The poster was presented at QSPC 2026
Authors: Veronika Musatova (1), Anastasiia Ivanova (1), Kseniia Kudriavtseva (1), Oleg Demin (2), Rajat Desikan (3)
Affiliation: (1) InSysBio CY; (2) InSysBio UK; (3) Clinical Pharmacology Pharmacometric Systems and Tools, Platform Sciences, GSK, UK
Background: Lipid nanoparticle (LNP)-mediated delivery of mRNA is a versatile platform for expressing encoded therapeutic proteins in vivo. However, rational optimisation of LNP-mRNA therapeutics remains challenging due to the multiscale nature of the underlying biological processes. Efficacy is governed by the interplay between formulation attributes (e.g., ionisable lipid composition and particle size) and RNA molecular characteristics, which influence systemic distribution, cellular uptake, intracellular processing, and translation into functional protein. These effects are difficult to extrapolate across experimental systems and species. To address this, we developed a Quantitative Systems Pharmacology (QSP) model incorporating mechanistic knowledge of LNP distribution/processing and protein synthesis across biological scales. The model integrates heterogeneous publicly available datasets and can generate predictions of the response of mRNA and protein concentration kinetics upon varying key model parameters reflecting tuneable biological handles.
Methods: A multi-compartment mechanistic QSP model was developed to describe the in vivo kinetics of both the LNP-encapsulated mRNA and its encoded protein. The model represents systemic LNP-mRNA disposition, cellular uptake, intracellular mRNA release and degradation, protein expression and turnover using ordinary differential equations. Intracellular mRNA degradation accounted for PARN-dependent poly(A) tail degradation. Model calibration was performed using mRNA and encoded protein kinetics data from preclinical and clinical datasets. Global sensitivity analysis (GSA) was conducted to identify the most influential parameters on systemic mRNA and protein exposure.
Results: The model captures different datasets representing both intravenous and intramuscular LNP-mRNA administration. GSA indicated a limited influence of poly(A) tail length on encoded protein kinetics. In contrast, parameters governing mRNA degradation at early intracellular stages of LNP processing showed a greater impact on protein exposure. The model revealed a separation between the determinants of mRNA and protein kinetics. Systemic mRNA exposure was regulated by parameters associated with cellular uptake and intracellular trafficking, including exocytosis, whereas protein kinetics were most sensitive tomRNA escape from endosomal compartments and mRNA-ribosome interaction efficiency.
Conclusion: These results suggest that intracellular processing has a greater impact on protein expression following LNP-mRNA delivery than RNA molecular features such as poly(A) tail length. The decoupling of physiological determinants of RNA and protein kinetics may need to be considered when designing LNP-mRNA optimisation strategies. The QSP model provides a quantitative, model-informed drug discovery and development (MID3) framework to prioritise formulation and design modifications that maximise therapeutic protein expression and efficacy.
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