Structure-Based Modeling of Environment-Dependent Protonation States Across LNP Formulations with Atomistic CpHMD

Area of Science:

• Biochemistry
• Computational Chemistry
• Materials Science

Background:

• Ionizable lipid pKa values and protonation states in lipid nanoparticles (LNPs) are highly sensitive to their chemical surroundings.
• This environmental dependence complicates structure-function relationships, impacting payload delivery, tissue targeting, and manufacturing processes.
• Current experimental and computational methods lack the spatial resolution to accurately capture these heterogeneous charge distributions within LNPs.

Purpose of the Study:

• To develop and validate a scalable computational model for predicting local charge distributions of ionizable lipids within LNPs.
• To investigate the environment-dependent pKa values and protonation states of ionizable lipids in various LNP formulations.
• To provide a structure-based computational tool for understanding LNP behavior during manufacturing and delivery.

Main Methods:

• A continuous constant pH molecular dynamics (Cp-HMD) model was employed to simulate LNP self-assembly.
• Ionizable lipid parameters were derived from Hamiltonian replica exchange (HREX) calculations for improved conformational sampling.
• Simulated systems comprised ionizable lipids, cholesterol, DSPC, and mRNA, mimicking the LNP interior, and were analyzed at various pH values and integrated with bilayer models.

Main Results:

• The Cp-HMD model successfully simulated LNP self-assembly, revealing pH-dependent structural changes.
• Theoretically calculated apparent pKa values from the model showed good agreement with experimental data (MAE = 0.32 pKa units, R² = 0.52).
• The study demonstrated the capability to predict environment-dependent pKa values and heterogeneous charge distributions in LNPs.

Conclusions:

• This work presents a novel computational platform for predicting ionizable lipid pKa values in diverse chemical environments.
• The developed model enables structure-based simulation of heterogeneous charge distributions in LNPs.
• This technology can advance the understanding and design of LNPs for improved drug delivery and manufacturing.