Oct 2026· Pharmaceuticals· 0 citations· 96 references
Biosimilars and Bioanalytical Methods
Abstract
The multi-attribute method (MAM) is a liquid chromatography–mass spectrometry (LC–MS) platform that combines site-specific quantitation of critical quality attributes (CQAs) with non-targeted new peak detection (NPD) in a single workflow, and can consolidate several conventional chromatographic and electrophoretic assays used to control biotherapeutics. More than a decade after the introduction of MAM, the method is widely used for characterization, yet adoption for release and stability testing in current Good Manufacturing Practice (cGMP) quality control (QC) laboratories remains limited, primarily by technical rather than conceptual or regulatory barriers. In this review, we examine method-induced artifacts and their control during sample preparation, the role of automation, chromatographic and mass spectrometric requirements, software-dependent variability, and the parameter setting and false-positive management required for robust NPD. In addition, we summarize applications across the product lifecycle and the regulatory framework for QC implementation. For each barrier, we assess the readiness of the reported solutions for implementation and identify the gaps where further innovation is most needed. Within the literature identified, most barriers have documented solutions, although only a subset have been demonstrated across independent laboratories. This review is intended as a practical reference for laboratories developing, qualifying, or transferring MAM workflows.
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P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
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Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
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