BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University, Gyeonggi 10326, Republic of Korea.
CKD Pharm Corp., Hyo-Jong Research Institute, Gyeonggi 16995, Republic of Korea.
Int J Pharm. 2023 Jun 10;640:123012. doi: 10.1016/j.ijpharm.2023.123012. Epub 2023 May 2.
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 = 0.7269-0.9946), while XGBoost demonstrated better RASE (0.2833-2.9817). The ANN-DOE model outperformed both optimized ML models by R = 1.21 % and RASE = 43.51 % (PS prediction), R = 0.23 % and RASE = 3.47 % (PDI prediction), R = 5.73 % and RASE = 27.95 % (ZP prediction), and R = 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.
为了开发组合人工神经网络实验设计（ANN-DOE）模型，使用确定筛选设计（DSD）和机器学习（ML）算法评估可离子化脂质、可离子化脂质与胆固醇的比例、N/P 比、流速比（FRR）和总流速（TFR）对 mRNA-LNP 疫苗结果的影响。在限定的约束条件（PS 40-100nm、PDI≤0.30、ZP≥(±)0.30mV、EE≥70%）内优化了 mRNA-LNP 的粒径（PS）、多分散指数（PDI）、Zeta 电位（ZP）和包封效率（EE），并将其输入到 ML 算法（XGBoost、自举森林、支持向量机、k-最近邻、广义回归-Lasso、ANN）中进行预测，并与 ANN-DOE 模型进行比较。增加 FRR 会降低 PS 并增加 ZP，而增加 TFR 会增加 PDI 和 ZP。同样，DOTAP 和 DOTMA 产生更高的 ZP 和 EE。特别是，N/P 比≥6 的阳离子可离子化脂质提供了更高的 EE。ANN 表现出更好的预测能力（R=0.7269-0.9946），而 XGBoost 则表现出更好的 RASE（0.2833-2.9817）。ANN-DOE 模型在 PS 预测中通过 R=1.21%和 RASE=43.51%、PDI 预测中通过 R=0.23%和 RASE=3.47%、ZP 预测中通过 R=5.73%和 RASE=27.95%、EE 预测中通过 R=0.87%和 RASE=36.95%，分别优于这两个优化后的 ML 模型，这表明 ANN-DOE 模型在预测生物过程方面优于独立模型。
