Rajesh Deshmukh, Lata Sharma, Muktika Tekade, Prashant Kesharwani, Piyush Trivedi, Rakesh K. Tekade. Force degradation behavior of glucocorticoid deflazacort by UPLC: isolation, identification and characterization of degradant by FTIR, NMR and mass analysis[J]. The Journal of Biomedical Research, 2016, 30(2): 149-161. DOI: 10.7555/JBR.30.20150074
Citation:
Rajesh Deshmukh, Lata Sharma, Muktika Tekade, Prashant Kesharwani, Piyush Trivedi, Rakesh K. Tekade. Force degradation behavior of glucocorticoid deflazacort by UPLC: isolation, identification and characterization of degradant by FTIR, NMR and mass analysis[J]. The Journal of Biomedical Research, 2016, 30(2): 149-161. DOI: 10.7555/JBR.30.20150074
Rajesh Deshmukh, Lata Sharma, Muktika Tekade, Prashant Kesharwani, Piyush Trivedi, Rakesh K. Tekade. Force degradation behavior of glucocorticoid deflazacort by UPLC: isolation, identification and characterization of degradant by FTIR, NMR and mass analysis[J]. The Journal of Biomedical Research, 2016, 30(2): 149-161. DOI: 10.7555/JBR.30.20150074
Citation:
Rajesh Deshmukh, Lata Sharma, Muktika Tekade, Prashant Kesharwani, Piyush Trivedi, Rakesh K. Tekade. Force degradation behavior of glucocorticoid deflazacort by UPLC: isolation, identification and characterization of degradant by FTIR, NMR and mass analysis[J]. The Journal of Biomedical Research, 2016, 30(2): 149-161. DOI: 10.7555/JBR.30.20150074
Force degradation behavior of glucocorticoid deflazacort by UPLC: isolation, identification and characterization of degradant by FTIR, NMR and mass analysis
In this investigation, sensitive and reproducible methods are described for quantitative determination of deflazacort in the presence of its degradation product. The method was based on high performance liquid chromatography of the drug from its degradation product on reverse phase using Acquity UPLC BEH C18 columns (1.7 μm, 2.1 mm × 150 mm) using acetonitrile and water (40:60 V/V) at a flow rate of 0.2 mL/minute in UPLC. UV detection was performed at 240.1 nm. Deflazacort was subjected to oxidative, acid, base, hydrolytic,thermal and photolytic degradation. The drug was found to be stable in water and thermal stress, as well as under neutral stress conditions. However, forced-degradation study performed on deflazacort showed that the drug degraded under alkaline, acid and photolytic stress. The degradation products were well resolved from the main peak, which proved the stability-indicating power of the method. The developed method was validated as per ICH guidelines with respect to accuracy, linearity, limit of detection, limit of quantification, accuracy, precision and robustness, selectivity and specificity. Apart from the aforementioned, the results of the present study also emphasize the importance of isolation characterization and identification of degradant. Hence, an attempt was made to identify the degradants in deflazacort. One of the degradation products of deflazacort was isolated and identified by the FTIR, NMR and LC-MS study.
Yu F, Zhao LX, Chu S. TCHH as a Novel Prognostic Biomarker for Patients with Gastric Cancer by Bioinformatics Analysis. Clin Exp Gastroenterol, 2024, 17: 61-74.
DOI:10.2147/CEG.S451676
2.
Li K, Qiu Y, Liu X, et al. Biomimetic Nanosystems for the Synergistic Delivery of miR-144/451a for Oral Squamous Cell Carcinoma. Balkan Med J, 2022, 39(3): 178-186.
DOI:10.4274/balkanmedj.galenos.2022.2021-11-1
3.
Yu CC, Chan MWY, Lin HY, et al. IRAK2, an IL1R/TLR Immune Mediator, Enhances Radiosensitivity via Modulating Caspase 8/3-Mediated Apoptosis in Oral Squamous Cell Carcinoma. Front Oncol, 2021, 11: 647175.
DOI:10.3389/fonc.2021.647175
4.
Amiri-Dashatan N, Koushki M, Jalilian A, et al. Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma. Asian Pac J Cancer Prev, 2020, 21(6): 1841-1848.
DOI:10.31557/APJCP.2020.21.6.1841
5.
Xu GQ, Li LH, Wei JN, et al. Identification and profiling of microRNAs expressed in oral buccal mucosa squamous cell carcinoma of Chinese hamster. Sci Rep, 2019, 9(1): 15616.
DOI:10.1038/s41598-019-52197-3
6.
Li CY, Zhang WW, Xiang JL, et al. Integrated analysis highlights multiple long non‑coding RNAs and their potential roles in the progression of human esophageal squamous cell carcinoma. Oncol Rep, 2019, 42(6): 2583-2599.
DOI:10.3892/or.2019.7377
7.
Zhong L, Liu Y, Wang K, et al. Biomarkers: paving stones on the road towards the personalized precision medicine for oral squamous cell carcinoma. BMC Cancer, 2018, 18(1): 911.
DOI:10.1186/s12885-018-4806-7
8.
Willett CS, Wilson EM. Evolution of Melanoma Antigen-A11 (MAGEA11) During Primate Phylogeny. J Mol Evol, 2018, 86(3-4): 240-253.
DOI:10.1007/s00239-018-9838-8
9.
Li J, Zhou D, Qiu W, et al. Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design. Sci Rep, 2018, 8(1): 622.
DOI:10.1038/s41598-017-18705-z
10.
Mallik S, Zhao Z. ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis. Genes (Basel), 2017, 9(1): 7.
DOI:10.3390/genes9010007
11.
Irimie AI, Braicu C, Pileczki V, et al. Knocking down of p53 triggers apoptosis and autophagy, concomitantly with inhibition of migration on SSC-4 oral squamous carcinoma cells. Mol Cell Biochem, 2016, 419(1-2): 75-82.
DOI:10.1007/s11010-016-2751-9