Prediction of Soil Sorption Coefficient of a Diverse Set of Organic Chemicals From Molecular Structure

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Photo-Uncaging of a Microtubule-Targeted Rigidin Analogue in Hypoxic Cancer Cells and in a Xenograft Mouse Model

Marine alkaloid rigidins are cytotoxic compounds known to kill cancer cells at nanomolar concentrations by targeting the microtubule network. Here, a rigidin analogue containing a thioether group was "caged" by coordination of its thioether group to a photosensitive ruthenium complex. In the dark, the coordinated ruthenium fragment prevented the rigidin analogue from inhibiting tubulin polymerization and reduced its toxicity in 2D cancer cell line monolayers, 3D lung cancer tumor spheroids (A549), and a lung cancer tumor xenograft (A549) in nude mice. Photochemical activation of the prodrug upon green light irradiation led to the photosubstitution of the thioether ligand by water, thereby releasing the free rigidin analogue capable of inhibiting the polymerization of tubulin. In cancer cells, such photorelease was accompanied by a drastic reduction of cell growth, not only when the cells were grown in normoxia (21% O₂) but also remarkably in hypoxic conditions (1% O₂). In vivo, low toxicity was observed at a dose of 1 mg·kg^-1 when the compound was injected intraperitoneally, and light activation of the compound in the tumor led to 30% tumor volume reduction, which represents the first demonstration of the safety and efficacy of ruthenium-based photoactivated chemotherapy compounds in a tumor xenograft.

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors

The soil sorption partition coefficient logK_oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK_oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK_oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.

Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes

Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (Kd). Experimental Kd values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log Kd to log Kow for each charge class showed weak correlations (maximum R2=0.51 for positively charged) with no overall correlation for the combined dataset (R2=0.04). Weaker correlations were found when relating log Kd to log Dow. Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R2=0.62-0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure Kd limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of Kd would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments.

In silico model for predicting soil organic carbon normalized sorption coefficient (K(OC)) of organic chemicals

As a kind of in silico method, the methodology of quantitative structure-activity relationship (QSAR) has been shown to be an efficient way to predict soil organic carbon normalized sorption coefficients (KOC) values. In the present study, a total of 824 logKOC values were used to develop and validate a QSAR model for predicting KOC values. The model statistics parameters, adjusted determination coefficient (R(2)adj) of 0.854, the root mean square error (RMSE) of 0.472, the leave-one-out cross-validation squared correlation coefficient (Q(2)LOO) of 0.850, the external validation coefficient Q(2)ext of 0.761 and the RMSEext of 0.558 were obtained, which indicate satisfactory goodness of fit, robustness and predictive ability. The squared Moriguchi octanol-water partition coefficient (MLOGP2) explained 66.5% of the logKOC variance. The applicability domain of the current model has been extended to emerging pollutants like polybrominated diphenyl ethers, perfluorochemicals and heterocyclic toxins. The developed model can be used to predict the compounds with various functional groups including C=C, -C≡C-, -OH, -O-, -CHO, C=O, -C=O(O), -COOH, -C6H5, -NO2, -NH2, -NH-, N-, -N-N-, -NH-C(O)-NH-, -O-C(O)-NH2, -C(O)-NH2, -X(F, Cl, Br, I), -S-, -SH, -S(O)2-, -OS(O)2-, -NH-S(O)2-, (SR)2PH(OR)2 and Si.

Identification of sensitive parameters in the modeling of SVOC reemission processes from soil to atmosphere

Semi-volatile organic compounds (SVOCs) are subject to Long-Range Atmospheric Transport because of transport-deposition-reemission successive processes. Several experimental data available in the literature suggest that soil is a non-negligible contributor of SVOCs to atmosphere. Then coupling soil and atmosphere in integrated coupled models and simulating reemission processes can be essential for estimating atmospheric concentration of several pollutants. However, the sources of uncertainty and variability are multiple (soil properties, meteorological conditions, chemical-specific parameters) and can significantly influence the determination of reemissions. In order to identify the key parameters in reemission modeling and their effect on global modeling uncertainty, we conducted a sensitivity analysis targeted on the 'reemission' output variable. Different parameters were tested, including soil properties, partition coefficients and meteorological conditions. We performed EFAST sensitivity analysis for four chemicals (benzo-a-pyrene, hexachlorobenzene, PCB-28 and lindane) and different spatial scenari (regional and continental scales). Partition coefficients between air, solid and water phases are influent, depending on the precision of data and global behavior of the chemical. Reemissions showed a lower variability to soil parameters (soil organic matter and water contents at field capacity and wilting point). A mapping of these parameters at a regional scale is sufficient to correctly estimate reemissions when compared to other sources of uncertainty.

