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Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study.

Trina Mouchahoir1,2, John E Schiel1,2, Rich Rogers3

  • 1National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, Maryland 20899, United States.

Journal of the American Society for Mass Spectrometry
|August 26, 2022
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Summary
This summary is machine-generated.

The multi-attribute method (MAM) shows promise for protein therapeutic analysis, but interlaboratory variability needs addressing for widespread quality control (QC) adoption. Further optimization is key to integrating MAM into routine QC environments.

Keywords:
MAM ConsortiumNISTmAbattribute analyticsmulti-attribute methodtargeted analytics

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Area of Science:

  • Biopharmaceutical Analysis
  • Analytical Chemistry
  • Mass Spectrometry

Background:

  • The multi-attribute method (MAM) was developed as a single, comprehensive assay for protein therapeutics.
  • MAM utilizes mass spectrometry (MS) for identifying and quantifying numerous protein attributes.
  • Current quality control (QC) relies on multiple, established orthogonal methods, hindering MAM adoption in QC labs.

Purpose of the Study:

  • To evaluate the current industry-wide status of MAM implementation.
  • To assess the targeted attribute analytics component of MAM.
  • To identify sources of interlaboratory variability and compare MAM data with orthogonal methods.

Main Methods:

  • An interlaboratory study was conducted by the MAM consortium.
  • Targeted attribute analytics were investigated within the MAM framework.
  • Data from MAM were compared against results from established orthogonal methods.

Main Results:

  • Variability between laboratories using MAM was investigated.
  • MAM data demonstrated comparability with orthogonal methods for targeted attributes.
  • Specific sources contributing to interlaboratory variability were identified.

Conclusions:

  • MAM shows potential as a unified assay for protein therapeutic characterization.
  • Addressing identified sources of variability is crucial for routine QC implementation.
  • Further optimization efforts can facilitate broader adoption of MAM in QC settings.