Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics
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arXiv
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2024
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| author | Inguva, Pavan K. Mukherjee, Saikat Walker, Pierre J. Tenberg, Vico Devos, Cedric Shin, Sunkyu Wu, Yanchen Santra, Srimanta Wang, Jie Singh, Shalini Kanso, Mona A. Kim, Shin Hyuk Trout, Bernhardt L. Bazant, Martin Z. Myerson, Allan S. Braatz, Richard D. |
| author_facet | Inguva, Pavan K. Mukherjee, Saikat Walker, Pierre J. Tenberg, Vico Devos, Cedric Shin, Sunkyu Wu, Yanchen Santra, Srimanta Wang, Jie Singh, Shalini Kanso, Mona A. Kim, Shin Hyuk Trout, Bernhardt L. Bazant, Martin Z. Myerson, Allan S. Braatz, Richard D. |
| contents | Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines and have received much attention. While modern manufacturing processes which involve rapidly mixing an organic stream containing the lipids with an aqueous stream containing the nucleic acids are conceptually straightforward, detailed understanding of LNP formation and structure is still limited and scale-up can be challenging. Mathematical and computational methods are a promising avenue for deepening scientific understanding of the LNP formation process and facilitating improved process development and control. This article describes strategies for the mechanistic modeling of LNP formation, starting with strategies to estimate and predict important physicochemical properties of the various species such as diffusivities and solubilities. Subsequently, a framework is outlined for constructing mechanistic models of reactor- and particle-scale processes. Insights gained from the various models are mapped back to product quality attributes and process insights. Lastly, the use of the models to guide development of advanced process control and optimization strategies is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08577 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics Inguva, Pavan K. Mukherjee, Saikat Walker, Pierre J. Tenberg, Vico Devos, Cedric Shin, Sunkyu Wu, Yanchen Santra, Srimanta Wang, Jie Singh, Shalini Kanso, Mona A. Kim, Shin Hyuk Trout, Bernhardt L. Bazant, Martin Z. Myerson, Allan S. Braatz, Richard D. Soft Condensed Matter Computational Engineering, Finance, and Science Biological Physics Chemical Physics Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines and have received much attention. While modern manufacturing processes which involve rapidly mixing an organic stream containing the lipids with an aqueous stream containing the nucleic acids are conceptually straightforward, detailed understanding of LNP formation and structure is still limited and scale-up can be challenging. Mathematical and computational methods are a promising avenue for deepening scientific understanding of the LNP formation process and facilitating improved process development and control. This article describes strategies for the mechanistic modeling of LNP formation, starting with strategies to estimate and predict important physicochemical properties of the various species such as diffusivities and solubilities. Subsequently, a framework is outlined for constructing mechanistic models of reactor- and particle-scale processes. Insights gained from the various models are mapped back to product quality attributes and process insights. Lastly, the use of the models to guide development of advanced process control and optimization strategies is discussed. |
| title | Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics |
| topic | Soft Condensed Matter Computational Engineering, Finance, and Science Biological Physics Chemical Physics |
| url | https://arxiv.org/abs/2408.08577 |