State-of-the-Art Design and Rapid-Mixing Production Techniques of Lipid Nanoparticles for Nucleic Acid Delivery
Evers Mjw, Kulkarni Ja, Van Der Meel R, Cullis Pr, Vader P, Schiffelers RmDOI 10.1002/smtd.201700375
Summary
Free siRNA, mRNA, pDNA, and CRISPR/Cas9 components are rapidly degraded and cleared, so delivery systems are required. LNPs are the most clinically advanced non-viral siRNA carriers; Patisiran reached Phase III and market application in late 2017. The field needed a clearer understanding of LNP design parameters and scalable rapid-mixing production methods. DLin-MC3-DMA has apparent pKa 6.44; LNP formulation at 40/10/40/10 mol% had ED50 0.03 mg/kg, while 50/10/38.5/1.5 mol% had ED50 0.005 mg/kg in mice. - cKK-E12 LNP had ED50 0.002 mg/kg; YSK13-C3 LNP had ED50 0.015 mg/kg.
Purpose: Free siRNA, mRNA, pDNA, and CRISPR/Cas9 components are rapidly degraded and cleared, so delivery systems are required. LNPs are the most clinically advanced non-viral siRNA carriers; Patisiran reached Phase III and market application in late 2017. The field needed a clearer understanding of LNP design parameters and scalable rapid-mixing production methods.
Hypothesis: If LNPs are formulated with optimized ionizable amino-lipids, helper lipids, cholesterol, and sheddable PEG-lipids, with controlled pKa, N/P ratio, size, and surface properties, and are produced by rapid-mixing methods, then they can efficiently encapsulate and deliver siRNA, mRNA, pDNA, or CRISPR/Cas9 components, protect nucleic acids in circulation, promote endosomal escape, and produce gene silencing, protein expression, or genome editing.
Aims: Describe LNP structure, lipid composition, and physicochemical properties affecting pharmacokinetics and gene silencing. - Review evolution of LNP production from conventional methods to rapid-mixing techniques. - Compare T-junction mixing, microfluidic hydrodynamic focusing (MHF), and staggered herringbone mixing (SHM). - Highlight LNP applications for siRNA, mRNA, pDNA, and CRISPR/Cas9 delivery. - Discuss design-of-experiment (DoE) approaches for formulation optimization.
Delivery system: Platform: lipid nanoparticles (LNPs), distinct from traditional liposomes; often electron-dense core with inverted micellar structures. - Core lipids: ionizable amino-lipid (e.g., DLin-MC3-DMA, DLin-KC2-DMA, DLin-DMA, C12-200, cKK-E12, YSK13-C3); helper lipid (DSPC or DOPE); cholesterol; PEG-lipid (e.g., PEG-DMG, PEG-C14/C16/C18). - Payloads: siRNA, mRNA, pDNA, CRISPR/Cas9 mRNA, sgRNA, and sgRNA-protein complexes. - Targeting/uptake: ApoE adsorption mediates hepatocyte uptake via LDL receptor; PEG shedding controls ApoE exposure; extrahepatic targeting remains challenging. - Production: thin-film hydration/extrusion, sonication, homogenization, ethanol injection, T-junction mixing, MHF, SHM.
Approach: Review and synthesis of preclinical and clinical literature. Models include murine Factor VII silencing, EPO mRNA expression, cancer immunotherapy, Zika/HIV vaccines, and CRISPR/Cas9 editing in mice. Clinical trials listed for LNPs/liposomes encapsulating siRNA, mRNA, pDNA, and antisense oligonucleotides. As a review, it reports no primary experimental groups, n values, doses, or controls.
Key methods: No primary methods. The review discusses data generated by cited studies using: - Dynamic light scattering (DLS), cryo-TEM, AF4/QELS for size, PDI, morphology. - Zeta potential, encapsulation efficiency, N/P ratio. - Factor VII silencing, EPO expression, CD8 T-cell responses. - ApoE knockout/rescue experiments. - CRISPR/Cas9 eGFP editing and tdTomato reporter activation. - Design-of-experiment (DoE) formulation optimization.
Key results: DLin-MC3-DMA has apparent pKa 6.44; LNP formulation at 40/10/40/10 mol% had ED50 0.03 mg/kg, while 50/10/38.5/1.5 mol% had ED50 0.005 mg/kg in mice. - cKK-E12 LNP had ED50 0.002 mg/kg; YSK13-C3 LNP had ED50 0.015 mg/kg. - PEG-lipid desorption rates: PEG-C14 45%/h, PEG-C16 1.3%/h, PEG-C18 0.2%/h. Within 4 h, ~55% of PEG-C14 LNPs accumulated in liver vs max 35% for C16 and 25% for C18. - SHM production: at flow rates >0.2 mL/min, LNPs were ~55 nm, PDI <0.1, encapsulation efficiency >95%. - Size-dependent silencing: 38–78 nm LNPs were most efficient; 117 nm particles were poor due to liver fenestrae (~100 nm); 27 nm particles were less stable. - MHF produced small liposomes ~20 nm with ~70% dsDNA encapsulation. - T-junction: particles 116 ± 54 nm with 74% encapsulation; single-step dilution reduced encapsulation to 17%; another T-junction formulation gave 140 nm with 82% encapsulation. - DoE-optimized mRNA LNP increased EPO expression ~7-fold over siRNA-optimized formulation. - mRNA cancer vaccine formulation B-11: cKK-E12/DOPE/cholesterol/PEG-C14/SLS = 10/15/40.5/2.5/16 mol%, size 152 nm, PDI 0.217, induced strong antigen-specific CD8+ T-cell response. - CRISPR: LNP-Cas9 mRNA + AAV sgRNA corrected 6.2 ± 1.0% of hepatocytes; biodegradable LLM LNPs reduced eGFP expression by 70% in vitro and 41% intratumorally.
Interpretation: The authors conclude that LNPs are a versatile platform for nucleic acid therapeutics. Key advances include ionizable amino-lipids, sheddable PEG coatings, and rapid-mixing production methods. Formulations must be optimized for each nucleic acid payload; siRNA-optimized LNPs are not directly interchangeable with mRNA, pDNA, or sgRNA systems. DoE approaches and continued lipid innovation are expected to improve potency and enable extrahepatic targeting.
Limitations: Review article; no primary data, effect sizes, n values, doses, or controls. - Clinical success is largely limited to hepatic targets via ApoE-mediated uptake; extrahepatic delivery remains difficult. - Rapid-mixing methods use organic solvents, have residual ethanol limits, limited lipid solubility, possible microchannel clogging, and require parallelization for scale-up. - Conventional methods are labor-intensive, poorly scalable, and may use chloroform/methanol. - PEGylation creates a “PEG dilemma”: stability vs ApoE adsorption and transfection. - Some LNP structure–function relationships remain poorly understood. - Formulations require payload-specific optimization; DoE is useful but not yet universally standardized.
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