Ranking and similarity for quantitative structure–retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons

Spatial pattern and compositional distribution of organochlorine pesticides in the black soil region of Shenyang

Organochlorine pesticides (OCPs) are persistent organic pollutants (POPs) prevalent in soils with carcinogenic, teratogenic and mutagenic hazards that are commonly found in soils and remain in the environment even though they have been banned. In order to fill the gap of fewer studies after the ban, soil samples were collected from 308 agricultural fields of cash crops and grain crops in the black soil area of Shenyang City (Liaozhong District, Faku County, Xinmin City and Kangping County) in this study. The aim was to determine, the use and distribution characteristics of OCPs in agricultural soils in the black soil region of Shenyang City. Compositional analysis showed that the detection rate of banned OCPs in agricultural soils was 71.75%, including contaminants such as technical dichloro-diphenyl-trichloroethane (DDT), chlordane and hexachlorobenzene (HCB), which were widely distributed in Liaozhong District, Faku County, Xinmin City and Kangping County, with 45.25% of the fields having compounded contamination of OCPs, and several areas were involved in the fresh inputs of contaminants such as technical DDT. Among them, Kangping County and Faku County are more seriously polluted, with 66.29% and 60.71% of OCPs exceeding the standard. Soil OCPs is more serious in cabbage and rice farmland among cash and food crop farmland. Based on Chinese policy on control, prevention and other pesticide management measures, it was concluded that the framework should be strengthened to prevent further illegal use of banned OCPs.

Organochlorine pesticide contamination of soils and dust from an urban environment in the Niger Delta of Nigeria

The concentrations, sources, and risk of twenty organochlorine pesticides (OCPs) in soils and dusts from a typical urban setting in the Niger Delta of Nigeria were examined. The Σ20 OCP concentrations (ng g-1) varied from 4.49 to 150 with an average value of 32.6 for soil, 4.67 to 21.5 with an average of 11.7 for indoor dust, and 1.6 to 96.7 with an average value of 23.5 for outdoor dust. The Σ20 OCP concentrations in these media were in the order: soil > outdoor dust > indoor dust, which was in contrast with the order of the detection frequency, i.e., indoor dust (95 to 100 %) > soil (60 to 90 %) > outdoor dust (30 to 80 %). The concentrations of the different OCP classes in these media followed the order: aldrin + dieldrin + endrin and its isomers (Drins) > chlordanes > dichlorodiphenyltrichloroethane (DDTs) > hexachlorocyclohexane (HCHs) > endosulfans for outdoor dust and soil, while that of the indoor dust followed the order: Drins > chlordanes > endosulfans > DDTs > HCHs. The cancer risk values for human exposure to OCPs in these sites exceeded 10-6 which indicates possible carcinogenic risks. The sources of OCPs in these media reflected both past use and recent inputs.

Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning

Given the difficult of experimental determination, quantitative structure-property relationship (QSPR) and deep learning (DL) provide an important tool to predict physicochemical property of chemical compounds. In this paper, partial least squares (PLS), genetic function approximation (GFA), and deep neural network (DNN) were used to predict the Lee retention index (Lee-RI) of PAHs in SE-52 and DB-5 stationary phases. Four molecular descriptors, molecular weight (MW), quantitative estimate of drug-likeness (QED), atomic charge weighted negative surface area (Jurs_PNSA_3), and relative negative charge (Jurs_RNCG) were selected to construct regression models based on genetic algorithm. For SE-52, PLS model showed best prediction power, followed by DNN and GFA. The relative error (RE), root mean square error (RMSE), and regression coefficient (R² ) of best PLS regression model are 1.228%, 5.407, and 0.980. For DB-5, DNN model showed best prediction power, followed by GFA and PLS. The RE, RMSE and R² of best DNN regression model for DB-5-1 and DB-5-2 are 1.058%, 4.325%, 0.976%, 0.821%, 3.795%, and 0.970%, respectively. The three regression models not only show good predictive ability, but also highlight the stability and ductility of the models.

Improvement of Carrot Accelerated Solvent Extraction Efficacy Using Experimental Design and Chemometric Techniques

Human studies have demonstrated the multiple health benefits of fruits and vegetables. Due to its high fiber, mineral and antioxidant content, carrot is an ideal source for the development of nutraceuticals or functional ingredients. Current research assesses accelerated solvent extraction (ASE) traits which affect the antioxidant qualities of carrot extract using response surface methodology (RSM), hierarchical cluster analysis (HCA), and the sum of ranking differences (SRD). A mixture of organic solvents, acetone, and ethanol with or without the addition of 20% water was applied. The total carotenoid and polyphenol contents in extracts, as well as their scavenging activity and reducing power, were used as responses for the optimization of ASE extraction. RSM optimization, in the case of 20% water involvement, included 49% of acetone and 31% of ethanol (Opt1), while in the case of pure organic solvents, pure ethanol was the best choice (Opt2). The results of HCA clearly pointed out significant differences between the properties of extracts with or without water. SRD analysis confirmed ethanol to be optimal as well. RSM, HCA, and SRD analysis confirmed the same conclusion—water in the solvent mixture can significantly affect the extraction efficacy, and the optimal solvent for extracting antioxidants from carrot by ASE is pure ethanol.

