Multiscale modeling guided potency assessment of mRNA-lipid nanoparticles

Area of Science:

• Biotechnology
• Pharmacology
• Computational Biology

Background:

• mRNA lipid nanoparticle (mRNA-LNP) technology is crucial for vaccine development, offering high efficiency and safety.
• Assessing mRNA-LNP potency is challenging due to limited mechanistic understanding and data.

Purpose of the Study:

• To introduce a mechanism-informed, multi-scale kinetic modularized modeling framework for quantitative assessment of mRNA delivery dynamics.
• To address gaps in understanding LNP-cell interactions and their impact on delivery efficiency and response heterogeneity.

Main Methods:

• Developed a multi-scale kinetic modularized modeling framework capturing nanoparticle, cellular, and macroscopic scales.
• Incorporated variability in LNP-cell interactions, dosage, size distributions, cell proliferation, and membrane properties.
• Utilized multi-omics assays, including single-molecule fluorescent in situ hybridization (smFISH), for single-cell resolution of mRNA and protein expression.

Main Results:

• The framework quantitatively captures coupled mRNA delivery dynamics across scales.
• It integrates critical determinants influencing delivery efficiency and response heterogeneity.
• Demonstrated mechanistically grounded modeling and robust prediction of therapeutic potency.

Conclusions:

• The developed framework provides a powerful platform for optimizing mRNA-based interventions.
• Its cell-based architecture and modular design offer adaptability to diverse delivery systems and physiological contexts.
• Enables more reliable and rapid potency assessment for mRNA vaccines and therapeutics.