Characterisation of Monoclonal Antibody Drug Candidates and Biosimilars Using Mass Spectrometry — ASN Events

Characterisation of Monoclonal Antibody Drug Candidates and Biosimilars Using Mass Spectrometry (#116)

Natasha Care 1 2 , Matthew Fitzhenry 1 2 , Mark P Molloy 1 2 , Xiaomin Song 1 2
  1. APAF, Sydney, NSW, Australia
  2. Macquarie University, Sydney, NSW, Australia

The selective and specific recognition properties of antibodies are fundamental to many applications in biomedical research. The use of monoclonal antibodies (mAbs) as therapeutic drugs has proved to be highly successful, and as patent rights run out, an increasing number of biosimilar antibodies are being developed.

The structural characterisation of mAbs is one essential requirement for their commercialisation. Mass spectrometry (MS) is a proven analytical tool for proteomics and allows the sensitive and high resolution characterisation of proteins, including mAbs.

In our work, we have applied an array of MS techniques to characterise several commercially available therapeutic mAbs. To first achieve baseline resolution of an IgG mAb, we performed intact protein mass measurement on a high resolution mass spectrometer. The relative abundance of glycans present on the antibody heavy chain was then determined using a mAb-Glyco chip LC-MS system (Agilent Technologies). To characterise the primary structure the antibody was reduced and alkylated then digested using three different enzymes (Trypsin, Glu C, Lys C) and microwave assisted acid hydrolysis. The acquired peptide MS/MS data was matched against the predicted peptide fragments for the antibody heavy and light chains. The combined results from each enzyme digestion achieved close to 100% sequence coverage for both the heavy and light chains. Disulfide bridges were determined by digestion with trypsin or GluC/Trypsin, followed by LC ESI MS/MS. A theoretical disulfide bond peak list was generated using in-house software and MasterView (AB Sciex) was used to screen the LC ESI MS/MS data and identify peptides with disulfide bonds.

This workflow demonstrates how MS can be used to generate accurate and comprehensive structural information for biomedically relevant mAbs.