Gábor Transform-Based Antibody Quantitation in Serum: An Interlaboratory Liquid Chromatography/High-Resolution Mass...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Pharmacology

Background:

• Therapeutic monoclonal antibodies (t-mAbs) are vital for treating cancers and autoimmune diseases.
• Current quantification methods (LBAs, LC-MS/MS) often lack the required sensitivity and specificity.
• High-resolution mass spectrometry (HRMS) offers improved proteoform quantification.

Purpose of the Study:

• To evaluate the novel Gábor transform (GT)-based iFAMS Quant+ software for therapeutic monoclonal antibody (t-mAb) quantification.
• To compare the performance of iFAMS Quant+ with a validated laboratory-developed test (LDT).
• To assess the utility of HRMS in top-down and middle-up approaches for t-mAb analysis.

Main Methods:

• Utilized iFAMS Quant+ software with an Agilent 6545XT AdvanceBio Q-TOF for t-mAb (vedolizumab) quantification in approximately 400 serum samples.
• Compared results against a Thermo Q Exactive Plus Orbitrap LDT from Mayo Clinic using conventional extracted ion chromatograms (XICs).
• Employed GT-based charge state deconvolution, background subtraction, and signal integration in the iFAMS Quant+ workflow.

Main Results:

• Demonstrated excellent agreement between iFAMS Quant+ and the LDT through calibration, quality control, and Passing-Bablok regression analyses of 351 subject samples.
• The iFAMS Quant+ workflow successfully quantified vedolizumab, a therapeutic monoclonal antibody.
• Identified unique advantages of the iFAMS Quant+ deconvolution approach for characterizing interferents and analyte signal anomalies.

Conclusions:

• The Gábor transform (GT)-based iFAMS Quant+ software provides accurate and reliable quantification of therapeutic monoclonal antibodies (t-mAbs).
• This HRMS-based approach offers a viable alternative to existing methods, with enhanced capabilities for data interpretation.
• The deconvolution-based workflow of iFAMS Quant+ is advantageous for complex biological matrices and identifying potential interferences in t-mAb analysis.