Forced degradation studies of norepinephrine and epinephrine from dental anesthetics: Development of stability‐indicating HPLC method and in silico toxicity evaluation

Abreu, Letícia Coli Louvisse de; Abrahim-Vieira, Bárbara; Souza, Alessandra Mendonça Teles de; Pinto, Enrico; Gonçalves, Mariana da Silva; Simon, Alice; Viana, Gil Mendes; Rodrigues, Carlos Rangel; Sousa, Valéria Pereira de; Cabral, Lúcio Mendes

doi:10.1002/bmc.4832

Cited by 7 publications

(3 citation statements)

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“…Jagannathan endeavored to discover novel psychoactive metabolites from Cannabis sativa, the smoke of C. sativa, and other phytocannabinoid matrices, based on the physiochemical descriptors and ADMET properties predicted by four ML techniques (NB, SVM, MLP, and hierarchical clustering) [156]. Coli Louvisse de Abreu et al evaluated multiple types of toxicity of the epinephrine and norepinephrine degradation products using an ADMET predictor [151].…”

Section: Evaluation Of Admet Propertiesmentioning

confidence: 99%

Anesthetic drug discovery with computer-aided drug design and machine learning

Liu,

Xue,

Luo

et al. 2024

APS

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Computer-aided drug design (CADD) has emerged as a highly effective and indispensable tool for streamlining the drug discovery process, leading to significant reductions in cost and time. The integration of CADD with machine learning (ML) and deep learning (DL) technologies further enhances its potential and promises novel advancements in the field. In this article, we provide a review of the computational methods employed in the development of novel anesthetics, outlining their respective advantages and limitations. These techniques have demonstrated their utility across various stages of drug discovery, encompassing the exploration of target-ligand interactions, identification and validation of new binding sites, de novo drug design, evaluation and optimization of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties in lead compounds, as well as prediction of adverse effects. Through an in-depth exploration of computational approaches and their applications, this article aims to help relevant researchers develop safer and more effective anesthetic drugs.

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Section: Evaluation Of Admet Propertiesmentioning

confidence: 99%

Anesthetic drug discovery with computer-aided drug design and machine learning

Liu,

Xue,

Luo

et al. 2024

APS

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“…Currently, in silico approaches are quite popular to facilitate toxicity prediction of potential DPs. They provide an added advantage against dedicated animal experiments and standardize the limits of respective DPs in pharmaceutical preparations 19,21,22 …”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18] Though the identification and preparations. 19,21,22 We initiated an attempt in this regard by developing an easy,…”

Section: Introductionmentioning

confidence: 99%

Hesperidin: Enrichment, forced degradation, and structural elucidation of potential degradation products using spectral techniques

Bisen,

Rawat,

Sharma

et al. 2023

Rapid Comm Mass Spectrometry

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RationaleHesperidin (HES) is a well‐known citrus bioflavonoid phyto‐nutraceutical agent with polypharmacological properties. After 2019, HES was widely used for prophylaxis and COVID‐19 treatment. Moreover, it is commonly prescribed for treating varicose veins and other diseases in routine clinical practice. Pharmaceutical impurities and degradation products (DP) impact the drug's quality and safety and thus its effectiveness. Therefore, forced degradation studies help study drug stability, degradation mechanisms, and their DPs. This study was performed because stress stability studies using detailed structural characterization of hesperidin are currently unavailable in the literature.MethodsIn the HES enrichment method crude HES was converted to its pure form (98% purity) using column chromatography and then subjected to forced degradation under acid, base, and neutral hydrolyses followed by oxidative, reductive, photolytic, and thermal stress testing (International Conference on Harmonization guidelines). The stability‐indicating analytical method (SIAM) was developed to determine DPs using reversed‐phase high‐performance liquid chromatography (C18 column with methanol and 0.1% v/v acetic acid in deionized water [70:30, v/v] at 284 nm). Further, structural characterization of DPs was performed using liquid chromatography‐electrospray ionization‐tandem mass spectrometry (LC‐ESI‐MS/MS) and nuclear magnetic resonance (NMR) spectroscopy. In addition, in silico toxicity predictions were performed using pKCSM and DataWarior freeware.ResultsHES was found to be susceptible to acidic and basic hydrolytic conditions and yielded three DPs in each, which were detected using designed SIAM. Of six DPs, three were pseudo‐DPs (short lived), and the remaining were characterized using LC–MS/MS and NMR spectroscopy. The tentative mechanism of the formation of proposed DPs was explained. The proposed DPs were found inactive from in silico toxicity predictions.ConclusionsHesperidin was labile under acidic and basic stress conditions. The potential DPs were characterized using LC‐ESI‐MS/MS and NMR spectral techniques. The proposed mechanism of formation was hypothesized. In addition, to identify and characterize the DPs, a SIAM, which has broad biomedical applications, was successfully developed.

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