A cyclodextrin polymer membrane-based passive sampler for measuring triclocarban, triclosan and methyl triclosan in rivers.

In recent years, extensive attention has been paid to the passive sampling technology of diffusive gradients in thin films (DGT) due to its growing application in the measurement of a widening variety of compounds. Within any DGT device, the binding phase is a key component, and seeking novel binding phases is an issue worth studying. Cyclodextrin polymer, as a green and eco-friendly material, may be a good choice for measuring organic chemicals. In this study, a novel DGT sampler with cyclodextrin polymer membrane (CDPM) as the binding phase was developed for measuring the concentrations of triclosan, triclocarban and methyl triclosan. Firstly, the type and content of cyclodextrin used in CDPM was optimized, and a series of tests showed that CDPM had good hydrophilicity, thermal stability, fast uptake rate and sufficient uptake capacity, thus CDPM was determined to be suitable for use as the binding phase of DGT sampler. Moreover, the sampling rates of this DGT sampler were not influenced by ionic strength and dissolved organic matter, making it feasible for in situ monitoring of compounds in the field. Hence, we deployed the developed DGT sampler in the Qinhuai and Jiuxiang Rivers to measure the concentrations of three compounds. We also collected water samples and processed them with the solid phase extraction (SPE) method. Results indicated that there was no significant difference between the DGT-measured and the SPE-measured concentrations for each compound, which confirmed the reliability of this DGT sampler for monitoring the concentrations of compounds in natural waters.

Development of QSAR model for predicting the inclusion constants of organic chemicals with α-cyclodextrin.

Solubility is a crucial limiting factor in pharmaceutical research and contaminated site remediation. Cyclodextrin, with its structure of hydrophilic exterior and hydrophobic cavity, has a potential ability to enhance the hydrophobic chemical's solubility through the formation of host-guest complex. The stability of host-guest complex is often quantified by the inclusion constant. In this study, the logarithm of 1:1 α-cyclodextrin inclusion constants (log Kα) for 195 organic chemicals was collected. With this parameter as the endpoint, a quantitative structure-activity relationship (QSAR) model was developed using DRAGON descriptors and stepwise multiple linear regression analysis. The model statistics parameters indicated that the established model had a good determination coefficient of 0.857, a high cross-validation coefficient of 0.835, a low root mean square error of 0.380, together with the acceptable results of external validation, which indicate a satisfactory goodness-of-fit, robustness, and predictive ability of the model. Based on the screened eight descriptors, we propose an appropriate mechanism interpretation for the inclusion interaction. Additionally, the applicability domain of the current model was characterized by the Euclidean distance-based method and Williams plot, and results indicated that the model covered a large number of structurally diverse chemicals belonging to 13 different classes. Comparing with the previous reported models, this model has obvious advantages with a larger dataset, a higher value of correlation coefficient, and a wider application domain.

Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor.

Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC 20 and log EC 50) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R 2) of 0.702-0.749 and the cross-validation and external validation coefficient (Q 2) of 0.643-0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R 2 > 0.6, Q 2 > 0.5. The small root mean square error (RMSE) of 0.254-0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.

Development of predictive models for predicting binding affinity of endocrine disrupting chemicals to fish sex hormone-binding globulin.

Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) exerting disrupting endocrine function. However, this mechanism has not received enough attention compared with that of hormones receptors and synthetase. Recently, we have explored the interaction between EDCs and sex hormone-binding globulin of human (hSHBG). In this study, interactions between EDCs and sex hormone-binding globulin of eight fish species (fSHBG) were investigated by employing classification methods and quantitative structure-activity relationships (QSAR). In the modeling, the relative binding affinity (RBA) of a chemical with 17ß-estradiol binding to fSHBG was selected as the endpoint. Classification models were developed for two fish species, while QSAR models were established for the other six fish species. Statistical results indicated that the models had satisfactory goodness of fit, robustness and predictive ability, and that application domain covered a large number of endogenous and exogenous steroidal and non-steroidal chemicals. Additionally, by comparing the log RBA values, it was found that the same chemical may have different affinities for fSHBG from different fish species, thus species diversity should be taken into account. However, the affinity of fSHBG showed a high correlation for fishes within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes and Siluriformes), thus the fSHBG binding data for one fish species could be used to extrapolate other fish species in the same Order.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water.

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (Kpew). For chemicals with the octanol-water partition coefficient (log Kow) <8, a TLSER model with Vx (McGowan volume) and qA- (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R2) and cross-validated coefficient (Q2). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log KOW>8, a TLSER model with Vx and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water.