Publication - Abstract
Dec 18, 2018
Gene therapy can be the solution to treatment of many undruggable conditions. RNA interference (RNAi) has been identified as a powerful gene therapy tool which can regulate and improve defective biological pathways through downregulation of target genes. Small interfering RNA (siRNA) is one such approach that has been validated extensively in the recent years. siRNA treatment is however impractical for in vivo purposes without a delivery vehicle because siRNA molecules can cause toxicity and inflammation and are rapidly cleared following systemic administration. Lipid nanoparticles (LNP) are the leading delivery vehicles for systemic administration of siRNA. Their high in vivo potency leads to exceptional gene silencing and they are relatively well tolerated. Several LNP systems are currently in clinical trials with many more to join in. Despite of their wide range of applications, LNP structure has not been extensively investigated. By gaining a better understanding of LNP structure, LNP function can be further improved, leading to more sophisticated systems and more efficacious formulations. Therefore, the Cullis group in the University of British Columbia (UBC), in collaboration with Precision Nanosystems and AlCana Technologies (now Acuitas Therapeutics) studied the LNP structure and came up with a model that is anticipated to be followed by LNPs with similar components. The particles described here were formulated using the NanoAssemblrTMBenchtop instrument and consist of cationic lipids, helper lipids (ie. phosphatidyl choline), cholesterol, polyethylene glycol (PEG) and siRNA.
The researchers first confirmed siRNA encapsulation efficiency over a wide range of siRNA/lipid charge ratios. The negatively charged siRNA is held in the LNP by its electrostatic interaction with the cationic lipids. Nearly complete siRNA encapsulation was achieved with siRNA/cationic charge ratios as high as 1.25. This result indicates superiority of the microfluidic mixing over other conventional methods of siRNA LNP formulation which only achieved a maximum of 70% encapsulation efficiency. The LNP were then imaged by cryo-electron microscopy where they exhibited an electron dense core structure contrary to bilayer aqueous core structure of liposomes composed of phosphatidyl choline and cholesterol. Additionally, in the absence of siRNA, the LNP still formed the electron dense core structure suggesting that the cationic lipid contributes to this feature even in the absence of siRNA. Additionally, the researchers studied the mobility of the siRNA inside the LNP structure with 31 P NMR and found that encapsulated siRNA is immobilized in the system. Interestingly, incubation of siRNA LNPs with RNAse enzymes did not lead to RNA degradation suggesting the siRNA is fully protected inside the LNP. Not surprisingly, further analysis by fluorescence resonance energy transfer (FRET) indicated that siRNA and cationic lipids form internal complexes in the LNP. This explains the almost full encapsulation of siRNA. If the cationic lipid and thus siRNA are distributed equally across the surface and interior, half the siRNA should be lost during dialysis. However, the siRNA is completely retained by the LNP during dialysis, suggesting it is buried inside the LNP along with the cationic lipid forming the internal complexes. Also, LNPs were denser than the phosphatidyl choline/cholesterol liposomes when separated via ultracentrifugation. Together, these data substantiate the understanding that LNPs have an electron-dense nanostructured core as opposed to a bilayer with aqueous core. To further confirm the hypothesized structure, the authors then modeled the LNP structure by performing computational simulations. The model consistently supported the experimental results, pointing to a structured core consisting of siRNA and cationic lipid complexes with a surface covered by PEG-lipids.
This paper is the first to study the detailed structure of LNP using both experimental data and computer structure modeling. The results in this study explain the exceptionally high encapsulation efficiency and retention of siRNA by microfluidic formulated LNP. Additionally, LNP function and potency can be more confidently explained by knowing the mechanism of formation. An improved understanding of LNP structure can lead to improved LNP systems with an even wider range of applications.
Lipid nanoparticles (LNP) containing ionizable cationic lipids are the leading systems for enabling therapeutic applications of siRNA; however, the structure of these systems has not been defined. Here we examine the structure of LNP siRNA systems containing DLinKC2-DMA(an ionizable cationic lipid), phospholipid, cholesterol and a polyethylene glycol (PEG) lipid formed using a rapid microfluidic mixing process. Techniques employed include cryo-transmission electron microscopy, 31P NMR, membrane fusion assays, density measurements, and molecular modeling. The experimental results indicate that these LNP siRNA systems have an interior lipid core containing siRNA duplexes complexed to cationic lipid and that the interior core also contains phospholipid and cholesterol. Consistent with experimental observations, molecular modeling calculations indicate that the interior of LNP siRNA systems exhibits a periodic structure of aqueous compartments, where some compartments contain siRNA. It is concluded that LNP siRNA systems formulated by rapid mixing of an ethanol solution of lipid with an aqueous medium containing siRNA exhibit a nanostructured core. The results give insight into the mechanism whereby LNP siRNA systems are formed, providing an understanding of the high encapsulation efficiencies that can be achieved and information on methods of constructing more sophisticated LNP systems.
Publication - Abstract
Dec 18, 2018
Publication - Abstract
Mar 01, 2014
The ability of leptin to improve metabolic abnormalities in models of leptin deficiency, lipodystrophy, and even type 1 diabetes is of significant interest. However, the mechanism by which leptin mediates these effects remains ill-defined.