Host cell proteins (HCPs) are the predominant class of impurities during manufacturing of therapeutic proteins. Previous reports have successfully shown that HCP characterization by LC–MS/MS ultimately leads to drug products of superior safety and quality. Here, we present two sample preparation strategies to approach the wide dynamic range required and compared them systematically to a standard protocol. First, we describe PreOmics fractionation as an effective 2D offline strategy. Second, we evaluate an alternative digestion approach specifically designed for purified antibodies – native (nondenaturing) digestion. Both protocols increased detection sensitivity as shown by two low level HCP models. Out of a 5 ppm spike of eight common HCPs into antibody product, all spiked proteins were positively identified. Additionally, by Universal Proteomics Standard 1 (UPS-1) spiking we obtained a comprehensive coverage of 77% below 10 ppm for the native digestion. Furthermore, we were able to detect 27% to 173% more HCPs in protein A elution pools of five different antibodies and to reject new concerns of HCP coprecipitation by pellet digestion. Although it encounters new challenges, the native digestion is very attractive due its simplicity and comparability to 2D workflows. However, for complex samples such as mock transfected cell culture fermentation, best results were obtained with peptide fractionation. This study highlights the advantages of both methods and their value to facilitate LC–MS/MS approaches to become an even more powerful tool for HCP profiling.

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Residual host cell proteins (HCPs) in the drug product can affect product quality, stability, and/or safety. In particular, highly active hydrolytic enzymes at sub-ppm levels can negatively impact the shelf life of drug products but are challenging to identify by liquid chromatography−mass spectrometry/mass spectrometry (LC−MS/MS) due to their high dynamic range between HCPs and biotherapeutic proteins. We employed new strategies to address the challenge: (1) native digest at a high protein concentration; (2) sodium deoxycholate added during the reduction step to minimize the inadvertent omission of HCPs observed with native digestion; and (3) solid phase extraction with 50% MeCN elution prior to LC−MS/MS analysis to ensure effective mAb removal. A 50 cm long nanoflow charged surface hybrid column was also packed to allow for higher sample load for increased sensitivity. Our workflow has increased the sensitivity for HCP identification by 10-to 100-fold over previous reports and showed the robustness as low as 0.1 ppm for identifying HCPs (34.5 to 66.2 kDa MW). The method capability was further confirmed by consistently identifying >85% of 48 UPS-1 proteins (0.10 to 1.34 ppm, 6.3 to 82.9 kDa MW) in a monoclonal antibody (mAb) and the largest number (746) of mouse proteins from NIST mAb reported to date by a single analysis. Our work has filled a significant gap in HCP analysis for detecting and demonstrating HCP clearance, in particular, extremely low-level hydrolases in drug process development.

Host cell proteins (HCPs) are process-related impurities derived from the manufacturing of recombinant biotherapeutics. Residual HCP in drug products, ranging from 1 to 100 ppm (ng HCP/mg product) or even below sub-ppm level, may affect product quality, stability, efficacy, or safety. Therefore, removal of HCPs to appropriate levels is critical for the bioprocess development of biotherapeutics. Liquid chromatography-mass spectrometry (LC-MS) analysis has become an important tool to identify, quantify, and monitor the clearance of individual HCPs. This review covers the technical advancement of sample preparation strategies, new LC-MS-based techniques, and data analysis approaches to robustly and sensitively measure HCPs while overcoming the high dynamic range analytical challenges. We also discuss our strategy for LC-MS-based HCP workflows to enable fast support of process development throughout the product life cycle, and provide insights into developing specific analytical strategies leveraging LC-MS tools to control HCPs in process and mitigate their potential risks to drug quality, stability, and patient safety.