SAR and QSAR in Environmental Research最新文献

Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.

The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q²). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.

A study of Quantitative Structure Activity Relationship (QSAR) was performed to assess the possible adverse effects of 25 pharmaceuticals commonly found in the Brazilian water compartments and to establish a ranking of environmental concern. The occurrence (O), the persistence (P), the mobility (M), and the toxicity (T) of these compounds in the Brazilian drinking water reservoirs were evaluated. Moreover, to verify the predicted OPMT dataset outcomes, a quality index (QI) was also developed and applied. The main results showed that: (i) after in silico predictions through VEGA QSAR, 19 from 25 pharmaceuticals consumed in Brazil were classified as persistent; (ii) moreover, after in silico predictions through OPERA QSAR, 15 among those 19 compounds considered persistent, were also classified as mobile or very mobile. On the other hand, the results of toxicity indicate that only 9 pharmaceuticals were classified with the highest toxicity level. Ultimately, the QI of 7 from 25 pharmaceuticals were categorized as 'optimal'; 15 pharmaceuticals were categorized as 'good'; and only 3 pharmaceuticals were categorized as 'regular'. Therefore, based on the QI criteria used, it is possible to assume that this OPMT prediction dataset had a good reliability. Efforts to reduce emissions of OPMT-pharmaceuticals in Brazilian drinking water reservoirs are encouraged.

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.