SAR and QSAR in Environmental Research最新文献

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Physical hazards of chemical mixtures, associated for example with their fire or explosion risks, are generally characterized using experimental tools. These tests can be expensive, complex, long to perform and even dangerous for operators. Therefore, for several years and especially with the implementation of the REACH regulation, predictive methods like quantitative structure-property relationships have been encouraged as alternatives tests to determine (eco)toxicological but also physical hazards of chemical substances. Initially, these approaches were intended for pure products, by considering a molecular similarity principle. However, additional to those for pure products, QSPR models for mixtures recently appeared and represent an increasing field of research. This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).

2,4-Disubstituted quinoline derivatives were designed based on a 3D-QSAR study, synthesized and evaluated for antimalarial activity. A large dataset of 178 quinoline derivatives was used to perform a 3D-QSAR study using CoMFA and CoMSIA models. PLS analysis provided statistically validated results for CoMFA (r²_ncv = 0.969, q² = 0.677, r²_cv = 0.682) and CoMSIA (r²_ncv = 0.962, q² = 0.741, r²_cv = 0.683) models. Two series of a total of 40 2,4-disubstituted quinoline derivatives were designed with amide (quinoline-4-carboxamide) and secondary amine (4-aminoquinoline) linkers at the -C4 position of the quinoline ring. For the purpose of selecting better compounds for synthesis with good pEC₅₀ values, activity prediction was carried out using CoMFA and CoMSIA models. Finally, a total of 10 2,4-disubstituted quinoline derivatives were synthesized, and screened for their antimalarial activity based on the reduction of parasitaemia. Compound #5 with amide linker and compound #19 with secondary amine linkers at the -C4 position of the quinoline ring showed maximum reductions of 64% and 57%, respectively, in the level of parasitaemia. In vivo screening assay confirmed and validated the findings of the 3D-QSAR study for the design of quinoline derivatives.

Leishmaniasis is a public health concern, especially in Brazil and India. The drugs available for therapy are old, cause toxicity and have reports of resistance. Therefore, this paper aimed to carry out initial structure-activity relationships (applying molecular docking and dynamic simulations) of arylindole scaffolds against the pteridine reductase (PTR1), which is essential target for the survival of the parasite. Thus, we used a series of 43 arylindole derivatives as a privileged skeleton, which have been evaluated previously for different biological actions. Compound 7 stood out among its analogues presenting the best results of average number of interactions with binding site (2.00) and catalytic triad (1.00). Additionally, the same compound presented the best binding free energy (-32.33 kcal/mol) in dynamic simulations. Furthermore, with computational studies, it was possible to comprehend and discuss the influences of the substituent sizes, positions of substitutions in the aromatic ring and electronic influences. Therefore, this study can be a starting point for the structural improvements needed to obtain a good leishmanicidal drug.

The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs' physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.

QSAR studies of a set of previously synthesized azole derivatives tested against human cytomegalovirus (HCMV) were performed using the OCHEM web platform. The predictive ability of the classification models has a balanced accuracy (BA) of 73-79%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA = 76-83%). The models were applied to screen a virtual chemical library with expected activity of compounds against HCMV. The five most promising new compounds were identified, synthesized and their antiviral activities against HCMV were evaluated in vitro. Two of them showed some activity against the HCMV strain AD169. According to the results of docking analysis, the most promising biotarget associated with HCMV is DNA polymerase. The docking of the most active compounds 1 and 5 in the DNA polymerase active site shows calculated binding energies of -8.6 and -7.8 kcal/mol, respectively. The ligand's complexation was stabilized by the formation of hydrogen bonds and hydrophobic interactions with amino acids Lys60, Leu43, Ile49, Pro77, Asp134, Ile135, Val136, Thr62 and Arg137.

