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

Abstract

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 (R2 = 0.7269–0.9946), while XGBoost demonstrated better RASE (0.2833–2.9817). The ANN-DOE model outperformed both optimized ML models by R2 = 1.21 % and RASE = 43.51 % (PS prediction), R2 = 0.23 % and RASE = 3.47 % (PDI prediction), R2 = 5.73 % and RASE = 27.95 % (ZP prediction), and R2 = 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.