Throughout drug development, it is important to characterize and quantify pharmaceutical compounds and any associated metabolites to ensure proper drug safety and efficacy. LC-MS/MS techniques are predominantly used for such analyses because they provide a comprehensive outlook and can offer a sensitive and selective approach for the quantification of compounds. However, limited deep fragment coverage and sensitivity challenge the confident identification and quantification of metabolites and drug products. Here, alternative fragmentation paired with a highly sensitive QTOF system was employed for comprehensive data collection to properly identify metabolites and quantify complex peptide therapeutics.
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.