Dataset Information

Optimized sample preparation, data processing of data-independent acquisition methods for the robust quantification of trace-level host cell protein impurities in drug products

ABSTRACT: In this study, we developed powerful and reproducible MS-based analytical workflows coupling optimized and efficient sample preparations, library-free DIA acquisition method and stringent validation criteria. The performances of several preparation protocols and DIA versus classical DDA were evaluated using a series of four commercially available Drug Products. Depending on the selected protocols, the user has access to different information: on the one hand, a deep profiling of tens of identified HCPs, and on the other hand an accurate and reproducible (CV<12%) quantification of major HCPs. Overall, a final global HCP amount of a few tens of ppm in these mAb samples was measured, while reaching a sensitivity down to the sub-ppm level. Thus, this straightforward and robust approach can be intended as a routine quality control for whichever Drug Products’ analysis.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Cricetulus Griseus (chinese Hamster) (cricetulus Barabensis Griseus)

TISSUE(S): Cell Culture

SUBMITTER: Nicolas Pythoud

LAB HEAD: Christine Carapito

PROVIDER: PXD019668 | Pride | 2020-10-19

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	Adalimumab_DDA_ND.zip	Other
	Adalimumab_DDA_OLD.zip	Other
	Adalimumab_DDA_OND.zip	Other
	Adalimumab_DDA_Semi-fractionation.zip	Other
	Adalimumab_DIA_ND.zip	Other

Items per page:

1 - 5 of 64

Publications

Optimized Sample Preparation and Data Processing of Data-Independent Acquisition Methods for the Robust Quantification of Trace-Level Host Cell Protein Impurities in Antibody Drug Products.

Pythoud Nicolas N Bons Joanna J Mijola Geoffroy G Beck Alain A Cianférani Sarah S Carapito Christine C

Journal of proteome research 20201004 1

Host cell proteins (HCPs) are a major class of bioprocess-related impurities generated by the host organism and are generally present at low levels in purified biopharmaceutical products. The monitoring of these impurities is identified as an important critical quality attribute of monoclonal antibody (mAb) formulations not only due to the potential risk for the product stability and efficacy but also concerns linked to the immunogenicity of some of them. While overall HCP levels are usually mon ...[more]

PMID: 33016074

Similar Datasets

Project description:Host cell proteins (HCPs) are process-related impurities that are generated by the host organism and are typically present at low levels in recombinant biopharmaceutical products, such as therapeutic antibodies. While overall HCP levels are usually monitored by enzyme-linked immunosorbent assay (ELISA), liquid chromatography coupled to mass spectrometry (LC-MS) is emerging as a powerful tool that can provide both qualitative and quantitative information about HCP levels during purification process development. However, a major challenge for LC-MS-based methods is that there can be a more than 5 orders of magnitude difference in the concentration between HCPs and therapeutic antibody in solution, which precludes the effective identification of low abundance HCPs in antibody product. This work reports a simple and powerful strategy to identify HCPs in antibody drug substance by applying molecular weight cut-off (MWCO) filtration step followed by shotgun proteomic analysis. After dissociating the interaction between HCPs and antibody with an anionic detergent, the depletion of antibody from HCPs can be easily achieved with the MWCO filtration step. Using this method, we observed that the dynamic range across proteins in the HCP samples was significantly decreased by 1,000-fold. In addition, by spiking in known amounts of HCPs to purified antibody drug substance with low levels of HCPs, we demonstrated that our method could detect HCP at a concentration as low as 1 ppm. When applying this methodology to the study of HCPs in NIST monoclonal antibody (NISTmAb), more than 150 HCPs were confidently identified, which doubles the number of identified HCPs that have been previously reported. Parallel reaction monitoring (PRM) results confirmed that the novel HCPs found using this method were present in very low abundance (0.01-8 ppm), highlighting that our method reduces the dynamic range by removing antibody interference and improving the sensitivity of HCP identification and quantification.