在LNP配方中以结构为基础对环境依赖的突起状态进行建模,采用原子化的CpHMD
Kyle J Colston1, Santiago C Monsalve1, Severin T Schneebeli1,2
1Department of Industrial and Molecular Pharmaceutics, Purdue University, West Lafayette, Indiana 47907, United States.
Molecular pharmaceutics
|October 31, 2025
概括
这项研究引入了一种新的计算模型,以预测脂质纳米粒子 (LNP) 中的电荷如何随着环境变化而变化. 这有助于理解LNP的功能,以改善药物输送.
科学领域:
- 生物化学 生物化学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 脂质纳米粒子 (LNP) 中的可电离脂质pKa值和质子化状态对它们的化学环境高度敏感.
- 这种环境依赖性使结构功能关系复杂化,影响有效载荷传递,组织向和制造过程.
- 当前的实验和计算方法缺乏空间分辨率,无法准确地捕捉这些在LNP中的异质电荷分布.
研究的目的:
- 开发和验证一个可扩展的计算模型,用于预测LNP内可电离脂质的局部电荷分布.
- 研究各种LNP配方中的可离子化脂质的环境依赖的pKa值和质子化状态.
- 为了解制造和交付期间的LNP行为提供基于结构的计算工具.
主要方法:
- 使用一个连续恒定的pH分子动力学 (Cp-HMD) 模型来模拟LNP自组装.
- 电离性脂质参数是从哈密尔顿复制品交换 (HREX) 计算中得出的,以改进构造性采样.
- 模拟系统包括可电离脂质,胆固醇,DSPC和mRNA,模仿LNP内部,并在各种pH值下进行分析,并与双层模型集成.
主要成果:
- Cp-HMD模型成功模拟了LNP自组装,揭示了pH依赖的结构变化.
- 从模型中理论计算的明显pKa值与实验数据有很好的一致性 (MAE = 0.32 pKa单位,R2 = 0.52).
- 该研究证明了预测环境依赖的pKa值和LNP中异质电荷分布的能力.
结论:
- 这项工作介绍了一种新的计算平台,用于预测各种化学环境中的可电离性脂质pKa值.
- 开发的模型允许基于结构的模拟LNP中异质的电荷分布.
- 这项技术可以促进LNP的理解和设计,以改善药物输送和制造.
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