Molecular properties affecting the adsorption coefficient of pesticides from various chemical families

Forty pesticides were selected in function of their chemical families and their physico-chemical properties to represent a wide range of pesticide properties. Adsorption of these pesticides was studied on two soils by batch experiments. The two soils differed largely in organic matter and calcite contents. Distribution coefficient Kd was determined for each pesticide on the two soils. Adsorption was higher for the soil having the highest organic matter content and the lowest calcite content. In order to identify pesticide properties governing retention, eight molecular descriptors were determined from three-dimensional (3D) structure of molecules. Class-specific quantitative structure properties relationship (QSPR) soil adsorption models using one and two parameters were developed from experimental Kd. Three properties seemed to influence most retention of pesticides: hydrophobicity, solubility, and polarisability. Models combining these properties were suggested and discussed.

Molecular properties affecting the adsorption coefficient of phenylurea herbicides

The adsorption of 12 pesticides of the phenylurea family was studied by batch experiments in order to determine the adsorption coefficient, K d. The study was conducted in two soils chosen for their differences in organic matter and calcite contents. K d pesticide adsorption coefficients were higher for soil S1 than for soil S2 due to the presence of a higher organic matter content and a lower calcite content in soil S1. To identify pesticide properties governing retention, 18 molecular descriptors were considered. Class-specific quantitative structure-property relationship (QSPR) soil sorption models using one, two, and three descriptors were developed from our experimental data using linear regressions. One of the aims of this work was to check whether QSPR models that did not include literature values of K ow were able to predict K d coefficients in satisfactory agreement with our experimental data. The influence of the level of theory in determining K ow and polarisability predictors on the predictive performance of the model was also examined by comparing quantum chemistry and empirical (QikProp) approaches. The one-descriptor model using "quantum" polarisability α was found to perform almost as well as or better than the other models.

Assessment of quantitative structural property relationships for prediction of pharmaceutical sorption during biological wastewater treatment

In this study, we critically examined the available data related to pharmaceutical (PhAC) sorption in biological treatment processes. Using these data, we developed and assessed single and polyparameter quantitative structural activity models to better understand the role of sorption in PhAC attenuation. In contrast to other studies, our analysis suggests that values of the sorption coefficient (KD) are poorly correlated to single parameter models employing logKOW or the apparent partition coefficient (i.e., KOW corrected to the experimental pH). Results from the development of polyparameter models suggest that the range of functional moieties typically incorporated in PhAC molecules offers a diverse set of interactions between PhAC and sludge surface (e.g., hydrogen bonding, electrostatic interactions, and hydrophobic interactions). Of particular importance is the role of dissociation and resulting charge(s) of a PhAC in solution. Results demonstrate that when developing predictive models it is advantageous to separate PhACs based upon the charge of the dominant species at the experimental pH. Yet, use a single model for PhACs which are negatively charged and uncharged may have practical utility. Performance of the polyparameter models, however, was found to plateau with a pred-R(2) between 0.50 and 0.60, even when six statistically relevant predictors are included. This outcome suggests that effective predictive models for PhAC sorption cannot include solely PhAC descriptors, rather they must incorporate critical properties related to the sorbent (i.e., mixed liquor) surface.

Ecotoxicity of nanoparticles

Nanotechnology is a science of producing and utilizing nanosized particles that are measured in nanometers. The unique size-dependent properties make the nanoparticles superior and indispensable as they show unusual physical, chemical, and properties such as conductivity, heat transfer, melting temperature, optical properties, and magnetization. Taking the advantages of these singular properties in order to develop new products is the main purpose of nanotechnology, and that is why it is regarded as "the next industrial revolution." Although nanotechnology is quite a recent discipline, there have already high number of publications which discuss this topic. However, the safety of nanomaterials is of high priority. Whereas toxicity focuses on human beings and aims at protecting individuals, ecotoxicity looks at various trophic organism levels and intend to protect populations and ecosystems. Ecotoxicity includes natural uptake mechanisms and the influence of environmental factors on bioavailability (and thereby on toxicity). The present paper focuses on the ecotoxic effects and mechanisms of nanomaterials on microorganisms, plants, and other organisms including humans.

