Nitrosamine contamination in pharmaceuticals has become a major regulatory concern due to their potent carcinogenicity, requiring ultra-trace-level detection to ensure patient safety. Advanced LC-MS/MS workflows provide the necessary sensitivity, selectivity, and robustness to accurately quantify nitrosamine drug substance-related impurities (NDSRIs), even within complex pharmaceutical matrices. Case studies with betahistine, orphenadrine, and chloropyramine demonstrate how streamlined LC-MS/MS strategies can reliably detect and confirm nitrosamine impurities well below regulatory thresholds.
Nano-liquid chromatography-high-resolution tandem mass spectrometry (nano-LC-HRMS/MS) reveals saliva protein shifts before and after multiple sclerosis therapy.
Max Besenhard explains how hybrid mechanistic and machine learning models can make HPLC method development quicker, more robust and ready for autonomous digital twins.