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

TL;DR

The paper addresses the challenge of mechanistically modeling lipid nanoparticle (LNP) formation for nucleic-acid therapeutics by outlining a multiscale modeling framework that spans mixer-scale CFD, population balance models, phase-field descriptions, and meso-/molecular-scale methods. It presents a spectrum of approaches, from global mass/energy balances to Euler–Euler multiphase CFD, PBMs capturing nucleation, growth, agglomeration, and breakage, and phase-field models for interfacial evolution, all coupled with model-based design, control, and sensing strategies within a MBSE or QbD context. Key contributions include explicit formulations for transport, thermodynamics, and interfacial phenomena in LNP systems, guidance on integrating CFD with PBM and PFMs, and recommendations for validation, parameterization, and progressive model complexity. The work emphasizes multiscale coupling, experimental design, and real-time sensing to improve process understanding, scale-up, and control of LNP production for NATs, with practical implications for manufacturing reliability and product quality. Overall, the framework provides a roadmap for advancing predictive models that inform advanced process control, quality attributes, and regulatory-compliant development of LNP-based NATs, while acknowledging data gaps and the need for continued methodological advances and sensor-enabled validation.

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.

Figures (10)

• Figure 1: Exemplar mixer geometries for rapid mixing of organic and aqueous streams for LNP manufacturing. Various modifications, such as flow constrictions and baffles, can be made upstream and/or downstream of the mixing point. For many microfluidic systems, rectangular geometries are typically employed due to the mixer manufacturing process.
• Figure 2: Summary of important material, process, and product variables and attributes affecting LNP manufacturing and product quality.
• Figure 3: Simplified representation of LNP interface. The distribution of components along the interface is not representative of what might occur in a real system as it will depend strongly on the formulation.
• Figure 4: Summary of modeling strategies available to characterize LNP manufacturing sorted based on depth of physical/process insights gained and normalized computational complexity (i.e., computational cost incurred to resolve the same length- and time-scale).
• Figure 5: Schematic of LNP mixer with two feed streams and two product streams as a result of LNP precipitation.