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Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

ABSTRACT: Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R2 > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future. Graphical abstract The AI algorithm was used to find the relationship between ionizable lipids and in vivo efficiencies of mRNA vaccines, which was validated by a mice study. The molecular dynamic simulation further provided mechanical details of lipid nanoparticle (LNP) formulation.Image 1

SUBMITTER: Wang W

PROVIDER: S-EPMC9214321 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Project description:Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains a challenge. This study investigates modified lipid nanoparticles (LNP) for mRNA delivery to hyperactivated microglia, particularly those with pro-inflammatory characteristics, utilizing supervised machine learning (ML) classifiers. We developed and screened a library of 216 LNP formulations with varying lipid compositions, N/P ratios, and hyaluronic acid (HA) modifications. The transfection efficiency of eGFP mRNA was assessed in the BV-2 murine microglia cell line under different immunological states, including resting and activated conditions (LPS-activated and IL4/IL13-activated). ML-guided morphometric analysis tracked the phenotypes of various microglia subtypes before and after transfection. Four supervised ML classifiers were investigated to predict transfection efficiency and phenotypic changes based on LNP design parameters. The Multi-Layer Perceptron (MLP) neural network emerged as the best-performing model, achieving weighted F1-scores ≥0.8. While it accurately predicted responses from LPS-activated and resting cells, it struggled with IL4/IL13-activated cells. The MLP model was validated by predicting the performance of four unseen LNP formulations delivering eGFP mRNA to LPS-activated BV2 cells. HA-LNP2 emerged as optimal formulation for delivering target IL10 mRNA, effectively suppressing inflammatory phenotypes, evidenced by shifts in cell morphology, increased IL10 expression, and reduced TNF-α levels. We also evaluated HA-LNP2 on LPS-activated human iPSC-derived microglia, confirming its efficacy in modulating inflammatory responses. This study highlights the potential of tailored LNP design and ML techniques to enhance mRNA therapy for neuroinflammatory disorders by leveraging carrier's immunogenic properties to modulate microglial responses.

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Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

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