QSPR modeling of the logKow and logKoc of polymethoxylated, polyhydroxylated diphenyl ethers and methoxylated-, hydroxylated-polychlorinated diphenyl ethers

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Prediction of phthalate acid esters degradation in soil using QSAR model: A combined consideration of soil properties and quantum chemical parameters

Phthalic acid esters (PAEs) are predominant hazardous substances and endocrine-disrupting compounds to be controlled in soil. The degradation behaviors of PAEs in soil had been long term concerned. Thus, the degradation rate (K) is important for assessing theexposure risk and is of great significance in evaluating the ecological risk of PAEs in soil environment. But by far, quantitative structure activity relationship (QSAR) models for PAEs degradation have rarely been considered in soil environment. In this study, quantum chemical parameters were considered along with soil properties as two kinds of descriptors in QSAR model. A total of 32 logk of PAEs were collected from reference and experiment. Degradation kinetics in soils were determined by pseudo-first order kinetic models. The residual concentration of PAEs in Udic ferrosols and Aquic cambisols suggesting a potential expose risks of PAEs to ecosystem in soil. The QSAR model between logk and quantum chemical parameters revealed that E_HOMO and qC- are two predominant factors in determining logk value. Furthermore,our study further indicated that soil organic matter (SOM) as new predictor contributes more to predict logk values of PAEs during degradation process than pH. Results from this study make a new contribution for methods to predict the degradation of PAEs in soil environment and highlight the potential to evaluate the environmental risks of degradation of PAEs.

Occurrence, distribution and partitioning of polychlorinated dibenzothiophenes (PCDTs) in Chaohu Lake, Southeast China

Polychlorinated dibenzothiophenes (PCDTs) are a class of compounds structurally similar to dioxins that possess various toxicological impacts on living organisms. Unfortunately, information on the levels of PCDTs in freshwater lakes in China is still scarce. In this work, the occurrence of 14 congeners of PCDTs in different matrices (i.e., sediment, suspended particulate matter (SPM), and water) of Chaohu Lake was investigated. It was determined that the concentrations of 14 PCDTs (Σ₁₄PCDTs) in the sediment, SPM, and surface water were 0.40-3.55 ng g^-1 (dry weight, d.w.), 0.38-2.95 ng·g^-1 d.w., and 0.34-2.61 ng L^-1, respectively. The dominant congener found in sediments was 1,2,3,4,7-penta-CDT (19.54%), and 1,3,9-tri-CDT was the predominant congener in SPM (19.13%) and water (20.08%). Medium- and high-chlorinated PCDTs were detected as the major compounds in sediments and SPM. The low-chlorinated PCDTs (e.g., mono-CDTs) have higher relative percentages in the water than those detected in the sediment samples. The annual Σ₁₄PCDT input of the eight main tributaries to Chaohu Lake was 19.90 kg. A strong linear correlation between the Σ₁₄PCDT levels and the organic carbon (OC) content demonstrated that OC had an important influence on the PCDT redistribution in Chaohu Lake. In addition, the organic carbon normalized partitioning coefficient (logK_OC) of PCDTs in the SPM-water system in Chaohu Lake was 1.95-2.49 mL g^-1, and correlations between logK_OC and other typical environment-related properties of PCDTs were established. This study provided useful data on the evaluation of ecological risks of PCDTs in Chaohu Lake.

QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices

Octanol-water partition coefficient (logK_ow) and soil organic carbon content normalized sorption coefficient (logK_oc) values are two important physicochemical properties in the context of bioaccumulation and environmental fate of organic compounds and their environmental risk assessment. Simple, interpretable and easy-to-derive extended topochemical atom (ETA) indices obtained from 2D structural representation of compounds were used for quantitative structure-property relationship (QSPR) modeling of these two endpoints. Linear regression based models developed using only ETA indices show encouraging statistical and validation results. Based on the information obtained from developed QSPR models, we may conclude that molecular volume, branching pattern, presence of hydrophobic Cl atoms, cyclicity/fusion, polar environment, electron density, unsaturation content, hydrogen bonding propensity or hydrogen bond donor atoms, local topology, presence of heteroatoms and aromaticity are crucial factors in controlling the logK_ow and logK_oc values of the compounds. The suggested explanatory features for different classes of chemicals or the whole diverse set can help in safer designing of chemicals, which is one of the primary agenda of the "Green Chemistry" program.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics

The n-octanol/water partition coefficient (logK_ow) is widely used in the environmental, agricultural and pharmaceutical fields for the risk evaluation and application of organic chemicals. In this work, grounded on atomic distribution matrices, a norm index-based QSPR model was built for organic chemicals with 18 kinds of diverse structures. The statistical results (R² = 0.9037, RMSE = 0.4515) showed that the QSPR model for describing the logK_ow of organics was fitted well. Various validation results showed that the model had good robustness, good predictability and wide applicability. These satisfactory results indicated that the model was applicable for the logK_ow description of organic chemicals and that norm descriptors were reliable and general for the description of organic structures. The model was relatively better at describing logK_ow for aromatics, alcohols, nitriles, esters, amides, halogenated compounds, acids and amine compounds. The intensity of spatial branching and the space charge distribution intensity descriptors could have a greater impact on the logK_ow value of a compound.

A novel and simple imidazo[1,2-a]pyridin fluorescent probe for the sensitive and selective imaging of cysteine in living cells and zebrafish

Cysteine (Cys), homocysteine (Hcy) and glutathione (GSH) play many crucial physiological roles in organisms. Their abnormal levels can cause and indicate various diseases. In the present study, a small-molecule fluorescent probe 2-(imidazo[1,2-a]pyridin-2-yl)phenyl acrylate (IPPA) was designed, synthesized and characterized by NMR, FT-IR and HRMS. IPPA can selectively detect Cys over other analytes because of an approximately 76 times enhancement in fluorescence intensity. The limit of detection of IPPA for Cys was 0.33 μM. The pseudo-first-order rate constant of the reaction between IPPA and Cys was approximately 10 times that of the reaction between IPPA and Hcy (KCys 3.18 × 10-3 S-1vs KHcy 4.92 × 10-4 S-1), indicating that Cys can be distinguished from Hcy. In addition, IPPA exhibits strong anti-interference ability, small molecular weight, high efficiency, low toxicity and good cell permeability. It was successfully used in imaging HepG2 cells and zebrafish. The fluorescence response of IPPA for calf serum are powerful proofs for practical application. Therefore, IPPA has high potential for bioassay applications.