Scientific Abstract

Advanced LC-MS Approach for Characterization of HCPs and Delivery Vehicle (Viral) Proteins in Gene Therapy Products (BEBPA)

Invited Podium Presentation

7th Annual BEBPA HCP Conference
San Pedro, CA
May 15-17, 2019


Shiaw-Lin (Billy) Wu*, Annie Wang, Wanlu Qu, Serah Liu, Jennifer S. Chadwick
BioAnalytix Inc., Cambridge, MA.
*Invited Speaker


Host cell proteins (HCPs) in recombinant protein products have been characterized increasingly by LC-MS based approaches as an orthogonal standard for typical ELISA detection. However, HCPs derived from human cell lines in typical gene therapy products are more complex than typical recombinant biologics produced in CHO or E. coli cell lines. Multiple viral proteins co-exist in gene therapy products, increasing complexity compared to most biologic drugs expressed as single proteins in host cells. We have developed advanced LC-MS approaches to determine the identity of HCPs while providing useful information about respective levels in gene therapy product samples. Both in-gel and in-solution LC-MS methods were developed along with a further annotated database (specific for viral protein produced cell line) with quantitation capability for determining relative HCP quantities as well as the viral protein types with related splice variants. The HCP method was applied for analysis of multiple GMP Lots with related purification step samples. In addition, viral proteins and related truncations were also identified. In HCP identification, tens of relatively high abundance HCPs (>100 ppm), with several HCPs (>1000 ppm), could be seen.  The level of HCP (by LC-MS) was estimated to be approximately 2x higher than the level estimated by ELISA. The LC-MS approach was used to determine the identity of HCPs while additionally providing useful information on their respective levels in drug product samples. In addition to HCP identification, the LC-MS method has the potential to be incorporated into typical assessment approaches for concurrently monitoring key HCPs as well as related viral product quality attributes (i.e. for lot-to-lot comparability).

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