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Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor level, using molecular docking and three-dimensional quantitative structure–activity relationship (3-D QSAR) studies. The bioactive conformations of cathinones were modeled by AutoDock Vina and were used to build structure-based (SB) 3-D QSAR models using the Open3DQSAR engine. Graphical inspection of the results led to the depiction of a 3-D structure analysis-activity relationship (SAR) scheme that could be used as a guideline for molecular determinants by which any untested cathinone molecule can be predicted as a potential 5-HT2AR binder prior to experimental evaluation. The obtained models, which showed a good agreement with the chemical properties of co-crystallized 5-HT2AR ligands, proved to be valuable for future virtual screening campaigns to recognize unused cathinones and similar compounds, such as 5-HT2AR ligands, minimizing both time and financial resources for the characterization of their psychedelic effects. [ABSTRACT FROM AUTHOR]

In this study, we considered some pesticides as active substances within formulations for the protection of plant-based food in the Republic of Serbia in silico, because these pesticides have not often been investigated in this way previously, and in an analytical way, because there are not very many available fast, cheap, and easy methods for their determination in real agricultural samples. Seven pesticides were detected in selected agricultural products (tomatoes, cucumbers, peppers, and grapes) using the QuEChERS methodology and HPLC-DAD. Standard curves for the investigated pesticides (chlorantraniliprole, methomyl, metalaxyl, thiacloprid, acetamiprid, emamectin benzoate, and cymoxanil) show good linearity, with R2 values from 0.9785 to 0.9996. The HPLC-DAD method is fast, and these pesticides can be determined in real spiked samples in less than 15 min. We further characterized the pesticides we found in food based on physicochemical properties and molecular descriptors to predict the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of the compounds. We summarized the data supporting their effects on humans using various computational tools to determine their potential adverse effects. The results of our prediction study show that all of the selected pesticides considered in this study have good oral bioavailability, and those with high toxicity, therefore, could be harmful to human health. Chlorantraniliprole was shown in a molecular docking study as a good starting point for a new Alzheimer's disease drug candidate. [ABSTRACT FROM AUTHOR]

Commercially available pesticides were examined as Mus musculus and Homo sapiens acetylcholinesterase (mAChE and hAChE) inhibitors by means of ligand-based (LB) and structure-based (SB) in silico approaches. Initially, the crystal structures of simazine, monocrotophos, dimethoate, and acetamiprid were reproduced using various force fields. Subsequently, LB alignment rules were assessed and applied to determine the inter synaptic conformations of atrazine, propazine, carbofuran, carbaryl, tebufenozide, imidacloprid, diuron, monuron, and linuron. Afterwards, molecular docking and dynamics SB studies were performed on either mAChE or hAChE, to predict the listed pesticides' binding modes. Calculated energies of global minima (Eglob_min) and free energies of binding (ΔGbinding) were correlated with the pesticides' acute toxicities (i.e., the LD50 values) against mice, as well to generate the model that could predict the LD50s against humans. Although for most of the pesticides the low Eglob_min correlates with the high acute toxicity, it is the ΔGbinding that conditions the LD50 values for all the evaluated pesticides. Derived pLD50 = f(ΔGbinding) mAChE model may predict the pLD50 against hAChE, too. The hAChE inhibition by atrazine, propazine, and simazine (the most toxic pesticides) was elucidated by SB quantum mechanics (QM) DFT mechanistic and concentration-dependent kinetic studies, enriching the knowledge for design of less toxic pesticides. [ABSTRACT FROM AUTHOR]