Thyroid hormone disrupting chemicals (THDCs) interfere with the thyroid hormone system and may induce multiple severe physiological disorders. Indoor dust ingestion is a major route of THDCs exposure in humans, and one of the molecular targets of these chemicals is the hormone transporter transthyretin (TTR). To virtually screen indoor dust contaminants and their metabolites for THDCs targeting TTR, we developed a quantitative structure-activity relationship (QSAR) classification model. The QSAR model was applied to an in-house database including 485 organic dust contaminants reported from literature data and their 433 in silico derived metabolites. The model predicted 37 (7.6%) dust contaminants and 230 (53.1%) metabolites as potential TTR binders. Four new THDCs were identified after testing 23 selected parent dust contaminants in a radio-ligand TTR binding assay; 2,2',4,4'-tetrahydroxybenzophenone, perfluoroheptanesulfonic acid, 3,5,6-trichloro-2-pyridinol, and 2,4,5-trichlorophenoxyacetic acid. These chemicals competitively bind to TTR with 50% inhibition (IC50) values at or below 10 μM. Molecular docking studies suggested that these THDCs interacted similarly with TTR via the residue Ser117A, but their binding poses were dissimilar to the endogenous ligand T4. This study identified new THDCs using an in silico approach in combination with bioassay testing and highlighted the importance of metabolic activation for TTR binding.

An artificial neural network was employed to predict the cellular uptake of 109 magnetofluorescent nanoparticles (NPs) in pancreatic cancer cells on the basis of quantitative structure activity relationship method. Six descriptors chosen by combining self-organizing map and stepwise multiple linear regression (MLR) techniques were used to correlate the nanostructure of the studied particles with their bioactivity using MLR and multilayered perceptron neural network (MLP-NN) modeling techniques. For the MLR and MLP-NN models, the correlation coefficient was 0.769 and 0.934, and the root-meansquare error was 0.364 and 0.150, respectively. The results obtained after a leave-many-out cross-validation test revealed the credibility of MLP-NN for the prediction of cellular uptake of NPs. In addition, sensitivity analysis of MLP-NN model indicated that the number of hydrogen-bond donor sites in the organic coating of a NP is the predominant factor responsible for cellular uptake.

The identification of industrial chemicals, which may cause developmental effects, is of great importance for an early detection of hazardous chemicals. Accordingly, categorical quantitative structure-activity relationship (QSAR) models were developed, based on developmental toxicity profile data for zebrafish from the ToxCast Phase I testing, to predict the toxicity of a large set of high and low production volume chemicals (H/LPVCs). QSARs were created using linear (LDA), quadratic, and partial least squares-discriminant analysis with different chemical descriptors. The predictions of the best model (LDA) were compared with those obtained by the freely available QSAR model VEGA, created based on a dataset with a different chemical domain. The results showed that despite similar accuracy (AC) of both models, the LDA model is more specific than VEGA and shows a better agreement between sensitivity (SE) and specificity (SP). Applying a 90% confidence level on the LDA model led to even better predictions showing SE of 0.92, AC of 0.95, and geometric mean of SE and SP (G) of 0.96 for the prediction set. The LDA model predicted 608 H/LPVCs as toxicants among which 123 chemicals fall inside the AD of the VEGA model, which predicted 112 of those as toxicants. Among the 112 chemicals predicted as toxic H/LPVCs, 23 have been previously reported as developmental toxicants. The here presented LDA model could be used to identify and prioritize H/LPVCs for subsequent developmental toxicity assessment, as a screening tool of potential developmental effects of new chemicals, and to guide synthesis of safer alternative chemicals.

