Design, synthesis, and biological evaluation of indolylidinepyrazolones as potential anti-bacterial agents

Hybrid silica nanoparticles functionalized with pyrazolone-tethered organosilane: Synthesis and application for Sn (II) ion sensing in tap water

Pyrazolone tethered triazole functionalized organosilane and their hybrid silica nanoparticles (HSNPs) have been synthesised for the selective detection of Sn(II) using spectrophotometric techniques. The prepared compounds are characterized by FT-IR, NMR (1H and 13C), XRD, mass spectrometry and FE-SEM spectral analyses. The synthesized hybrid silica nanoparticles gave improved detection limit of 4.3 × 10-8 M and stoichiometry of complex between analyte and probe was found to be 1:1. The association constant calculated for organosilane and their HSNPs came out to be 9.54 × 104 M-1 and 7.47 × 104 M-1 while the presence of other ions showed no interference in the sensing behaviour. The results of the use of this sensing system in tap water facilitate its applicability in real samples with the recovery % more than 98.

Pyrazolone-type compounds (part II): in vitro and in silico evaluation of antioxidant potential; structure-activity relationship

The pyrazolone class comprises a variety of hybrid compounds displaying diverse biological actions. Although studied for decades, these compounds are still of interest due to their facile chemical transformations. In our previous work, we presented the synthetic route of functionalised pyrazolone derivatives. The presence of pyrazolone structural motif in many drugs, such as edaravone, prompted us to investigate the antioxidant features of the selected compounds. In this paper, we provide an extensive in vitro and in silico description of the antioxidant properties of selected pyrazolone analogues. The obtained in vitro results revealed their great antiradical potency against the DPPH radical (IC₅₀ values in the 2.6-7.8 μM range), where the best results were obtained for analogues bearing a catechol moiety. Density functional theory (DFT) was used to assess their antioxidant capacity from the thermodynamic aspect. Here, good agreement with in vitro results was achieved. DFT was employed for the prediction of the most preferable radical scavenging pathway, also. In polar solvents, the SPLET mechanism is a favourable scavenging route, whereas in nonpolar solvents the HAT is slightly predominant. Furthermore, antioxidant mechanisms were studied in the presence of relevant reactive oxygen species. The obtained values of the reaction enthalpies with the selected radicals revealed that HAT is slightly prevailing in polar solvents, while the SPLET mechanism is dominant in nonpolar solvents. Regarding the well-known antioxidant features of the drug edaravone, these findings represent valuable data for this pyrazolone class and could be used as the basis for further investigations.

A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy

The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R² of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (¹H and ¹³C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.