New synergistic benzoquinone scaffolds as inhibitors of mycobacterial cytochrome bc1 complex to treat multi-drug resistant tuberculosis.

Through a comprehensive molecular docking study, a unique series of naphthoquinones clubbed azetidinone scaffolds was arrived with promising binding affinity to Mycobacterial Cytbc1 complex, a drug target chosen to kill multi-drug resistant Mycobacterium tuberculosis (MDR-Mtb). Five compounds from series-2, 2a, 2c, 2g, 2h, and 2j, showcased significant in vitro anti-tubercular activities against Mtb H37Rv and MDR clinical isolates. Further, synergistic studies of these compounds in combination with INH and RIF revealed a potent bactericidal effect of compound 2a at concentration of 0.39 µg/mL, and remaining (2c, 2g, 2h, and 2j) at 0.78 µg/mL. Exploration into the mechanism study through chemo-stress assay and proteome profiling uncovered the down-regulation of key proteins of electron-transport chain and Cytbc1 inhibition pathway. Metabolomics corroborated these proteome findings, and heightened further understanding of the underlying mechanism. Notably, in vitro and in vivo animal toxicity studies demonstrated minimal toxicity, thus underscoring the potential of these compounds as promising anti-TB agents in combination with RIF and INH. These active compounds adhered to Lipinski's Rule of Five, indicating the suitability of these compounds for drug development. Particular significance of molecules NQ02, 2a, and 2h, which have been patented (Published 202141033473).

Physicochemical, Interaction & Topological Descriptors vs. hMAO-A Inhibition of Aplysinopsin Analogs: A Boulevard to the Discovery of Semi-synthetic Antidepression Agents.

BACKGROUND: Depression, a neurological disorder, is globally the 4th leading cause of chronic disabilities in human beings. OBJECTIVE: This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysinopsin analogs with potent hMAO-A inhibition. METHODS: Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model development was done using principal component regression, partial least square regression, and multiple regression. RESULTS: The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions, including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with ~83% of the variance in the training set, the internal predictive ability of ~74%, while having the external predictive capability of ~79%. CONCLUSION: ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in the way of summation or multiplication products are quite influential and yield significant 2D-QSAR models with good prediction efficiency. This model can be used to design a more potent hMAO-A inhibitor with an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.