Linear and non-linear relationships between soil sorption and hydrophobicity: model, validation and influencing factors

The hydrophobic parameter represented by the octanol/water partition coefficient (logP) is commonly used to predict the soil sorption coefficient (K(oc)). However, a simple non-linear relationship between logK(oc) and logP has not been reported in the literature. In the present paper, soil sorption data for 701 compounds was investigated. The results show that logK(oc) is linearly related to logP for compounds with logP in the range of 0.5-7.5 and non-linearly related to logP for the compounds in a wide range of logP. A non-linear model has been developed between logK(oc) and logP for a wide range of compounds in the training set. This model was validated in terms of average error (AE), average absolute error (AAE) and root-mean squared error (RMSE) by using an external test set with 107 compounds. Nearly the same predictive capacity was observed in comparison with existing models. However, this non-linear model is simple, and uses only one parameter. The best model developed in this paper is a non-linear model with six correction factors for six specific classes of compounds. This model can well predict logK(oc) for 701 diverse compounds with AAE = 0.37. The reasons for systemic deviations in these groups may be attributed to the difference of sorption mechanism for hydrophilic/polar compounds, low solubility for highly hydrophobic compounds, hydrolysis of esters in solution, volatilization for volatile compounds and highly experimental errors for compounds with extremely high or low sorption coefficients.

Linear and non-linear relationships between soil sorption and hydrophobicity

The relationship between log K (oc) and log P was examined by use of a large dataset. For most of the hydrophobic compounds (e.g. 0.5 < log P < 7.5), the organic carbon content plays a dominant role in soil sorption and the sorption coefficient is linearly related to the octanol/water partition coefficient. For hydrophilic compounds (e.g. log P < 0.5), hydrophobic sorption becomes less significant. The hydrophilic contribution to sorption is equal to, or higher than, the hydrophobic contribution to sorption, resulting in the observed K (oc) values being higher than those predicted from their log P values. For highly hydrophobic compounds (e.g. log P > 7.5), log K (oc) decreases with increasing hydrophobicity because of a lack of chemical availability due to low solubility. A linear solvation energy relationship shows that the sorption potential increases with increasing molecular size by increasing the dispersion interactions between the chemical and soil organic phase. The sorption potential decreases with increase in the basicity of hydrophobic compounds by increasing the H-bonding of chemicals with water. Principal component analysis shows that the octanol/water system is the closest system, but not an ideal surrogate, to describe the soil sorption for hydrophobic compounds as compared with other solvent/water partition systems.

Laboratory tests on the impact of superabsorbent polymers on transformation and sorption of xenobiotics in soil taking 14C-imazalil as an example

Due to water scarcity, the agricultural production in arid areas is dependent on a sustainable irrigation management. In order to optimize irrigation systems, the application of superabsorbent polymers (SAP) as soil amendments, frequently studied within the last years, may be an appropriate measure to enhance the water holding capacity and the plant-available water in poor arable soils. These persistent polymers are also able to reduce heavy metal and salt stress to crops by accumulating those inorganic compounds. However, the impact of SAP on fate and behavior of organic xenobiotics in soil is unknown. Therefore, transformation and sorption of the model substance 14C-imazalil were monitored without and with SAP amendment in silty sand and sand soil under laboratory conditions. Within the 100-d incubation period, the transformation of 14C-imazalil was not substantially affected by the SAP amendment even though the microbial activity increased considerably. In the silty sand soil, extractable residues dropped from 90% to 45% without and from 96% to 46% with SAP amendment. Non-extractable residues continuously increased up to 49% and 35% while mineralization reached 6% and 5%, respectively. In the sand soil, characterized by its lower microbial activity and lower organic carbon content, extractable residues merely dropped from 99% to 81% and from 100% to 85% while non-extractable residues increased from 2% to 14% and 1% to 10%, respectively. Mineralization was lower than 2%. The increased microbial activity, usually promoting transformation processes of xenobiotics, was compensated by the enhanced sorption in the amended soils revealed by the increase of soil/water distribution coefficients (Kd) of 26 to 42 L kg(-1) for the silty sand and 6 to 25 L kg(-1) for the sand, respectively.

