Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics

2024-08-16

arXiv.org

AI Summary

This study addresses the lack of mechanistic guidance and empirical quality control in lipid nanoparticle (LNP) manufacturing. We developed the first multiscale mechanistic model integrating molecular property prediction, reactor hydrodynamics, and nucleation/growth kinetics. Methodologically, the framework combines physicochemically grounded differential equation modeling, mass-transfer and phase-transition kinetic simulation, multiscale coupling, sensitivity analysis, and embedded closed-loop control. Our key contribution is the first interpretable, quantitative simulation of the entire LNP formation process, establishing causal, quantifiable mappings from process parameters to particle structure and critical quality attributes (CQAs). The model significantly enhances process reproducibility and scalability, enabling robust manufacturing of mRNA vaccines and other nucleic acid therapeutics, and provides a computationally rigorous scientific foundation for Quality-by-Design (QbD).

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Modeling lipid nanoparticle formation for nucleic acid delivery
• Understanding LNP structure and formation challenges
• Developing computational methods for process optimization

Innovation

• Mechanistic modeling of LNP formation process
• Predicting physicochemical properties like diffusivities and solubilities
• Framework for reactor- and particle-scale mechanistic models