For a better understanding of species-specific relative effect potencies (REPs), responses of dioxin-like compounds (DLCs) were assessed. REPs were calculated using chemical-activated luciferase gene expression assays (CALUX) derived from guinea pig, rat, and mouse cell lines. Almost all 20 congeners tested in the rodent cell lines were partial agonists and less efficacious than 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). For this reason, REPs were calculated for each congener using concentrations at which 20% of the maximal TCDD response was reached (REP20TCDD). REP20TCDD values obtained for PCDD/Fs were comparable with their toxic equivalency factors assigned by the World Health Organization (WHO-TEF), while those for PCBs were in general lower than the WHO-TEF values. Moreover, the guinea pig cell line was the most sensitive as indicated by the 20% effect concentrations of TCDD of 1.5, 5.6, and 11.0 pM for guinea pig, rat, and mouse cells, respectively. A similar response pattern was observed using multivariate statistical analysis between the three CALUX assays and the WHO-TEFs. The mouse assay showed minor deviation due to higher relative induction potential for 2,3,7,8-tetrachlorodibenzofuran and 2,3,4,6,7,8-hexachlorodibenzofuran and lower for 1,2,3,4,6,7,8-heptachlorodibenzofuran and 3,3',4,4',5-pentachlorobiphenyl (PCB126). 2,3,7,8-Tetrachlorodibenzofuran was more than two times more potent in the mouse assay as compared with that of rat and guinea pig cells, while measured REP20TCDD for PCB126 was lower in mouse cells (0.05) as compared with that of the guinea pig (0.2) and rat (0.07). In order to provide REP20TCDD values for all WHO-TEF assigned compounds, quantitative structure-activity relationship (QSAR) models were developed. The QSAR models showed that specific electronic properties and molecular surface characteristics play important roles in the AhR-mediated response. In silico derived REP20TCDD values were generally consistent with the WHO-TEFs with a few exceptions. The QSAR models indicated that, e.g., 1,2,3,7,8-pentachlorodibenzofuran and 1,2,3,7,8,9-hexachlorodibenzofuran were more potent than given by their assigned WHO-TEF values, and the non-ortho PCB 81 was predicted, based on the guinea-pig model, to be 1 order of magnitude above its WHO-TEF value. By combining in vitro and in silico approaches, REPs were established for all WHO-TEF assigned compounds (except OCDD), which will provide future guidance in testing AhR-mediated responses of DLCs and to increase our understanding of species variation in AhR-mediated effects.

In this study, quantitative structure-retention relationship (QSRR) was used for the prediction of Kováts retention indices of 180 alkylphenols and their derivatives using the multiple linear regression (MLR) and support vector machine (SVM). After the calculation of some molecular descriptors for all molecules, the data set was randomly divided into training and test sets. The diversity of training and test sets was examined by molecular diversity validation test. Then stepwise MLR was used for the selection of the most important descriptors and development of MLR models. Descriptors which appeared in these QSRR models are number of H atoms, relative number of O atoms, Balaban index, relation yz-shadow/yz-rectangle and partial charges hydrogen bond donor atoms HDCA 2 index. These descriptors were used as inputs for developing the SVM model. After optimizing the SVM parameters, it was used for the calculation of chromatographic retention of interest molecules. The values of SE in calculation of Kováts retention indices for training and test sets are 0.34 and 0.63, respectively, for MLR model and 0.35 and 0.63, respectively, for SVM model. The overall values of average absolute relative error were 13.24 and 13.83 for MLR and SVM models, respectively. In addition, the cross-validation tests were performed to further examine the obtained model. IntroductionAlkylphenols (APs) are a family of chemicals that are mainly used as raw materials in the production of varieties of industrial products such as surfactants, detergents, phenolic resins, polymer additives and lubricants [1,2]. Long-chained branched para-substituted isomers of APs are known to have oestrogenic activity and therefore represent an environmental problem [3][4][5]. APs are present in raw oil and those of shorter alkyl chain may enter into the environment via produced water. Produced water is defined as the water that comes up with oil and gas from sea bed reservoirs, separated on the platform from the oil and discharged into the sea, from offshore oil installations [6]. Due to biohazards in aquatic environment of APs, the detection and quantification of them are very important. Few methods have been developed to determine APs in the aquatic environment and most of them invariably comprise three steps, namely, extraction/clean-up/extract evaporation, chromatographic separation and mass spectroscopic detection [7][8][9][10][11][12][13]. The identification of these compounds by chromatography was done on the basis of their retention parameters. On a GC retention index scale, the retention of a compound is described in relation to the retention of a series of homologues. The most common retention system is the Kováts retention indices [14,15], where n-alkanes are applied as reference compounds. Retention indices are independent of carrier gas flow, column dimensions and phase ratios [16]. Special retention index for alkylphenol Abbreviations: AARD, average absolute relative deviation; AP, alkylphenol; APRI X , retention index for alkylphenol; BI, Balaban...