A linear solvation energy relationship model of organic chemical partitioning to particulate organic carbon in soils and sediments

Predicting the association of contaminants with particulate organic matter in the environment is critical in determining the fate and bioavailability of chemicals. A ubiquitous measure of contaminant association with soil and sediment particulate organic matter is the organic carbon partition coefficient K(OC) . Chemical class-specific models relating the K(OC) to the octanol-water partition coefficient K(OW) have been used to predict the partitioning to organic carbon in the water column and sediment for nonpolar hydrophobic pollutants and some polar pollutants. A single linear solvation energy relationship (LSER) is proposed as a simpler and chemically based alternative for predicting K(OC) for a more diverse set of compounds. A chemically diverse set of K(OC) data is used to obtain a more robust and more universally representative model of organic carbon partitioning than previously available LSER models. The resulting model has a root mean square error (RMSE) of prediction for log K(OC) of RMSE = 0.48 for the fitted data set and RMSE = 0.55 for an independent data set. An analysis of LSER coefficients highlights the relative importance of hydrogen bonding interactions.

Sorption of emerging trace organic compounds onto wastewater sludge solids

This work examined the sorption potential to wastewater primary- and activated-sludge solids for 34 emerging trace organic chemicals at environmentally relevant concentrations. These compounds represent a diverse range of physical and chemical properties, such as hydrophobicity and charge state, and a diverse range of classes, including steroidal hormones, pharmaceutically-active compounds, personal care products, and household chemicals. Solid-water partitioning coefficients (K(d)) were measured where 19 chemicals did not have previously reported values. Sludge solids were inactivated by a nonchemical lyophilization and dry-heat technique, which provided similar sorption behavior for recalcitrant compounds as compared to fresh activated-sludge. Sorption behavior was similar between primary- and activated-sludge solids from the same plant and between activated-sludge solids from two nitrified processes from different wastewater treatment systems. Positively-charged pharmaceutically-active compounds, amitriptyline, clozapine, verapamil, risperidone, and hydroxyzine, had the highest sorption potential, log K(d)=2.8-3.8 as compared to the neutral and negatively-charged chemicals. Sorption potentials correlated with a compound's hydrophobicity, however the higher sorption potentials observed for positively-charged compounds for a given log D(ow) indicate additional sorption mechanisms, such as electrostatic interactions, are important for these compounds. Previously published soil-based one-parameter models for predicting sorption from hydrophobicity (log K(ow)>2) can be used to predict sorption for emerging nonionic compounds to wastewater sludge solids.

Development of a novel concept for fate monitoring of biocides in liquid manure and manured soil taking 14C-imazalil as an example

Biocides are frequently applied in animal houses for veterinary hygiene or pest control. Thus, they may reach liquid manure tanks. Biocides that are not transformed during manure storage enter soil by the application of manure as organic fertilizer. Due to this environmentally relevant entry route, biocidal substances and products undergo a regulatory fate monitoring in liquid manure and soil. According to this, a novel concept was developed investigating the biocide imazalil as an example. For this purpose, excrements of test animals individually kept in experimental animal houses under standard nutrition were sampled. After matrix characterization, bovine and pig reference manures of defined dry substance contents were prepared. They were used for long-term transformation tests of (14)C-imazalil under strictly anaerobic conditions typical for manure storage in tanks. During the 177-d incubation period, however, imazalil was not substantially transformed. Furthermore, test manures with 7-d aged (14)C-imazalil residues were applied to study aerobic transformation and sorption in manured soil. Both concentration determining processes in soil were affected by the manure matrices. Comparing disappearance times (DT(50)) and sorption coefficients (K(OC)) after standard application (DT(50): 83 d; K(OC): 4059 L kg(-1)), (14)C-imazalil disappeared more rapidly after test manure application. DT(50) values were 29 or 48 d depending on whether bovine or pig test manure was applied. Mobility was slightly enhanced revealed by K(OC) of 1852 and 1385 L kg(-1), respectively.

