Search Results (7)

The increasing complexity of modern protein therapeutics and the demand for faster development of new therapeutics require not only advanced analytical tools that provide in-depth understanding of the product quality attributes (PQAs) that are crucial for safety and efficacy but also the implementation of a control strategy to ensure that the released drug product meets the desired quality profile. The multi-attribute method (MAM), a liquid chromatography–mass spectrometry (LC-MS) approach, has emerged as a transformative solution that provides site-specific monitoring of multiple critical quality attributes (CQAs) in a single workflow, replacing several conventional profile-based assays. The MAM achieves comprehensive quality oversight through two primary mechanisms: targeted attribute quantitation (TAQ) for the simultaneous quantification of predefined known modifications, and new peak detection (NPD) for identifying unforeseen impurities, thereby establishing a robust, “double-layered” control strategy. This article reviews the practical challenges and current industry practices for successfully implementing the MAM within current good manufacturing practice (cGMP) environments. The successful transition of the MAM from a specialized characterization tool to a validated quality control (QC) cornerstone relies on a framework built upon four foundational pillars: stringent instrument qualification paired with compliance-ready informatics, proactive and strictly scheduled instrument maintenance, rigorous system suitability testing (SST), and phase-appropriate method validation and transfer. The challenges of global method transfer and the solutions to mitigate inter-laboratory variability are discussed. Finally, the review explores the emerging trends shaping the future of the MAM in QC and the critical role of the MAM in facilitating real-time release testing (RTRT) in next-generation autonomous manufacturing. Full article

Background/Objectives: The multi-attribute method (MAM) is a powerful LC-MS workflow for site-specific monitoring of critical quality attributes (CQAs) in biopharmaceutical products, but its sensitivity to LC drift, ionization variability, and mass-calibration shifts has slowed adoption in routine quality control. A blank injection confirms that the system is clean; it does not confirm that the system is ready. This work evaluates a system performance check (SPC) workflow, built around a 13-peptide LC/MS reference standard designed against the system-readiness metrics in USP General Chapter 1060, as a readiness assessment performed before MAM sample analysis. Methods: The workflow was run on a recently installed Agilent 6230C LC/TOF with OpenLab CDS and the integrated MAM for OpenLab CDS, following routine instrument tune and calibration. The 13-peptide mix probes mass accuracy, retention time, peak area and height, in-source fragmentation, methionine oxidation, chromatographic resolution, and sodium and iron adduct formation; it also includes a heavy-to-light peptide pair for a single-point, low-abundance response check and a deamidated peptide pair to monitor a clinically important post-translational modification. Triplicate injections were evaluated against per-target acceptance criteria: mass accuracy (±10 ppm or ±13 ppm for EYK), retention-time %RSD (≤0.5%), and, for a subset of targets, peak-area %RSD (≤10%). Results: Across the three replicates, all thirteen peptides met every acceptance criterion applied to them. The twelve primary targets met their mass-accuracy and retention-time criteria, and the relative-area monitors (sodium and iron adducts, methionine oxidation, the heavy/light detector check, and the deamidation pair) met theirs. The asparagine deamidation pair was detected and reproducibly measured as a relative-area ratio at the +2 and +3 charge states in every replicate (6.7 to 7.0% at +2 and 7.5 to 7.8% at +3, against a <8% criterion), demonstrating reproducible relative monitoring of a clinically important post-translational modification. Conclusions: The SPC workflow provides a single, evidence-backed readiness measurement prior to MAM sample analysis, and it produces documentation, including the audit trail and electronic-signature functionality in OpenLab CDS 3.0, aligned with data-integrity expectations. Full article

Background/Objectives: Process characterization (PC) of biopharmaceutical products is intended to identify critical process parameters (CPPs) based on their impact on critical quality attributes (CQAs), as well as to define acceptable process parameter ranges to ensure consistent product quality and process performance. However, conventional analytical methods with indirect readouts, e.g., the sum of size/charge variants, often fall short in differentiating CQAs that have safety and efficacy relevance from product quality attributes (PQAs) that do not, thus limiting their utility in establishing a thorough process understanding. The Multi-Attribute Method (MAM) addresses these limitations by monitoring CQAs using the required resolution and specificity. Methods: A MAM platform was developed, and its robust performance and reproducibility were demonstrated across six laboratories. The MAM platform was applied for the drug substance (DS) PC of two monoclonal antibodies and the analysis of up to a hundred PQAs across 472 and 644 samples, respectively. The MAM results were compared to data obtained with conventional methods like hydrophilic interaction chromatography–fluorescence detection (HILIC-FLD) and cation-exchange chromatography with UV detection (CEX-UV). Results: The levels of PQAs, including succinimide, deamidation, glycosylation, and oxidation, were reproducible between six laboratories. Artefactual oxidation was limited by controlling the quality of TFA reagent. CPPs impacting the level of, e.g., oxidation, glycation, O-glycosylation, and N-glycan sialylation, were identified, leveraging a simultaneous, consistent, and fast MAM data analysis across all samples. Conclusions: MAM enables high-resolution PC to identify CPPs and inform control strategies for CQAs that are not resolved by conventional methods. Full article

