Qualification of Targeted Attribute Quantitation and New Peak Detection for Automated Multi-Attribute Method Workflows Using Liquid Handling Systems

Background/Objectives: The Multi-Attribute Method (MAM) uses LC-MS peptide mapping for targeted attribute quantitation (TAQ) and untargeted New Peak Detection (NPD) in product quality monitoring and quality control of biotherapeutics. As automated liquid handling systems are adopted, data demonstrating similar performance for both TAQ and NPD are essential to implement an automatic sample preparation workflow to fully achieve MAM capabilities. This study compares manual versus automated sample preparation workflows for both TAQ and NPD. Methods: Peptide mapping sample preparation was performed manually and on an automated liquid handling system. TAQ was evaluated using a reference monoclonal antibody (mAb1) while NPD was assessed by spiking a second monoclonal antibody (mAb2) into mAb1 (0.5–2% w/w relative abundance). NPD specificity and sensitivity were qualified using a product-specific peptide library, multiple bracketing comparisons, and a statistically derived Fold Change Detection (FCD) threshold. Results: For TAQ, the automated workflow demonstrated comparable performance to the manual method, with minimal quantitative differences and high reproducibility across 28 monitored product quality attributes. For NPD, all expected mAb2 peptides were detected with no false positives at each spike level. No new peaks were observed when comparing bracketing unspiked mAb1 samples, confirming specificity for NPD. Conclusions: The results demonstrate that automated sample preparation yields consistent TAQ and NPD data compared to manual methods and can be used for routine product quality monitoring.