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Multi-Attribute Method for Quality Control of Therapeutic Proteins.

Sarah Rogstad1, Haoheng Yan2, Xiaoshi Wang2

  • 1Office of Testing and Research, Office of Pharmaceutical Quality, CDER , U.S. Food and Drug Administration , Silver Spring , Maryland 20993 , United States.

Analytical Chemistry
|October 17, 2019
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Summary

High-resolution mass spectrometry (MS) enables the multi-attribute method (MAM) for therapeutic protein quality control (QC) in cGMP environments. This perspective addresses key scientific and regulatory questions for MAM implementation, including risk assessment and method validation.

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Area of Science:

  • Analytical Chemistry
  • Biotechnology
  • Pharmaceutical Science

Background:

  • High-resolution mass spectrometry (MS) and semi-automated software advancements drive the adoption of MS-based methods for therapeutic protein quality control (QC).
  • The multi-attribute method (MAM) is a proposed MS-based approach offering potential benefits over conventional QC techniques like CEX, HILIC, and CE-SDS.
  • The established use of MS in cGMP environments for QC is limited, necessitating the exploration of new scientific and regulatory considerations.

Purpose of the Study:

  • To address scientific and regulatory questions surrounding the implementation of the multi-attribute method (MAM) for therapeutic protein QC in current Good Manufacturing Practice (cGMP) settings.
  • To provide a framework for evaluating MAM for both new and existing protein products.
  • To identify and suggest approaches for overcoming challenges associated with MAM adoption.

Main Methods:

  • The study categorizes implementation questions into four key areas: risk assessment, method validation, New Peak Detection (NPD) capabilities, and comparisons with conventional methods.
  • A perspective-based approach is used to outline considerations and suggest solutions for each identified aspect.
  • The focus is on practical implementation within a cGMP framework.

Main Results:

  • Key considerations for MAM implementation include comprehensive risk assessment, rigorous method validation, understanding the specifics of New Peak Detection (NPD), and direct comparisons to established QC methods.
  • Potential issues and challenges are identified across these areas.
  • Guidance is offered to facilitate the successful integration of MAM into cGMP workflows.

Conclusions:

  • Successful implementation of MAM in cGMP requires careful attention to risk assessment, validation, NPD functionality, and comparative analyses.
  • Addressing these aspects proactively will support the reliable use of MAM for therapeutic protein QC.
  • This perspective serves as a guide for navigating the complexities of adopting advanced MS-based analytical strategies in pharmaceutical manufacturing.