Development of binary classification models for grouping hydroxylated polychlorinated biphenyls into active and inactive thyroid hormone receptor agonists.

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.

2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents.

Objectives: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC. Methods: The two dimensional-quantitative structure-activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively. Results: The model generated via 2D-QSAR modeling was highly predictive with R2= 0.798, R2adj = 0.754, Q2CV = 0.673, R2 test = 0.531, and cRp2 = 0.627 assessment parameters. Molecular docking-based virtual screening identified compounds 25, 32, 15, 21, and 23 with the highest MolDock scores as the best hits, of which compound 25 had the highest MolDock score of -138.329 kcal/mol. All of the identified hits had higher MolDock scores than the standard drug (osimertinib). The best hit compounds were ascertained to be drug-like in nature following the Lipinski's rule of five. Also, their ADMET features displayed average pharmacokinetic profiles. Conclusion: After successful preclinical testing, the hit compounds identified in this study may serve as potential NSCLC therapeutic agents due to their safety and efficacy with the exception of compound 23, which was found to be toxic. They can also serve as a template for designing novel NSCLC therapeutic agents.

The role of molecular modelling strategies in validating the effects of chrysin on sodium arsenite-induced chromosomal and DNA damage.

Chrysin (CHR) is a food-based bioactive ingredient whereas, sodium arsenite (SA) is one of the major contaminant in drinking water. When ingested, SA contributes to tissue damage due to bioactivation by S-adenosyl methionine (SAM)-dependent methyltransferase. Hence, the needs to nullify this effect by investigating the potentials of CHR on SA-induced genotoxicity in rats. The experiment was divided into two successive stages (ameliorative and preventive, curative studies) for 1 week. Rats were divided into four groups: distilled water, 10mg/kg SA, 10mg/kg CHR and co-administration. In stage 2, the experimental groups were given either CHR or SA for 1 week, and treated in reversed order for additional week. Lipid peroxidation, protein carbonyl and DNA fragmentation in liver, blood brain and bone marrow cells micronucleus were assayed for using standard protocols. Molecular docking of SAM-dependent methyltransferase in the presence of CHR was conducted. CHR significantly ( p < 0.05) decreased the level of lipid peroxidation, protein carbonyls and DNA fragmentation in blood, liver and brain tissues as against group treated with SA. It also significantly ( p<0.05) reduced the level of micronuclei generated in bone marrow cells. The effects of CHR were shown to be ameliorative, preventive and curative in nature. Furthermore, CHR was able to dock (with binding energy of -24.81 kcal/mol and predicted inhibition kinetic constant (Ki) of 0.959 µM) into the active site of SAM-dependent methyltransferase with strong hydrogen bond and hydrophobic interactions. The study might have unravelled the potentials of CHR against SA-induced chromosomal and DNA damage, which might be due to inhibition of SAM-dependent methyltransferase.