Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach.

To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of mRNA-LNP vaccine were evaluated using a definitive screening design (DSD) and machine learning (ML) algorithms. Particle size (PS), PDI, zeta potential (ZP), and encapsulation efficiency (EE) of mRNA-LNP were optimized within a defined constraint (PS 40-100 nm, PDI ≤ 0.30, ZP≥(±)0.30 mV, EE ≥ 70 %), fed to ML algorithms (XGBoost, bootstrap forest, support vector machines, k-nearest neighbors, generalized regression-Lasso, ANN) and prediction was compared to ANN-DOE model. Increased FRR decreased the PS and increased ZP, while increased TFR increased PDI and ZP. Similarly, DOTAP and DOTMA produced higher ZP and EE. Particularly, a cationic ionizable lipid with an N/P ratio ≥ 6 provided a higher EE. ANN showed better predictive ability (R ²  = 0.7269-0.9946), while XGBoost demonstrated better RASE (0.2833-2.9817). The ANN-DOE model outperformed both optimized ML models by R ²  = 1.21 % and RASE = 43.51 % (PS prediction), R ²  = 0.23 % and RASE = 3.47 % (PDI prediction), R ²  = 5.73 % and RASE = 27.95 % (ZP prediction), and R ²  = 0.87 % and RASE = 36.95 % (EE prediction), respectively, which demonstrated that ANN-DOE model was superior in predicting the bioprocess compared to independent models.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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