Solution biophysics identifies lipid nanoparticle non-sphericity, polydispersity, and dependence on internal ordering for efficacious mRNA delivery DOI Creative Commons
Marshall S. Padilla, Sarah J. Shepherd, Andrew R. Hanna

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 22, 2024

Abstract Lipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated since success of Patisiran, first siRNA-LNP therapeutic, and mRNA vaccines that emerged during COVID-19 pandemic. Designing LNPs with specific targeting, high potency, minimal side effects is crucial their successful clinical use. These characteristics have been improved through microfluidic platforms, which enhanced efficacy uniformity LNP batches. However, our understanding how composition mixing method influences structural, biophysical, biological properties resulting particles remains limited, hindering LNPs. Our lack structural extends from physical compositional polydispersity LNPs, render traditional characterization methods, such as dynamic light scattering (DLS), unable to accurately quantitate physicochemical In this study, we address challenge structurally characterizing polydisperse formulations using emerging solution-based biophysical methods higher resolution provide data beyond size polydispersity. techniques include sedimentation velocity analytical ultracentrifugation (SV-AUC), field flow fractionation followed by multi-angle (FFF-MALS), size-exclusion chromatography in-line synchrotron small-angle X-ray (SEC-SAXS). Here, show intrinsic in size, loading, shape, these parameters dependent on both formulation technique lipid composition. Lastly, demonstrate can be employed predict transfection three models examining relationship between translation characteristics. We envision employing will essential determining structure-function relationships, facilitating creation new design rules future

Language: Английский

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery DOI Creative Commons
Markéta Paloncýová, Mariana Valério, Ricardo Nascimento dos Santos

et al.

Molecular Pharmaceutics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design LNCs, detailed understanding composition-structure-function relationships is missing. This review presents currently available computational methods LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, focus on molecular dynamics simulations all-atom coarse-grained resolution. Their strengths weaknesses discussed, highlighting aspects necessary obtaining reliable results simulations. Furthermore, machine learning, i.e., data-based approach to lipid-mediated introduced. data produced by experimental theoretical provide valuable insights. Processing these help optimize LNCs better performance. In final section this Review, computer reviewed, specifically addressing compatibility

Language: Английский

Citations

2
Investigating the stability of RNA-lipid nanoparticles in biological fluids: Unveiling its crucial role for understanding LNP performance DOI Creative Commons
Heyang Zhang, Matthias Barz

Journal of Controlled Release, Journal Year: 2025, Volume and Issue: 381, P. 113559 - 113559

Published: Feb. 27, 2025

Language: Английский

Citations

1
Physicochemical and structural insights into lyophilized mRNA-LNP from lyoprotectant and buffer screenings DOI
Yuchen Fan, Diamanda Rigas, Lee Joon Kim

et al.

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 373, P. 727 - 737

Published: Aug. 2, 2024

Language: Английский

Citations

7
Review of Machine Learning for Lipid Nanoparticle Formulation and Process Development DOI
Phillip J. Dorsey, Christina L Lau,

Ti‐chiun Chang

et al.

Journal of Pharmaceutical Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

Citations

4
Thin-film freeze-drying of an influenza virus hemagglutinin mRNA vaccine in unilamellar lipid nanoparticles with blebs DOI
Qin Li,

Ruiqi Shi,

Haiyue Xu

et al.

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 375, P. 829 - 838

Published: Oct. 10, 2024

Language: Английский

Citations

1
Solution biophysics identifies lipid nanoparticle non-sphericity, polydispersity, and dependence on internal ordering for efficacious mRNA delivery DOI Creative Commons
Marshall S. Padilla, Sarah J. Shepherd, Andrew R. Hanna

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 22, 2024

Abstract Lipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated since success of Patisiran, first siRNA-LNP therapeutic, and mRNA vaccines that emerged during COVID-19 pandemic. Designing LNPs with specific targeting, high potency, minimal side effects is crucial their successful clinical use. These characteristics have been improved through microfluidic platforms, which enhanced efficacy uniformity LNP batches. However, our understanding how composition mixing method influences structural, biophysical, biological properties resulting particles remains limited, hindering LNPs. Our lack structural extends from physical compositional polydispersity LNPs, render traditional characterization methods, such as dynamic light scattering (DLS), unable to accurately quantitate physicochemical In this study, we address challenge structurally characterizing polydisperse formulations using emerging solution-based biophysical methods higher resolution provide data beyond size polydispersity. techniques include sedimentation velocity analytical ultracentrifugation (SV-AUC), field flow fractionation followed by multi-angle (FFF-MALS), size-exclusion chromatography in-line synchrotron small-angle X-ray (SEC-SAXS). Here, show intrinsic in size, loading, shape, these parameters dependent on both formulation technique lipid composition. Lastly, demonstrate can be employed predict transfection three models examining relationship between translation characteristics. We envision employing will essential determining structure-function relationships, facilitating creation new design rules future

Language: Английский

Citations

0