SAR and QSAR in Environmental Research最新文献

Thiadiazole and hydrazone derivatives (5a-5i) were synthesized and their chemical structures were verified and described by ¹H NMR, ¹³C NMR, and HRMS spectra. Three cancer cell lines (MCF-7, MDA, and HT-29) and one healthy cell line (L929) were used to test the cytotoxicity activity of synthesized compounds as well as their inhibitory activity against carbonic anhydrase I, II and IX isoenzymes. Compound 5d (29.74 µM) had a high inhibitory effect on hCA I and compound 5b (23.18 µM) had a high inhibitory effect on hCA II. Furthermore, compound 5i was found to be the most potent against CA IX. Compounds 5a-5i, 5b and 5i showed the highest anticancer effect against MCF-7 cell line with an IC₅₀ value of 9.19 and 23.50 µM, and compound 5d showed the highest anticancer effect against MDA cell line with an IC₅₀ value of 10.43 µM. The presence of fluoro substituent in the o-position of the phenyl ring increases the effect on hCA II, while the methoxy group in the o-position of the phenyl ring increases the activity on hCA I as well as increase the anticancer activity. Cell death induction was evaluated by Annexin V assay and it was determined that these compounds cause cell death by apoptosis. Molecular docking was performed for compounds 5b and 5d to understand their biological interactions. The physical and ADME properties of compounds 5b and 5d were evaluated using SwissADME.

Prior to the manufacture of new chemicals, regulations mandate a thorough review of the chemicals under risk management. This review involves evaluating their effects on the environment and human health. To assess these effects, a review report that conforms to the OECD Test Guidelines must be submitted to the regulatory body. One of the essential components of the report is an assessment of the biodegradability of chemicals in the environment. In addition to conventional methods, quantitative structure-activity relationship (QSAR) models have been developed to predict the properties of chemicals based on their structural features. Although a greater number of chemicals in the learning set may enhance the prediction accuracy, it may also lead to a decrease in accuracy due to the mixing of different structural features and properties of the chemicals. To improve the prediction performance, it is recommended to use only the appropriate data for biodegradability prediction as a training set. In this study, we propose a novel approach for the optimal selection of training set that enables a highly accurate prediction of the biodegradability of chemicals by QSAR. Our findings indicate that the proposed method effectively reduces the root mean squared error and improves the prediction accuracy.

In the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreover, molecular simulation revealed stable dynamics, tighter structural packing and less flexible behaviour for all the complexes. We further reported that the average hydrogen bonds in TCM27763, TCM33057, TCM34450 and TCM31564I7L complexes remained higher than the control drug. Finally, the BF energy results revealed -62.60 ± 0.65 for the controlI7L complex, for the TCM27763I7L complex -71.92 ± 0.70 kcal/mol, for the TCM33057I7L complex the BF energy was -70.94 ± 0.70 kcal/mol, for the TCM34450I7L the BF energy was -69.94 ± 0.85 kcal/mol while for the TCM31564I7L complex the BF energy was calculated to be -69.16 ± 0.80 kcal/mol. Although, we used stateoftheart computational methods, these are theoretical insights that need further experimental validation.

The human gut microbiota (HGM) comprises a complex population of microorganisms that significantly affect human health, including their influence on xenobiotics metabolism. Many pharmaceuticals are taken orally and thus come into contact with HGM, which can metabolize them. Therefore, it is necessary to evaluate the effect of HGM on the fate of pharmaceuticals in the organism. We have collected information about over 600 compounds from more than eighty publications. At least half of them (329 compounds) are known to be metabolized by HGM. We have used PASS (Prediction of Activity Spectra for Substances) software to build three classification SAR models for HGM-mediated drug metabolism prediction. The first model with an accuracy of prediction 0.85 estimates whether compounds will be metabolized by HGM. The second model with an average accuracy of prediction 0.92 estimates which bacterial genera are responsible for the drug metabolism. The third model with an average accuracy of prediction 0.92 estimates the biotransformation reactions during HGM-mediated drug metabolism. The created models were used to develop the freely available web application MDM-Pred (http://www.way2drug.com/mdm-pred/).