Assessment of epigenotoxic profiles of Dongjiang River: A comprehensive of chemical analysis, in vitro bioassay and in silico approach

This research explored the occurrence, epigenetic toxic profiling and main toxic pollutants of POPs in surface water of Dongjiang River, southern China. The concentrations of selected POPs including polycyclic aromatic hydrocarbons (PAHs), endocrine disrupting chemicals (EDCs), phthalate esters (PAEs) and polybrominated diphenyl ethers (PBDEs) of surface water from 18 sites were investigated. ∑₁₆PAHs and ∑₄EDCs were at a moderate level, while ∑₆PAEs and ∑₆PBDEs had low pollution levels. PAHs, EDCs and PAEs showed higher concentrations in dry season than those in wet season, and the loading of selected POPs in tributaries was higher than those in mainstream due to intensive manufactures and lower runoff volume. Moreover, activities of DNA methyltransferase (DNMT)1, histone deacetylase (HDAC2, HDAC8) were confirmed to be sensitive indicators for epigenetic toxicity. The DNMT1-mediated epigenetic equivalency toxicity of organic extracts in Dongjiang River were more serious than those of HDAC2 and HDAC8. Correlation analysis shown binding affinity between POPs and DNMT1, HDAC2 and HDAC8 could be regarded as toxic equivalency factors. Risk assessment suggested that 4-nonylphenol and bisphenol A were the largest contributors to epigenetic risk. This study is the first attempt to quantify epigenetic toxicity and epigenetic risk evaluation of river water.

Evaluation of antimicrobial activity and retention behavior of newly synthesized vanilidene derivatives of Meldrum's acids using QSRR approach

Increased antimicrobial resistance together with the lack of new antimicrobial drugs suggest on an urgent need for new therapeutics in this field. Vanilidene derivatives of Meldrum's acid present one of the possible approaches. In this work lipophilicity of 13 vanilidene derivatives of Meldrum's acid as well as their predicted antimicrobial activity towards several characteristic species has been evaluated. 10 vanilidene derivatives have been previously synthesized and 3 new compounds are synthetized afterwards following the same procedure. These selected 13 candidates were examined using thin layer chromatography in two different solvent systems. Gained retention parameters were a starting point for further Quantitative Structure Property Relationships (QSRR) studies in which minimum inhibitory concentration (MIC) for Candida albicans, Trichoderma viride, Penicillium italicum, Fuscarium oxysporum, Pseudomonas aeruginosa and Escherichia coli were determined. Statistically significant QSRR models were established and clustering of the compounds was performed with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Absorption, Distribution, Metabolism, and Excretion (ADME) properties of investigated molecules were subjected to sum of ranking differences (SRD) analysis in order to explore their pharmacokinetic properties. SRD analysis was also performed for the ranking of the established QSRR models. It was shown that compounds 6, 8 and 9 possess a significant antimicrobial activity, satisfied ADME properties and these candidates should be further optimized in order to utilize unexplored potential of Meldrum's acid in synthesis of novel antifungal compounds.

Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods

This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pančevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure - sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques.

Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids

Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD₂ , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD₂ and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc.

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters

Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

Chemometric evaluation of the column classification system during the pharmaceutical analysis of lamotrigine and its related substances

This paper investigates the performance of a column classification system developed at the Katholieke Universiteit Leuven applied to pharmaceutical chromatographic analyses. The liquid chromatography assay of lamotrigine and related compounds was carried out according to the method prescribed in the European Pharmacopoeia monograph, using 28 brands of stationary phases. A ranking was built based on the F_KUL value calculated against the selected reference column, then compared with the column test performance established for the stationary phases studied. Therefore, the system suitability test prescribed by the European Pharmacopoeia in order to distinguish between suitable or unsuitable columns for this analysis was evaluated. Moreover, it was examined whether the classes of the stationary phases, determined using test parameter results, contain either suitable or unsuitable supports for the lamotrigine separation. This assay was performed using chemometric a technique, namely factor analysis.

Figure

Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives

One of the most important physicochemical parameters of a molecule that determines its bioactivity is its lipophilicity. Cluster analysis (CA), principal component analysis (PCA), and sum of ranking differences (SRD) were used to compare the lipophilic parameters of twenty phenylacetamide derivatives, obtained experimentally as chromatographic retention data in the presence of different solvents and calculated by different mathematical methods. All the applied methods of multivariate analysis gave approximately similar grouping of the studied lipophilic parameters. In the attempt to group the investigated compounds in respect of their lipophilicity, the obtained results appeared to be dependent on the applied chemometric method. The CA and PCA, grouped the compounds on the basis of the nature of the substituents R1 and R2, indicating that they determine to a great extent the lipophilicity of the investigated molecules. Unlike them, the SRD method could not be used to group the studied compounds on the basis of their lipophilic character.