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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
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Updated: Jun 6, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Exploring the Correlation between LC-MS Multi-Attribute Method and Conventional Chromatographic Product Quality

Tingting Jiang1, Francis Kwofie1, Nick Attanasio1

  • 1Global Vaccines and Biologics Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, West Point, Pennsylvania, 19486, USA.

The AAPS Journal
|November 21, 2024
PubMed
Summary
This summary is machine-generated.

A rapid multi-attribute method (MAM) workflow for biotherapeutics was developed. This method quantitatively predicts traditional quality attributes using mass spectrometry, enhancing process understanding and evaluating MAM for quality control release.

Keywords:
CQAHICIEXMAMMVDAMonoclonal antibodyPeptide mappingQC

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Area of Science:

  • Biopharmaceutical Analysis
  • Analytical Chemistry
  • Process Analytical Technology (PAT)

Background:

  • Biotherapeutics exhibit heterogeneity due to complex manufacturing.
  • Critical Quality Attributes (CQAs) monitoring is essential for safety and efficacy.
  • Mass Spectrometry (MS)-based Multi-Attribute Method (MAM) shows promise but requires validation against traditional methods.

Purpose of the Study:

  • To develop a simple, robust, and rapid MAM workflow for CQA monitoring.
  • To establish a comprehensive database correlating MAM with traditional analytical methods.
  • To evaluate MAM's suitability as a replacement for conventional quality control assays.

Main Methods:

  • Development of a rapid MAM workflow for biotherapeutic CQAs.
  • Creation of a database (~700 samples) with MAM, charge variant, and oxidation data.
  • Application of Multivariate Data Analysis (MVDA), including Partial Least Squares Regression (PLSR), to correlate MAM data with traditional assays.

Main Results:

  • Successfully created a database integrating MAM and traditional analytical data.
  • Demonstrated quantitative prediction of charge variants (Ion Exchange Chromatography - IEX) and oxidation levels (Hydrophobic-Interaction Chromatography - HIC) using MAM data via PLSR models.
  • Highlighted the strong interconnectivity between MAM and conventional product quality assays.

Conclusions:

  • The rapid MAM workflow enables robust monitoring of biotherapeutic CQAs.
  • MVDA effectively leverages MAM data to predict outcomes of traditional assays, supporting process understanding.
  • Findings support the evaluation of MAM as a potential replacement for conventional release assays, enhancing biopharmaceutical quality control.