Presented at CASSS-AT Europe 2019, Dublin, Ireland
The presence of sequence variants (SVs) can adversely affect the safety and efficacy of protein therapeutics, and therefore an analytical method that enables sensitive detection of SVs would represent a significant step toward ensuring product quality. Mass spectrometric methods enable identification of SVs, but the minimization of false positives remains a challenge during data analysis. We present an automated processing workflow for LC-MS/MS data that provides comprehensive characterization and sensitive quantification of sequence variants and describe an effective strategy to minimize the number of false positive and false negative identifications.
Peptide mapping was performed using the NIST monoclonal antibody (mAb). Raw LC-MS/MS data was analyzed using the dedicated software platform Genedata Expressionist® (Genedata AG, Basel, Switzerland). In our approach, MS and MS/MS signals are first consolidated across multiple sampling points, charge states, and samples. This significantly increases the quality of data for subsequent identification and quantification, and provides improved characterization of SVs down to low-ppm levels. Second, a stepwise assignment and subsequent exclusion of identified signals is performed on the consolidated data. Compared to traditional methods relying solely on the efficacy of search engines, this approach radically reduces the number of false positive identifications.