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Mixing lipids to manipulate the ionization status of lipid nanoparticles for specific tissue targeting.

ABSTRACT: Introduction:The development of targeted drug delivery systems is a rapidly growing area in the field of nanomedicine. Methods:We report herein on optimizing the targeting efficiency of a lipid nanoparticle (LNP) by manipulating the acid dissociation constant (pKa) value of its membrane, which reflects its ionization status. Instead of changing the chemical structure of the lipids to achieve this, we used a mixture of two types of pH-sensitive cationic lipids that show different pKa values in a single LNP. We mixed various ratios of YSK05 and YSK12-C4 lipids, which have pKa values of 6.50 and 8.00, respectively, in one formulation (referred to as YSK05/12-LNP). Results:The pKa of the YSK05/12-LNP was dependent not only on the molar ratio of each lipid but also on the individual contribution of each lipid to the final pKa (the YSK12-C4 lipid showed a higher contribution). Furthermore, we succeeded in targeting and delivering short interfering RNA to liver sinusoidal endothelial cells using one of the YSK05/12-LNPs which showed an optimum pKa value of 7.15 and an appropriate ionization status (~36% cationic charge) to permit the particles to be taken up by liver sinusoidal endothelial cells. Conclusion:This strategy has the potential for preparing custom LNPs with endless varieties of structures and final pKa values, and would have poten tial applications in drug delivery and ionic-based tissue targeting.

SUBMITTER: Shobaki N

PROVIDER: S-EPMC6294068 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Mixing lipids to manipulate the ionization status of lipid nanoparticles for specific tissue targeting.

Shobaki Nour N Sato Yusuke Y Harashima Hideyoshi H

International journal of nanomedicine 20181210

<h4>Introduction</h4>The development of targeted drug delivery systems is a rapidly growing area in the field of nanomedicine.<h4>Methods</h4>We report herein on optimizing the targeting efficiency of a lipid nanoparticle (LNP) by manipulating the acid dissociation constant (<i>pKa</i>) value of its membrane, which reflects its ionization status. Instead of changing the chemical structure of the lipids to achieve this, we used a mixture of two types of pH-sensitive cationic lipids that show diff ...[more]

PMID: 30587967

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Project description:A lipid nanoparticle (LNP) formulation is a state-of-the-art delivery system for genetic drugs such as DNA, mRNA, and siRNA, which is successfully applied to COVID-19 vaccines and gains tremendous interest in therapeutic applications. Despite its importance, a molecular-level understanding of the LNP structures and dynamics is still lacking, which makes a rational LNP design almost impossible. In this work, we present an extension of CHARMM-GUI Membrane Builder to model and simulate all-atom LNPs with various (ionizable) cationic lipids and PEGylated lipids (PEG-lipids). These new lipid types can be mixed with any existing lipid types with or without a biomolecule of interest, and the generated systems can be simulated using various molecular dynamics engines. As a first illustration, we considered model LNP membranes with DLin-KC2-DMA (KC2) or DLin-MC3-DMA (MC3) without PEG-lipids. The results from these model membranes are consistent with those from the two previous studies albeit with mild accumulation of neutral MC3 in the bilayer center. To demonstrate Membrane Builder â€™s capability of building a realistic LNP patch, we generated KC2- or MC3-containing LNP membranes with high concentrations of cholesterol and ionizable cationic lipids together with 2 mol% PEG-lipids. We observe that PEG-chains are flexible, which can be more preferentially extended laterally in the presence of cationic lipids due to the attractive interactions between their head groups and PEG oxygen. The presence of PEG-lipids also relaxes the lateral packing in LNP membranes, and the area compressibility modulus ( KA ) of LNP membranes with cationic lipids fit into typical KA of fluid-phase membranes. Interestingly, the interactions between PEG oxygen and head group of ionizable cationic lipids induce a negative curvature. We hope that this LNP capability in Membrane Builder can be useful to better characterize various LNPs with or without genetic drugs for a rational LNP design.

Project description:A lipid nanoparticle (LNP) formulation is a state-of-the-art delivery system for genetic drugs such as DNA, messenger RNA, and small interfering RNA, which is successfully applied to COVID-19 vaccines and gains tremendous interest in therapeutic applications. Despite its importance, a molecular-level understanding of the LNP structures and dynamics is still lacking, which makes rational LNP design almost impossible. In this work, we present an extension of CHARMM-GUI Membrane Builder to model and simulate all-atom LNPs with various (ionizable) cationic lipids and PEGylated lipids (PEG-lipids). These new lipid types can be mixed with any existing lipid types with or without a biomolecule of interest, and the generated systems can be simulated using various molecular dynamics engines. As a first illustration, we considered model LNP membranes with DLin-KC2-DMA (KC2) or DLin-MC3-DMA (MC3) without PEG-lipids. The results from these model membranes are consistent with those from the two previous studies, albeit with mild accumulation of neutral MC3 in the bilayer center. To demonstrate Membrane Builder's capability of building a realistic LNP patch, we generated KC2- or MC3-containing LNP membranes with high concentrations of cholesterol and ionizable cationic lipids together with 2 mol % PEG-lipids. We observe that PEG-chains are flexible, which can be more preferentially extended laterally in the presence of cationic lipids due to the attractive interactions between their head groups and PEG oxygen. The presence of PEG-lipids also relaxes the lateral packing in LNP membranes, and the area compressibility modulus (KA) of LNP membranes with cationic lipids fit into typical KA of fluid-phase membranes. Interestingly, the interactions between PEG oxygen and the head group of ionizable cationic lipids induce a negative curvature. We hope that this LNP capability in Membrane Builder can be useful to better characterize various LNPs with or without genetic drugs for rational LNP design.

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Mixing lipids to manipulate the ionization status of lipid nanoparticles for specific tissue targeting.

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Mixing lipids to manipulate the ionization status of lipid nanoparticles for specific tissue targeting.

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