脂质纳米颗粒形成用于核酸治疗药物递送的机理建模
Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics
AI总结：
本文提出通过机理建模方法深入理解脂质纳米颗粒（LNP）形成过程，涵盖物性预测、多尺度建模及工艺控制优化，以促进核酸递送平台的发展。
摘要：
核酸（如mRNA）已成为一种有前景的治疗模式，能够应对多种疾病。脂质纳米颗粒（LNPs）作为核酸的递送平台，已用于COVID-19疫苗并受到广泛关注。现代制造工艺涉及将含有脂质的有机流与含有核酸的水性流快速混合，虽然概念上简单明了，但对LNP形成和结构的详细理解仍然有限，且放大生产可能具有挑战性。数学和计算方法有望加深对LNP形成过程的科学理解，并促进工艺开发和控制的改进。本文描述了LNP形成机理建模的策略，首先介绍了估算和预测各种物种（如扩散系数和溶解度）重要物理化学性质的方法。随后，概述了构建反应器和颗粒尺度过程机理模型的框架。从各种模型中获得的见解被映射回产品质量属性和工艺见解。最后，讨论了利用这些模型指导先进过程控制和优化策略的开发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
