Table 4.

Machine learning/Data-driven optimization of culture parameters influencing charge variant.

Cell line	Parameter	Media	Product	Algorithm	Charge Variant	Ref.
CHO-GS	Cobalt, Nickel, Magnesium, Manganese, Zinc, Iron, Copper	CD CHO®	IgG1 (Trastuzumab)	Random forest regression, linear regression, lasso regression, decision tree regression, extra tree regression, ridge regression, lasso least regression, Bayesian ridge, catboost regression, Huber regression, extreme gradient boosting, gradient boosting regression, elastic net, support vector regression, and k-nearest regression	Iron reduces acidic variants; Zinc increases basic variants.	(Gangwar et al.,29)
CHO-GS	Glucose, Lactate, pH, Dissolved Oxygen, Temperature, Ammonia	CD CHO®	IgG1	CNN, LSTM, Bayesian Optimization, Random Forest, K-nearest Neighbor	39.5% reduction in total acidic and basic variant levels	(Lu et al.,97)
CHO-S and CHO-GS	pH, temperature, initial seeding density	Serum-free commercial medium	IgG1, IgG4, (IgG+IgM)	Statistical Modeling, PCA, Transfer Learning, Active Learning	Higher pH increases Acidic Variants and decreases Basic Variants; Temperature shifts also inversely affect charge variants.	(Kumar et al.,111)