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Abstract
Background Lipid Nanoparticles (LNP) are novel vehicles for the delivery of Small and Nucleic acid therapies (gene/mRNA/siRNA) and were vital for the success of COVID-19 mRNA vaccines.1 2 LNPs consist of 1) Ionizable lipid, 2) Helper lipid, 3) Cholesterol, and 4) PEG-lipid3 and their composition affects the observed pharmacokinetic characteristics and efficacy. In addition, physio-chemical properties like the size and charge can be adjusted to improve their distribution.4–7 Some of the major challenges for LNP based programs are 1) Understanding barriers for drug availability at the tissue of interest and how to optimize the formulation to increase the bioavailability? 2) Are animal models good systems for studying bioavailability? 3) How to derive initial human dose from animal models?
The objective of this work is to incorporate the various aspects of LNPs affecting its PK into a mechanistic model to address above challenges with focus on systemic delivery (i.v.).
Methods The proposed mPBPK captures physical properties of LNPs and their impact on 1. Systemic distribution, 2. Availability at site of action 3. Cellular uptake, and 4. Intracellular drug release (figure 1).8–11 The model application is shown through two use cases. In case study 1, we used published preclinical data7 12 from the approved LNP-siRNA Onpattro to predict FIH dosing and validate with the clinical data.13 In case study 2, we predicted the optimal formulation for a hypothetical LNP-mRNA given via i.v. administration to maximize availability in the lung based on published data in mice.14
Results The mechanistic model reported here can predict kinetics of LNPs based on their physio-chemical properties. In case study 1, our predictions for FIH closely matched with the reported doses for Onpattro. In case study 2, we predicted the optimal formulation for a hypothetical LNP-mRNA given intravenously to maximize the bioavailability in Lung. Furthermore, we simulate different dosing strategies and their effect on drug efficacy. In both these cases we discuss modeling uncertainties/knowledge gaps.
Conclusions LNP design optimization is vital for improving the efficacy of nucleic acid therapies and was discussed in the literature.10 15 The proposed model has the potential to support the design of novel LNP based mRNA therapeutics. For e.g. the hypothetical case study 2 presented here is relevant for the treatment of Cystic Fibrosis. Furthermore, the model can be minimally tweaked to capture different administration routes (i.m, s.c. intranasal), different sites of action, and different drugs (mRNA/siRNA).
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Proposed mechanistic model schematic for LNP distribution
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