Background/Objectives: Mass spectrometry-based multi-attribute methods (MAM) have the potential to transform therapeutic protein analysis by enabling comprehensive monitoring of multiple quality attributes in a single assay. However, the widespread adoption of MAM is hindered by significant challenges, most notably artificial oxidation during sample preparation and analysis. This report summarizes long-term operational observations and several case studies that substantiate this concern. Methods: A tryptic digest, high-resolution LC-MS MAM workflow was applied to an Fc-fusion protein and multiple antibody-based therapeutics, with a frozen reference standard analyzed in each run for system suitability and longitudinal trending. Oxidation excursions were investigated by comparing laboratories, consumables, LC-MS configurations, and other method parameters. Results: In a seven-year trending record, apparent total methionine oxidation in the Fc-fusion protein reference standard showed an abrupt, sustained increase (up to ~5-fold); the shift was traced to a specific bag of microcentrifuge-tubes used during buffer exchange and resolved after those tubes were discontinued. In an antibody–drug conjugate, observed methionine oxidation was strongly influenced by the sample preparation procedure. In other antibodies, variability of observed methionine oxidation was attributed to on-column oxidation, which produced a broad and noisy peak that interferes with automated peak integration. EDTA flushing reduced this feature, implicating exposure to metal ions. Conclusions: While advances continue to address many MAM challenges, artificial oxidation remains unpredictable and constitutes a major obstacle to robust implementation in regulated QC environments. Enhanced control strategies and further research are urgently needed to ensure reliable therapeutic protein analysis. Such control strategies include consumable qualification and change control, system suitability/trending using a reference standard, metal management across LC flow path/column lifecycle, reduction of trifluoracetic acid (TFA) exposure, data analysis to safeguard excessive on-column oxidation, etc. Full article

Background: Protein A resins are indispensable for monoclonal antibody (mAb) production, yet their condition and performance are traditionally assessed using indirect or qualitative methods. In this study, the multi-attribute method (MAM), previously applied to therapeutic protein characterization, is systematically adapted for the first time as a unified liquid chromatography–mass spectrometry (LC-MS) platform for Protein A resin analysis. Method: Four Cytiva Protein A resins, MabSelect™, MabSelect SuRe™, MabSelect SuRe™ LX, and MabSelect™ PrismA, were evaluated by MAM for resin identity, Protein A ligand integrity, fouling by impurities, and cleaning performance. Results: MAM enables resin-specific peptide fingerprinting and quantitative monitoring of Protein A ligand post-translational modifications (PTMs), including deamidation, isomerization, and fragmentation induced by repeated clean-in-place (CIP) cycles. Comparative analysis of virgin and used resins revealed ligand degradation and fouling despite engineered alkaline stability, with MabSelect™ showing the greatest susceptibility. Importantly, residual monoclonal antibodies (mAbs) and host cell proteins (HCPs) were directly detected and quantified from the resin matrix, providing a molecular-level assessment of resin cleaning effectiveness not achievable with conventional approaches. Conclusions: This work establishes MAM as a novel, sensitive, and comprehensive strategy for Protein A resin lifecycle management, delivering actionable insight for resin selection, cleaning optimization, and downstream process development. Full article

Background/Objectives: The multi-attribute method (MAM) has found diverse use in the analytical characterization of therapeutic protein products during their development and production. As the MAM matures it has the potential to enter quality control (QC) laboratories, consolidating and replacing many less informative chromatographic techniques; however, this requires an appropriate risk assessment and understanding of method capability. Methods: A validated MAM approach was used to quantify product quality attributes (PQAs) using three different mass spectrometers across two laboratories; the results were compared to conventional hydrophilic interaction chromatography–fluorescence detection (HILIC-FLD) and cation exchange chromatography–ultraviolet (CEX-UV) techniques. Results: Stressed, long-term, and accelerated stability studies were performed, and their effects on glycosylation, deamidation, oxidation and N- and C-termini were quantified. Conclusions: Overall, the inter-instrument inter-laboratory data provided here showed important considerations for transferring methods between laboratories and establishing the correlation between the MAM and conventional data, elements which are necessary to transition the MAM to the QC environment and ultimately achieving the goal of replacing orthogonal QC methods. Full article

In the quest to market increasingly safer and more potent biotherapeutic proteins, the concept of the multi-attribute method (MAM) has emerged from biopharmaceutical companies to boost the quality-by-design process development. MAM strategies rely on state-of-the-art analytical workflows based on liquid chromatography coupled to mass spectrometry (LC–MS) to identify and quantify a selected series of critical quality attributes (CQA) in a single assay. Here, we aimed at evaluating the repeatability and robustness of a benchtop LC–MS platform along with bioinformatics data treatment pipelines for peptide mapping-based MAM studies using standardized LC–MS methods, with the objective to benchmark MAM methods across laboratories, taking nivolumab as a case study. Our results evidence strong interlaboratory consistency across LC–MS platforms for all CQAs (i.e., deamidation, oxidation, lysine clipping and glycosylation). In addition, our work uniquely highlights the crucial role of bioinformatics postprocessing in MAM studies, especially for low-abundant species quantification. Altogether, we believe that MAM has fostered the development of routine, robust, easy-to-use LC–MS platforms for high-throughput determination of major CQAs in a regulated environment. Full article