Fungal mediated biotransformation of melengestrol acetate, and T-cell proliferation inhibitory activity of biotransformed compounds

Identification of effective synthetic molecules against viral-induced cytokine release syndrome using in silico and in vitro approaches

Acute respiratory distress syndrome (ARDS) is the leading cause of mortality in pathogen-mediated lung inflammation. Viral-induced cytokine release syndrome (CRS) has emerged as a global pandemic, characterized by a hyperactive immune response and excessive cytokine production causing irreversible lung injury. This study aimed to evaluate FDA-approved drugs for their potential to target hyperactive immune response and SARS-CoV-2 viral replication simultaneously. Six potential 3-CLpro inhibitors were identified by molecular docking using MOE software, including ebastine (1), orlistat (2), atracurium besylate (3), piperaquine phosphate (4), valsartan (5), and acarbose (6), among which 1-3 binds strongly to the target protein with binding affinity of - 8.22, - 9.12, and - 7.81, kcal/mol, respectively. Additionally, all identified inhibitors except 4 revealed significant anti-viral potential, with a 50-100% reduction in SARS-CoV-2 plaques. Significant attenuation of phagocyte oxidative burst and inflammatory cytokines (IFN-γ, GM-CSF, IL-6, IL-2, IL-1β, TNF-α) demonstrated the immunomodulatory potential of these drugs. This study demonstrates the potential of pre-existing drugs to ameliorate the cytokine storm and oxidative damage with simultaneous anti-viral effects. The data provide pre-clinical support to develop these drugs as potential therapeutic agent against ARDS.

Further transformation of fungal catalyzed transformed metabolite 11β-hydroxy-dianabol into new aromatase inhibitors

Secondary biotransformation of 11β-hydroxy-dianabol (11β,17β-dihydroxy-17α-methylandrost-1,4-dien-3-one) (1), catalyzed by using two fungi Gibberella fujikuroi and Cunninghamella blakesleeana at ambient conditions, was carried out to synthesize its analogues. Transformation of compound 1 with G. fujikuroi yielded a new metabolite, 11β, 17β-dihydroxy-17α-methyl-5β-androst-1-ene-3-one (2), while four new derivatives, 6β, 17β-dihydroxy-17α-methylandrost-1,4-diene-3,11-dione (3), 15α,17β-dihydroxy-17α-methylandrost-1,4-diene-3,11-dione (4), 6β,11β,17β-trihydroxy-17α-methylandrost-1,4-dien-3-one (5), and 7β,11β,17β-trihydroxy-17α-methylandrost-1,4-dien-3-one (6) were obtained by transformation with C. blakesleeana. Compounds 1-6 showed a significant aromatase inhibition with IC50 values in the range of 2.01-3.13 μM as compared to the standard drug, exemestane (IC50 = 0.21 ± 0.16 μM). Aromatase is a valid target for drug discovery against ER+ breast cancers. Compounds 1-6 were subjected to molecular docking studies to predict the key interactions, and the MMGBSA studies to analyze the binding affinity and thermal stability of the protein-ligand complexes. Further, the relationship between the metabolites 1-6 and breast tumor androgen receptors was evaluated by in silico approach to analyze the binding interactions between androgen receptors and metabolites. Moreover, compounds 1-6 were found as non-cytotoxic to BJ (Human fibroblast) normal cell line. Hence, these molecules can be further studied for optimization as potential aromatase inhibitors against breast cancer.

Metabolites rapid-annotation in mice by comprehensive method of virtual polygons and Kendric mass loss filtering: A case study of Dendrobium nobile Lindl

With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.