Toward unbiased identification and comparative quantification of host cell protein impurities by automated iterative...

Host cell proteins (HCPs) are process-related impurities expressed by the host cells used for production of therapeutic proteins. Although an extensive purification process removes most of the HCPs, residual HCPs are commonly present in protein therapeutics. If not well-controlled, certain HCPs may be present in the product, impacting drug stability, and potentially affecting product safety leading to safety risks for patients. Therefore, as a critical quality attribute, the levels of HCPs must be closely monitored during drug development and determined in the final drug substance at release. Liquid chromatography-mass spectrometry (LC-MS) as an orthogonal approach to traditional ELISA for HCP analysis has shown tremendous value in drug process development and analytical characterization by providing additional critical information, such as protein identity and relative abundance of individual HCPs. To meet the challenges in HCP analysis during drug development, especially downstream process development, which entails fast turnaround time and robustness while identifying high level of HCPs and their clearance trend for further purification development, we have developed HCP-automated iterative MS (HCP-AIMS): it is a simple, automated and robust HCP analysis workflow with deep and unbiased identification and relative quantification capability. This HCP-AIMS approach only requires easy direct digestion of the samples without enrichment or pre- treatment. With the fully automated precursor ion exclusion in MS/MS mode, low abundance HCP peptides could be selected for MS/MS analysis in iterative replicates, and therefore, the identification of HCPs at low abundance can be achieved. Using an in-house mAb with various levels of spiked-in HCPs as well as the NIST mAb, we were able to achieve unbiased identification and quantitation of HCPs as low as 10 ppm level. Furthermore, robustness of the HCP-AIMS approach was also confirmed for the feasibility of large-scale and high-throughput analysis.