Automatic Identification of Lansoprazole Degradants under Stress Conditions by LC-HRMS with MassChemSite and WebChembase.
Stefano BonciarelliJenny DesantisLaura GoracciLydia SiragusaIsmael ZamoraElisabeth Ortega-CarrascoPublished in: Journal of chemical information and modeling (2021)
Stress testing is one of the most important parts of the drug development process, helping to foresee stability problems and to identify degradation products. One of the processes involving stress testing is represented by forced degradation studies, which can predict the impact of certain conditions of pH, moisture, heat, or other negative effects due to transportation or packaging issues on drug potency and purity, ensuring patient safety. Regulatory agencies have been working on a standardization of laboratory procedures since the past two decades. One of the results of those years of intensive research is the International Conference on Harmonization (ICH) guidelines, which clearly define which forced degradation studies should be performed on new drugs, which become a routine work in pharmaceutical laboratories. Since used techniques based on high-performance liquid chromatography coupled with high-resolution mass spectrometry have been developed years ago and are now mastered by pharmaceutical scientists, automation of data analysis, and thus data processing, is becoming a hot topic nowadays. In this work, we present MassChemSite and WebChembase as a tandem to automatize the routine analysis studies without missing information quality, using as a case study the degradation of lansoprazole under acidic, oxidative, basic, and neutral stress conditions.
Keyphrases
- patient safety
- high resolution mass spectrometry
- data analysis
- high performance liquid chromatography
- liquid chromatography
- clinical practice
- mass spectrometry
- simultaneous determination
- quality improvement
- tandem mass spectrometry
- case control
- stress induced
- mental health
- machine learning
- solid phase extraction
- emergency department
- healthcare
- transcription factor
- gas chromatography
- deep learning
- social media
- electronic health record
- artificial intelligence
- big data