No abstract

Silver nanoparticles (Ag NPs) are interesting nanotechnology materials with borderless applications in medical science, environmental science, material science and are also used in various kinds of industrial household products. Increasing usage of Ag NPs every year leads to increased risk of nanomaterial contamination in the environment, especially natural water sources with harmful effects in aquatic animals, and to ecosystem disruption. In the present study, Ag NPs toxicity was determined using brine shrimp Artemia salina as a model. A total of 10 adult brine shrimps were incubated in solutions containing various Ag NPs concentrations for 24[Formula: see text]h at room temperature. Percentages of brine shrimp dead were recorded and calculated to determine the lethal concentration. We found the LC[Formula: see text] of Ag NPs is 3521.13[Formula: see text]mg/L. Histopathology study was done using survival brine shrimps after 24[Formula: see text]h of incubation with 25% LC[Formula: see text] Ag NPs concentration. Tissue processing and H&E staining to remark pathological areas with emphasis on intestinal tract, were also done. Ag NPs obstruction was found in intestinal lumen. The intestinal epithelial cells showed hyperplasia and blebbing, increased mucous thickening, detachment from basement muscle lining, and necrosis area. These findings indicated the effect of Ag NPs and their negative impact on aquatic animals which might lead to further disruption of ecosystem.

Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There have been numerous studies to develop antagonists against androgen receptor (AR), a crucial therapeutic target for PCa. This study is a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure−activity relationship, and landscape of human AR antagonists. There are 1678 molecules as final data sets. Chemical space visualization by physicochemical property visualization has demonstrated that molecules from the potent/active class generally have a mildly smaller molecular weight (MW), octanol−water partition coefficient (log P), number of hydrogen-bond acceptors (nHA), number of rotatable bonds (nRot), and topological polar surface area (TPSA) than molecules from intermediate/inactive class. The chemical space visualization in the principal component analysis (PCA) plot shows significant overlapping distributions between potent/active class molecules and intermediate/inactive class molecules; potent/active class molecules are intensively distributed, while intermediate/inactive class molecules are widely and sparsely distributed. Murcko scaffold analysis has shown low scaffold diversity in general, and scaffold diversity of potent/active class molecules is even lower than intermediate/inactive class molecules, indicating the necessity for developing molecules with novel scaffolds. Furthermore, scaffold visualization has identified 16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3, 4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due to their high scaffold enrichment factor values. Based on scaffold analysis, their local structure−activity relationships (SARs) were investigated and summarized. In addition, the global SAR landscape was explored by quantitative structure−activity relationship (QSAR) modelings and structure−activity landscape visualization. A QSAR classification model incorporating all of the 1678 molecules stands out as the best model from a total of 12 candidate models for AR antagonists (built on PubChem fingerprint, extra trees algorithm, accuracy for training set: 0.935, 10-fold cross-validation set: 0.735 and test set: 0.756). Deeper insights into the structure−activity landscape highlighted a total of seven significant activity cliff (AC) generators (ChEMBL molecule IDs : 160257, 418198, 4082265, 348918, 390728, 4080698, and 6530), which provide valuable SAR information for medicinal chemistry. The findings in this study provide new insights and guidelines for hit identification and lead optimization for the development of novel AR antagonists.