Charge variants, especially acidic charge variants, of recombinant monoclonal antibodies are the major critical quality attributes in the biotechnology industry due to their potential influence on stability and biological activity. The chemical properties of the acidic charge variants have been challenging to fully characterize, and it is critical for process development and optimization. To completely understand the multiple sources of acidic charge variants, the major charge forms of an IgG1 monoclonal antibody were firstly isolated and then analyzed by a battery of characterization tools. It was found that various degrees of disulfide bond reduction, the deamination of HC-T8 Asn84 and HC-T35 Asn388 and aggregation account for the majority of acidic charge heterogeneity and the terminal galactosylation content was in relation to the acidic charge heterogeneity. The correlation between acidic charge heterogeneity and galactosylation content was further explored by weak cation exchange chromatography with the use of β1-4 galactosidase digestion. The results showed that galactosylation was not the source of the acidic charge variants per se. Meanwhile, to gain insights into the impact on binding affinity of monoclonal antibody to IgE and FcRn, charge variants were also analyzed by competitive ELISA and surface plasmon resonance, respectively. All isolated charge variants had similar affinity binding to IgE and FcRn binding relative to the starting material.

As the composition of animal cell culture medium becomes more complex, the identification of key variables is important for simplifying and guiding the subsequent medium optimization. However, the traditional experimental design methods are impractical and limited in their ability to explore such large feature spaces. Therefore, in this work, we developed a NRGK (nonparametric regression with Gaussian kernel) method, which aimed to identify the critical components that affect product titres during the development of cell culture media. With this nonparametric model, we successfully identified the important components that were neglected by the conventional PLS (partial least squares regression) method. The superiority of the NRGK method was further verified by ANOVA (analysis of variance). Additionally, it was proven that the selection accuracy was increased with the NRGK method because of its ability to model both the nonlinear and linear relationships between the medium components and titres. The application of this NRGK method provides new perspectives for the more precise identification of the critical components that further enable the optimization of media in a shorter timeframe.