Impurity profiling and stability-indicating method development and validation for the estimation of assay and degradation impurities of midostaurin in softgel capsules using HPLC and LC-MS.
Narasimha Swamy LakkaChandrasekar KuppanPoornima RavinathanPublished in: Biomedical chromatography : BMC (2021)
Midostaurin (MDS) is used for the treatment of acute myeloid leukemia, myelodysplastic syndrome, and advanced systemic mastocytosis. MDS softgel capsule samples were subjected to stress testing per International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use guidelines for impurity profiling study. MDS underwent extensive degradation under stress testing (acid, alkaline, oxidative, photolytic, thermolytic, and hydrolysis conditions) and formed four degradation products (DPs). MDS and its DPs were separated well from one another with good resolution using reserved-phase HPLC using an Inertsil ODS-3V column (250 × 4.6 mm, 5 μm) and a mobile phase of ammonium formate (40 mM) and acetonitrile. The stability-indicating characteristic of the newly developed method was proven for the estimation of MDS assay, and its organic impurities were free from interference. The validated method exhibited excellent linearity, accuracy, precision, specificity, detection limit, and quantitation limit within 25 min run time. Stress testing, robustness, and solution stability were performed to ensure the continuous performance of the developed method. The peak fractions of DPs formed under stress testing were isolated and characterized using LC-MS, 1 H and 13 C NMR, IR, and UV-Vis. The structure of the major DPs was predicted as DP1 based on the spectral data. The proposed method is effectively used for MDS in bulk drug and finished formulations in the pharmaceutical industry.
Keyphrases
- ms ms
- acute myeloid leukemia
- simultaneous determination
- magnetic resonance
- endothelial cells
- solid phase extraction
- stress induced
- emergency department
- magnetic resonance imaging
- machine learning
- heat stress
- liquid chromatography tandem mass spectrometry
- anaerobic digestion
- optical coherence tomography
- deep learning
- single molecule
- acute lymphoblastic leukemia
- real time pcr