QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (K_PE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (K_PE-W), LDPE-air (K_PE-A) and LDPE-seawater (K_PE-SW) partition coefficients. These developed models have satisfying predictability (R²_adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSE_tra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q²_ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSE_ext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown K_PE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (K_PUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the K_PUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.

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.

An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials

The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (K_OW ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the K_OW values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Polychlorinated biphenyls (PCBs) residues in commercial pasteurized cows' milk in Tehran, Iran

BACKGROUND

To date, despite the facts that pasteurized milk is the most consumed dairy product in Iran and its consumption has increased almost two fold during the last 10 years, no data are available concerning the concentrations of polychlorinated biphenyls (PCBs) in commercial cow milk in Iran market.

METHODS

This study designed to determine the levels of PCBs in these products and to assess population exposure to PCBs by estimating the daily intakes. Pasteurized cows' milk samples (10 brands) were collected from local markets at two different seasons and analyzed using sensitive and reliable methods.

RESULTS

Based on the results all the indicator PCBs were detected and quantified in all of the samples, the mean ± SD concentration for the sum of the six congeners was 18.92 ± 14.36 ng g^-1 fat. None of the samples surpassed the provisional value established by the EU of 40 ng g^-1 fat. The sum of dioxin-like congeners, expressed as WHO-TEQ was 0.492 pg/g of fat which was considerably lower than the defined limit 3 pg/g fat, set for cow's milk. Furthermore, a similar DL-PCBs profile as other studies was found for analyzed samples. The results indicated that concentrations of DL-PCBs were very low, and all of milk samples were compliant with EC legislation. In addition, seasonal variations were not observed for DL- and NDL-PCBs levels (p values >0.05).

CONCLUSIONS

The estimated dietary intake for target population was 0.06 pg TEQ/kg of body weight/day, much smaller than the amounts declared by the World Health Organization as tolerable daily intake.