QSAR studies on a number of pyrrolidin-2-one antiarrhythmic arylpiperazinyls

Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation

The water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are international issues of common concern. The partition coefficients with PCBs between low-density polyethylene (LDPE) and water (K_PE-w) are significant to assess their environmental transport and/or fate in aquatic environment. Even moderately hydrophobic PCBs, however, possess large K_PE-w values, which makes directly experimental measurement labored. Here, based on the combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models are developed to provide a quick estimate of K_PE-w. These models exhibit good goodness-of-fit (R²_adj: 0.919-0.975), robustness (Q²_LOO: 0.870-0.954) and external prediction performances (Q²_ext: 0.880-0.971), providing a speedy feasibility to close data gaps for limited or absent experimental information, especially the RF-2 model. Particularly, an additional experimental verification is performed for models by a rapid and accurate three-phase system (aqueous phase, surfactant micelles and LDPE). The results of the experiments for 16 PCBs show the modeling results agree well with experimental values, within or approaching the residuals of ± 0.3 log unit. Mechanism interpretations imply that the number of chlorine atoms and ortho-substituted chlorines are the great effect parameters for K_PE-w. This result also heightens interest in measuring and predicting the K_PE-w values of chemicals containing halogen atoms in water.

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor

Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure–activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R²) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model’s predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.

Metabolic benefits of 1-(3-(4-(o-tolyl)piperazin-1-yl)propyl)pyrrolidin-2-one: a non-selective α-adrenoceptor antagonist

PURPOSE

Previous studies have shown that several components of the metabolic syndrome, such as hypertension, obesity or imbalanced lipid and carbohydrate homeostasis, are associated with the sympathetic nervous system overactivity. Therefore, the inhibition of the adrenergic nervous system seems to be a reasonable and appropriate therapeutic approach for the treatment of metabolic disturbances. It has been suggested that non-selective adrenoceptor antagonists could be particularly beneficial, since α₁-adrenoceptor antagonists can improve disrupted lipid and carbohydrate profiles, while the inhibition of the α₂-adrenoceptor may contribute to body weight reduction. The aim of the present study was to investigate the metabolic benefits deriving from administration of a non-selective α-adrenoceptor antagonist from the group of pyrrolidin-2-one derivatives. The aim of the present study was to investigate the potential metabolic benefits deriving from chronic administration of a non-selective α-adrenoceptor antagonist, from the group of pyrrolidin-2-one derivatives.

METHODS

The α₁- and α₂-adrenoreceptor affinities of the tested compound-1-(3-(4-(o-tolyl)piperazin-1-yl)propyl)pyrrolidin-2-one had been investigated previously by means of the radioligand binding assay. In the present study, we extended the pharmacological profile characteristics of the selected molecule by additional intrinistic activity assays. Next, we investigated the influence of the tested compound on body weight, hyperglycemia, hypertriglyceridemia, blood pressure in the animal model of obesity induced by a high-fat diet, and additionally we measured the spontaneous activity and body temperature.

RESULTS

The intrinistic activity studies revealed that the tested compound is a potent, non-selective antagonist of α_1B and α_2A-adrenoceptors. After the chronic administration of the tested compound, we observed reduced level of triglycerides and glucose in the rat plasma. Interestingly, the tested did not reduce the body weight and did not influence the blood pressure in normotensive animals. Additionally, the administration of the tested compound did not change the animals' spontaneous activity and body temperature.

CONCLUSION

Non-selective α-adrenoceptor antagonist seems to carry potential benefits in the improvement of the reduction of elevated glucose and triglyceride level. The lack of influence on blood pressure suggests that compounds with such a pharmacological profile may be particulary beneficial for the patients with disturbed lipid and carbohydrate profile, who do not suffer from hypertension. These results are particulary valuable, since currently there are no safe α_2A-adrenoceptor antagonist drugs available in clinical use with the ability to modulate hyperglycemia that would not affect blood pressure.

Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds

Quantitative structure-retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC-HRMS) and hydrophilic interaction chromatography (HILIC-LC-HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP(2), FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R(2)=0.95 for RP and R(2)=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.