Synthesis, characterization, biological evaluation and QSAR studies of 11-p-substituted phenyl-12-phenyl-11a,12-dihydro-11H-indeno[2,1-c][1,5]benzothiazepines as potential antimicrobial agents

Synthesis of dibenzothiazepine analogues by one-pot S-arylation and intramolecular cyclization of diaryl sulfides and evaluation of antibacterial properties

Dibenzothiazepine analogues containing lactam, amidine and imine moieties were prepared from 2-aminophenyl disulfides via one-pot S-arylation. The S-arylation involved cleavage of an S-S bond of disulfides and SNAr reaction in aqueous ammonia solution of L-cysteine to afford diaryl sulfides. Dibenzothiazepine analogues having lactam and amidine moieties were obtained by cyclization of the corresponding diaryl sulfides under acidic conditions. One-pot S-arylation of 2-bromo-5-nitrobenzaldehyde gave dibenzothiazepine analogues with an imine moiety in one step through intramolecular cyclization. Compounds with antibacterial activities against Staphylococcus aureus and Escherichia coli were obtained.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Design, synthesis, and biological evaluation of a new class of MT2-selective agonists

In this study, we have designed and synthesized a novel class of chiral 2,3-dihydro-1H-indene derivatives as highly selective MT₂ agonists.