The development of sensitive techniques to detect sequence variants (SVs), which naturally arise due to DNA mutations and errors in transcription/translation (amino acid misincorporations), has resulted in increased attention to their potential presence in protein-based biologic drugs in recent years. Often, these SVs may be below 0.1%, adding challenges for consistent and accurate detection. Furthermore, the presence of false-positive (FP) signals, a hallmark of SV analysis, requires time-consuming analyst inspection of the data to sort true from erroneous signal. Consequently, gaps in information about the prevalence, type, and impact of SVs in marketed and in-development products are significant. Here, we report the results of a simple, straightforward, and sensitive approach to sequence variant analysis. This strategy employs mixing of two samples of an antibody or protein with the same amino acid sequence in a dilution series followed by subsequent sequence variant analysis. Using automated peptide map analysis software, a quantitative assessment of the levels of SVs in each sample can be made based on the signal derived from the mass spectrometric data. We used this strategy to rapidly detect differences in sequence variants in a monoclonal antibody after a change in process scale, and in a comparison of three mAbs as part of a biosimilar program. This approach is powerful, as true signals can be readily distinguished from FP signal, even at a level well below 0.1%, by using a simple linear regression analysis across the data set with none to minimal inspection of the MS/MS data. Additionally, the data produced from these studies can also be used to make a quantitative assessment of relative levels of product quality attributes. The information provided here extends the published knowledge about SVs and provides context for the discussion around the potential impact of these SVs on product heterogeneity and immunogenicity.