This brief literature survey groups the (numerical) validation methods and emphasizes the contradictions and confusion considering bias, variance and predictive performance. A multicriteria decision-making analysis has been made using the sum of absolute ranking differences (SRD), illustrated with five case studies (seven examples). SRD was applied to compare external and cross-validation techniques, indicators of predictive performance, and to select optimal methods to determine the applicability domain (AD). The ordering of model validation methods was in accordance with the sayings of original authors, but they are contradictory within each other, suggesting that any variant of cross-validation can be superior or inferior to other variants depending on the algorithm, data structure and circumstances applied. A simple fivefold cross-validation proved to be superior to the Bayesian Information Criterion in the vast majority of situations. It is simply not sufficient to test a numerical validation method in one situation only, even if it is a well defined one. SRD as a preferable multicriteria decision-making algorithm is suitable for tailoring the techniques for validation, and for the optimal determination of the applicability domain according to the dataset in question.

Clinical studies show that the pyroglutamate alteration of amyloid-β (Aβ) catalysed by metalloenzyme glutaminyl cyclase results in the formation of the more neurotoxic pGlu-Aβ, and inhibition of glutaminyl cyclase can bring down the load of pGlu-Aβ in the brain and reduces Alzheimer's disease pathology with improvement in cognition. The present study involves the identification of activity-modulating structural features of 188 inhibitors of glutaminyl cyclase under the influence of index of ideality of correlation (IIC) and correlation intensity index (CII) as prediction parameters. The QSAR models developed employing IIC and CII were found to be statistically better and had better predictability than the models developed without them. The best model (split 4) showed r² values of 0.8155 and 0.8218 for calibration and validation sets, respectively. The structural features classified from QSAR models were used to design some new glutaminyl cyclase inhibitors. Among the designed ligands, ligand 5 possesses the highest pIC₅₀ value (6.30) as well as binding affinity (-6.2 kcal/mol) and creates hydrogen bonds with TRP 329, π-alkyl interactions with ILE 303 and TYR 299, π-π stacking interaction with PHE 325 and interactions with ZN 391. All novel designed ligands have better pIC₅₀ values and binding affinities.

Nine new functionally substituted derivatives of 2-aminothiazole were evaluated for antimicrobial activity using microdilution method against the panel of eight bacterial and eight fungal strains. Evaluation of antibacterial activity revealed that compounds are potent antibacterial agents, more active than ampicillin and streptomycin except of some compounds against B. cereus and En. cloacae. The best compound appeared to be compound 8. The most sensitive bacteria appeared to be En. cloacae, while L. monocytogenes was the most resistant. Compounds also exhibited good antifungal activity much better than two reference drugs, ketoconazole and bifonazole. Compound 1 exhibited the best antifungal activity. The most sensitive fungus was T. viride, while A. fumigatus was the most resistant. Bacteria as well as fungi in general showed different sensitivity towards compounds tested. Molecular docking studies revealed that MurB inhibition is probably involved in the mechanism of antibacterial activity, while CYP51 of C. albicans is responsible for the mechanism of antifungal activity. Finally, it should be mentioned that all compounds displayed very good druglikeness scores.

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (r_ext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC₅₀ values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

Some adverse effects of hydroxylated polychlorinated biphenyls (OH-PCBs) in humans are presumed to be initiated via thyroid hormone receptor (TR) binding. Due to the trial-and-error approach adopted for OH-PCB selection in previous studies, experiments designed to test the TR binding hypothesis mostly utilized inactive OH-PCBs, leading to considerable waste of time, effort and other material resources. In this paper, linear discriminant analysis (LDA) and binary logistic regression (LR) were used to develop classification models to group OH-PCBs into active and inactive TR agonists using radial distribution function (RDF) descriptors as predictor variables. The classifications made by both LDA and LR models on the training set compounds resulted in an accuracy of 84.3%, sensitivity of 72.2% and specificity of 90.9%. The areas under the ROC curves, constructed with the training set data, were found to be 0.872 and 0.880 for LDA and LR models, respectively. External validation of the models revealed that 76.5% of the test set compounds were correctly classified by both LDA and LR models. These findings suggest that the two models reported in this paper are good and reliable for classifying OH-PCB congeners into active and inactive TR agonists.

As a target for clinical anti-cancer treatment, epidermal growth factor receptor (EGFR) exhibits its over-expression on various tumour cells and is associated with the development of a variety of human cancers. Herein, we described the synthesis, antiproliferative activity assay and 4D-QSAR studies of thiadiazole derivatives bearing acrylamide moiety as EGFR inhibitors. Compared with Gefitinib, some of the target compounds have excellent antiproliferative activities against EGFR-expressed A431 cell line. The robust and reliable 4D-QSAR was constructed using comparative distribution detection algorithm, ordered predictors selection and genetic algorithm method, and the following acceptable statistics are shown: r² = 0.82, Q²_LOO = 0.67, Q²_LMO = 0.61, r²_Pred = 0.78.