability: many delivery vehicles that are initially promising in cell culture do not retain efficacy in animals. Despite its importance, little information exists on the predictive nature of in vitromethodologies, most likely due to the cost and time associated with generating in vitro–in vivodata sets. Recently, high-throughput techniques have been developed that have allowed the examination of hundreds of lipid nanoparticle formulations for transfection efficiency in multiple experimental systems. The large resulting data set has allowed the development of correlations between in vitroand characterization data and in vivoefficacy for hepatocellular delivery vehicles. Consistency of formulation technique and the type of cell used for in vitroexperiments was found to significantly affect correlations, with primary hepatocytes and HeLa cells yielding the most predictive data. Interestingly, in vitrodata acquired using HeLa cells were more predictive of in vivoperformance than mouse hepatoma Hepa1-6 cells. Of the characterization parameters, only siRNA entrapment efficiency was partially predictive of in vivosilencing potential, while zeta-potential and, surprisingly, nanoparticle size (when <300 nm) as measured by dynamic light scattering were not. These data provide guiding principles in the development of clinically viable siRNA delivery materials and have the potential to reduce experimental costs while improving the translation of materials into animals.
Database: Supplemental Index
More Details
ISSN:19360851
1936086X
DOI:10.1021/nn301922x
Published in:ACS Nano
Language:English