Malaria is a lethal disease that claims thousands of lives worldwide annually. The objective of this study was to identify new natural compounds that can target two P. falciparum enzymes; P. falciparum Dihydroorotate dehydrogenase (PfDHODH) and P. falciparum phosphoethanolamine methyltransferase (PfPMT). To accomplish this, e-pharmacophore modelling and molecular docking were employed against PfDHODH. Following this, 1201 natural compounds with docking scores of ≤ -7 kcal/mol were docked into the active site of the second enzyme PMT. The top nine compounds were subjected to further investigation using MM-GBSA free binding energy calculations and ADME analysis. The results revealed favourable free binding energy values better than the references, as well as acceptable pharmacokinetic properties. Compounds ZINC000013377887, ZINC000015113777, and ZINC000085595753 were scrutinized to assess their interaction stability with the PfDHODH enzyme, and chemical stability reactivity using molecular dynamics (MD) simulation and density functional theory (DFT) calculations. These findings indicate that the three natural compounds are potential candidates for dual PfDHODH and PfPMT inhibitors for malaria treatment.

Combating global warming-related climate change demands prompt actions to reduce greenhouse gas emissions, particularly carbon dioxide. Biomass-based biofuels represent a promising alternative fossil energy source. To convert biomass into energy, numerous conversion processes are performed at high pressure and temperature conditions, and the design and dimensioning of such processes requires thermophysical property data, particularly thermal conductivity, which are not always available in the literature. In this paper, we proposed the application of Chemoinformatics methodologies to investigate the prediction of thermal conductivity for hydrocarbons and oxygenated compounds. A compilation of experimental data followed by a careful data curation were performed to establish a database. The support vector machine algorithm has been applied to the database leading to models with good predictive abilities. The support vector regression (SVR) model has then been applied to an external set of compounds, i.e. not considered during the training of models. It showed that our SVR model can be used for the prediction of thermal conductivity values for temperatures and/or compounds that are not covered experimentally in the literature.

The HDAC6 (histone deacetylase 6) enzyme plays a key role in many biological processes, including cell division, apoptosis, and immune response. To date, HDAC6 inhibitors are being developed as effective drugs for the treatment of various diseases. In this work, adequate QSAR models of HDAC6 inhibitors are proposed. They are integrated into the developed application HDAC6 Detector, which is freely available at https://ovttiras-hdac6-detector-hdac6-detector-app-yzh8y5.streamlit.app/. The web application HDAC6 Detector can be used to perform virtual screening of HDAC6 inhibitors by dividing the compounds into active and inactive ones relative to the reference vorinostat compound (IC₅₀ = 10.4 nM). The web application implements a structural interpretation of the developed QSAR models. In addition, the application can evaluate the compliance of a compound with Lipinski's rule. The developed models are used for virtual screening of a series of 12 new hydroxamic acids, namely, the derivatives of 3-hydroxyquinazoline-4(3H)-ones and 2-aryl-2,3-dihydroquinazoline-4(1H)-ones. In vitro evaluation of the inhibitory activity of this series of compounds against HDAC6 allowed us to confirm the results of virtual screening and to select promising compounds V-6 and V-11, the IC₅₀ of which is 0.99 and 0.81 nM, respectively.

The physicochemical characteristics of polycyclic aromatic compounds critical to environmental modelling such as octanol partition coefficients, solubility, lipophilicity, polarity and several equilibrium constants are functions of their underlying molecular structures, prompting the development of mathematical models to predict such characteristics for which experimental results are difficult to obtain. We propose twelve novel descriptors derived from geometric, harmonic and Zagreb degree-based descriptors and then test the effectiveness of these descriptors on a data set consisting of 55 benzenoid hydrocarbons of environmental importance. Our computations show that the proposed descriptors have a good linear correlation and predictive power when compared to the degree and distance type descriptors. We have also derived the QSPR expressions for four properties of a large series of polycyclic aromatics arising from circumscribing coronenes and show that a scaling factor can be deduced to derive physicochemical properties of such series up to 2D graphene sheets.