Environmentally friendly polychlorinated naphthalenes (PCNs) derivatives designed using 3D-QSAR and screened using molecular docking, density functional theory and health-based risk assessment
Graphical abstract
Introduction
Polychlorinated naphthalenes (PCNs), a group of semi-volatile organochlorine compounds composed of 75 congeners [1], were used extensively in capacitors, transformers, engine oil additives, cable insulation materials, and preservatives before the 1980s [2,3]. However, PCNs are a class of persistent organic pollutant (POP) [4] which are toxic, persistent, lipophilic substances, and demonstrate long-distance migration [5]. PCNs are structurally and toxicologically similar to polychlorinated dibenzo-p-dioxins/dibenzo-furans, which pose risks in terms of embryotoxicity, hepatotoxicity, immunotoxicity, skin damage, teratogenicity and carcinogenicity [1], and seriously threaten the global environment and human health. Therefore, there is worldwide concern about PCNs and extensive research into their presence in the environment is ongoing.
In some polluted areas, the toxic equivalency of PCNs exceeds the contribution of polychlorinated biphenyls to the pollution found in air, sediment, and biological samples [6]. Previous research studies have shown that [[7], [8], [9]] chemical degradation and microbial degradation methods can be used to control PCN pollution in the environment, and research has mainly focused on the mechanisms underlying the degradation pathway of mono-chloronaphthalene and the degradation of industrially produced PCN Halowax formulations. However, research on the microbial degradation of high-chloronaphthalene has not been performed. The current paper uses a molecular modification method to reduce the biological toxicity of PCNs, providing a theoretical basis for the control of high-chloronaphthalene pollution.
In recent years, Quantitative Structure Activity Relationship (QSAR) models, which are based on the concept that the molecular structures greatly influence the chemical/biological properties of molecules, have received a lot of attentions in the area of environment [10]. Several robust and stable 2D-QSAR studies have been established for interpreting the degradation of organic compounds [11,12], but the traditional QSAR method can’t determine the influencing factors of compounds’ biological characteristics. Three-dimensional quantitative structure–activity relationship (3D-QSAR) models can be helpful in visualizing useful structural information and designing new beneficial compounds with altered activity [[13], [14], [15]]. Some substitutes of banned POPs have been studied because of their specific practicability. For example, Penta-BDEs and Octa-BDEs have been gradually banned due to an increasing environmental concern. As an alternative, the production and use of Hexabromocyclododecane (HBCD), 1, 2-bis (2, 4, 6- dibromophenoxy) ethane (BTBPE) and decabromodiphenylethane (DBDPE) are suggested [16]. Jiang Long et al. [17] designed a novel PBDE flame retardant, which was less harmful to the environment, based on QSAR and a 3D pharmacophore model to control the four POP characteristics of PBDEs. However, the study did not evaluate the effects of the new flame retardant on human health. Unlike before, the environmental-friendly PCNs derivatives, which is screened for its POP characteristics, practicability and human health risk, is designed using a 3D-QSAR method.
Health-based risk assessment, which combines a multimedia human exposure model to quantitatively estimate the absorption of pollutants in the human body through various routes and media, is based on a dynamic multimedia fugacity model to simulate the concentration distribution of pollutants in various media, and uses simulation results as basic data. A dynamic multimedia fugacity model can be used to predict the concentration, mass distribution, accumulation trend and residual time of chemicals in various environmental media, and effectively evaluate the risk of pollution to aquatic organisms and human health, combined with exposure and toxicity research data [18,19].
At present, the biological toxicity of PCNs, which is referred to as the DR-CALUX-REP logEC50 [20] value, is mainly relative to 2, 3, 7, 8- tetrachlorodibenzo-p-dioxin (2, 3, 7, 8-TCDD), and is only applicable to some derivatives not being quantitatively studied for biological and human health risks [[21], [22], [23]]. In the current study, QSAR models were constructed using 3D descriptors, in accordance with the experimental values of logEC50 for 14 PCN congeners. Two types of QSAR method were used to predict the logEC50 values of the remaining 61 PCN congeners and to investigate the relationship between the structure of the PCNs and their biological toxicity. The mono-substituted and bis-substituted sites (lower biological toxicity) of PCNs were precisely screened out in accordance with the contour map, resulting in 67 modified derivatives based on CN-70. The POP characteristics and practicality of the new CN-70 derivatives were evaluated, and 21 modified derivatives, which have a lower biological toxicity and maintain or improve the practical properties of CN-70, were selected. In addition, the dynamic multimedia fugacity model was used to simulate the concentration spatial distribution of CN-70 and the new CN-70 derivatives, and then a multimedia exposure model was used to estimate their health effects in humans, and investigate the toxic effects of the new CN-70 derivatives. This theoretical method could be used to study PCN biological toxicity, predict the logEC50 of PCN homologs and modified environmentally friendly derivatives.
Section snippets
Data
3D-QSAR models were analyzed using the SYBYL molecular modeling software package [23]. To establish the 3D-QSAR models, structural parameters and logEC50 values [20] were used as independent and dependent variables, respectively. The whole data set (n = 14) was divided into a training set (containing 11 compounds) for 3D-QSAR model generation and a test set (containing four compounds) for model validation [24,25]. The target compound (CN-70) was present in both the training set and the test
Evaluation and validation of the CoMFA and CoMSIA models for PCN biological toxicity
Table 1, Table 2 show the contribution rate of the molecular field and the statistical results of the obtained models, respectively. These results illustrate that the two models have suitable predictive abilities and stability [41]. The predicted values and relative errors of the 10 compounds (Table 3) show that the relative errors of the test set are all less than 10%, which is acceptable. The CoMFA and CoMSIA analysis revealed that the electrostatic field was the major contributor to the logEC
Conclusions
In summary, the CoMFA and CoMSIA models showed a satisfactory fitting ability and an acceptable predictive ability for the logEC50 values of the remaining 61 PCNs and enabled a modification of CN-70 to reduce the logEC50. As a consequence, five new CN-70 derivatives with a significant decrease both in biological toxicity and risk were selected as novel CN-70 derivatives. There was not a significant difference observed between the stability, insulativity and flame retardancy of the new modified
Acknowledgments
This project was supported by the Fundamental Research Funds for the Central Universities in 2013 (JB2013146) and the Key Projects in the National Science & Technology Pillar Program in the Eleventh Five-Year Plan Period (2008BAC43B01). We thank Conn Hastings, PhD, from Liwen Bianji, Edanz Editing China, for editing
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