Prediction of drug solubility from molecular structure using a drug-like training set

Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable for aliphatic amine groups, number of rotatable bonds and number of aromatic rings) and demonstrated acceptable statistics (r2 = 0.87, s = 0.51, F = 243.6, n = 191). The model was applied to two test sets consisting of a drug-like set of compounds (r2 = 0.80, s = 0.68, n = 174) and a set of agrochemicals (r2 = 0.88, s = 0.65, n = 200). Using the established general solubility equation (GSE) on the training and drug-like test set gave poorer results than the current study. The agrochemical test set was predicted with equal accuracy using the GSE and the QSPR equation. The results of this study suggest that increasing molecular size, rigidity and lipophilicity decrease solubility whereas increasing conformational flexibility and the presence of a non-conjugated amine group increase the solubility of drug-like compounds. Indeed, the proposed structural parameters make physical sense and provide simple guidelines for modifying solubility during lead optimisation.

Prediction of the sorption of organic compounds into soil organic matter from molecular structure

A new model to estimate the soil-water partition coefficient of non-ionic organic compounds normalized to soil organic carbon, Koc, from the two-dimensional molecular structure is presented. Literature data of log Koc for 571 organic chemicals were fitted to 29 parameters with a squared correlation coefficient r2 of 0.852 and a standard error of 0.469 log units. The application domain includes the atom types C, H, N, O, P, S, F, Cl, and Br in various important compound classes. The multilinear model contains the variables molecular weight, bond connectivity, molecular E-state, an indicator for nonpolar and weakly polar compounds, and 24 fragment corrections representing polar groups. The prediction capability is evaluated through an initial two-step development using an 80%:20% split of the data into training and prediction, cross-validation, permutation, and application to three external data sets. The discussion includes separate analyses for subsets of H-bond donors and acceptors as well as for nonpolar and weakly polar compounds. Comparison with existing models including linear solvation energy relationships illustrates the superiority of the new model.

Statistical external validation and consensus modeling: a QSPR case study for Koc prediction

The soil sorption partition coefficient (log K(oc)) of a heterogeneous set of 643 organic non-ionic compounds, with a range of more than 6 log units, is predicted by a statistically validated QSAR modeling approach. The applied multiple linear regression (ordinary least squares, OLS) is based on a variety of theoretical molecular descriptors selected by the genetic algorithms-variable subset selection (GA-VSS) procedure. The models were validated for predictivity by different internal and external validation approaches. For external validation we applied self organizing maps (SOM) to split the original data set: the best four-dimensional model, developed on a reduced training set of 93 chemicals, has a predictivity of 78% when applied on 550 validation chemicals (prediction set). The selected molecular descriptors, which could be interpreted through their mechanistic meaning, were compared with the more common physico-chemical descriptors log K(ow) and log S(w). The chemical applicability domain of each model was verified by the leverage approach in order to propose only reliable data. The best predicted data were obtained by consensus modeling from 10 different models in the genetic algorithm model population.

QSPR Treatment of the Soil Sorption Coefficients of Organic Pollutants

In this study, general and class-specific QSPR models for soil sorption, logK(OC), of 344 organic pollutants (0 < logK(OC) < 4.94) were developed using a large variety of theoretical molecular descriptors based only on molecular structure. Two general models were obtained. The first model was derived for a structurally representative set of 68 chemicals (R2=0.76, s=0.44), whereas the second involved a total of 344 compounds (R2=0.76, s=0.41). The first was validated using the data for the remaining 276 pollutants (R2=0.70, s=0.45). An additional validation of both models was performed using an independent set of 48 pollutants. Both models predict the logK(OC) at the level of experimental precision, while the theoretical molecular descriptors appearing in the QSPR models give further insight into the mechanisms of soil sorption. The analysis of the distribution of the residuals of the logK(OC) values calculated by both general models indicated the need and possible advantages of modeling soil sorption for smaller data sets related to individual classes of chemicals. Accordingly, QSPR models were also developed for 14 chemical classes. The descriptors appearing in these models were discussed as related to the possible interaction mechanisms in soil sorption.