Manufacturing of biopharmaceuticals involves recombinant protein expression in host cells followed by extensive purification of the target protein. Yet, host cell proteins (HCPs) may persist in the final drug product, potentially reducing its quality with respect to safety and efficacy. Consequently, residual HCPs are closely monitored during downstream processing by techniques such as enzyme-linked immunosorbent assay (ELISA) or high-performance liquid chromatography combined with tandem mass spectrometry (HPLC-MS/MS). The latter is especially attractive as it provides information with respect to protein identities. Although the applied HPLC-MS/MS methodologies are frequently optimized with respect to HCP identification, acquired data is typically analyzed using standard settings. Here, we describe an improved strategy for evaluating HPLC-MS/MS data of HCP-derived peptides, involving probabilistic protein inference and peptide detection in the absence of fragment ion spectra. This data analysis workflow was applied to data obtained for drug products of various biotherapeutics upon protein A affinity depletion. The presented data evaluation strategy enabled in-depth comparative analysis of the HCP repertoires identified in drug products of the monoclonal antibodies rituximab and bevacizumab, as well as the fusion protein etanercept. In contrast to commonly applied ELISA strategies, the here presented workflow is process-independent and may be implemented into existing HPLC-MS/MS setups for drug product characterization and process development. Graphical abstract.
Keywords: Biopharmaceutical; Host cell proteins; Monoclonal antibodies; Probabilistic protein inference; Tandem mass spectrometry.