1. INTRODUCTION AND SCOPE
In the development of therapeutic proteins, a thorough understanding of a molecule’s product quality attributes (PQAs), and their effect on various structure-function relationships and long-term stability, is essential for ensuring the safety and efficacy of the product. The majority of PQAs are commonly characterized by various chromatographic and electrophoretic assays. These assays include capillary electrophoresis-sodium dodecyl sulfate (CE-SDS) and size-exclusion chromatography (SEC) for purity and size variant analysis, capillary isoelectric focusing (cIEF), imaged capillary isoelectric focusing (icIEF), or ion exchange chromatography (IEX) for charge variant analysis, and hydrophilic interaction liquid chromatography (HILIC) for glycan analysis. However, these conventional assays often provide an indirect measure of biologically relevant PQAs and execution of multiple methods for batch release, stability analysis, and process- and formulation-development support becomes time and resource intensive. Liquid chromatography mass spectrometry (LC-MS) peptide mapping has long been utilized as a heightened characterization tool to elucidate primary structure of therapeutic proteins and their PQAs. However, it was generally not set up as a PQA monitoring assay with the ability to supplement or replace the conventional chromatographic and electrophoretic assays. Introduced in 2015, the multi-attribute method (MAM), based on an LC-MS peptide mapping method and automated data analysis principles, provides site-specific detection, identification, quantitation, and quality control (monitoring) of many PQAs simultaneously within a single method. With its multiplexing and site-specific monitoring capabilities, a single MAM assay provides direct quantitation of each individual attribute, in a manner that also reduces time and resources by replacing multiple conventional chromatographic and electrophoretic assays. The MAM approach can provide enhanced attribute information during the product and process development stage. It has become a release testing method in addition to or in place of conventional release methods in the quality control (QC) environment. Recent regulatory expectations have shifted more towards a quality by design (QbD) approach for specification setting and filings, which requires a thorough understanding of the critical quality attributes (CQAs) of the products and designing of control strategies around the CQAs. A CQA is a physical, chemical, biological, or microbiological property, or a characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality [ICH Q8(R2)]. The need for an LC-MS-based analytical method that can monitor CQAs at the amino acid level during current good manufacturing practice (cGMP) release and stability testing would be ideal in the QbD paradigm. In addition, MAM approaches could be utilized as in-line monitoring tools during clinical and commercial manufacturing. They can also serve as process analytical technology (PAT) tools.
MAM in its broader sense can include any methods that are capable of monitoring multiple PQAs within a single method, such as some spectroscopic methods. In this chapter, the discussion of MAM will be focused on the utilization of peptide mapping LC-MS-based approaches for the analysis of therapeutic proteins. Although other non-MS based approaches may also be capable of monitoring multiple quality attributes, and the same approach could potentially also be applied to other types of therapeutics, these topics are not included in this chapter. This chapter includes guidance on sample preparation, system readiness (hardware), and software considerations. Due to the complexity of data analysis of MAM, this chapter will provide guidance on how to design the target analysis and new peak detection features. This chapter will also discuss the applications of MAM in product development and product production phases, and the method qualification and validation. In addition to the peptide mapping MAM, intact and subunit-level LC-MS analysis and use of MAM in PAT will be addressed briefly at the end of the chapter.
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