A topological substructural molecular design to predict soil sorption coefficients for pesticides

Mechanistic studies of congener-specific adsorption and bioaccumulation of polycyclic aromatic hydrocarbons and phthalates in soil by novel QSARs

Polycyclic aromatic hydrocarbons (PAHs) and phthalic acid esters (PAEs) which are structurally featured with one or more aromatic skeletons are often regarded as two important groups of organic pollutants due to the widespread distribution and notorious toxic effects in soils. Relative to the great number of structural analogues or congeners detected in soil, however, the soil adsorption and bioaccumulation of PAHs/PAEs by plant is far less studied for the insufficiency of experimental determinations or lack of insights into the inherent structural requirements. To mechanistically evaluate the congener-specific soil adsorption and bioaccumulation for PAHs/PAEs, the quantitative structure-activity relationships (QSARs) were successfully developed by density functional theory (DFT) computation and partial least squares (PLS) analysis. As verified with the higher cumulative variance coefficients and cross-validated correlation coefficients for strong stability, interpretability and predictability, the QSARs could be used for prediction of unknown adsorption potency or bioavailability within the specified applicability domain, respectively. It was indicated by QSAR that the structural requirements of PAHs/PAEs necessary for strengthening the soil adsorption were mainly attributed to the molecular polarizability and the associated dispersion interaction with soil. As regards the bioaccumulation by carrot, the aggravation of spherical polarity change of molecules and the involved electrostatic interaction with soil entity or electron transfer from the highest occupied molecular orbital (HOMO) of PAHs/PAEs was implied to be inherently decisive for the variance of bioavailability among congeners. Based on the holistic view of negative correlation relationship, the soil adsorption seemed to act as the forceful constraint in decreasing the bioaccumulation of PAHs/PAEs and could also be alternatively gauged as the preliminary evaluation of bioavailability and risks on soil ecosystem. It would thus help better understand the soil adsorption and bioaccumulation with the informative mechanistic insights and provide data support for ecological risk assessment of PAHs/PAEs in soils.

Activity, biomass and composition of microbial communities and their degradation pathways in exposed propazine soil

Propazine is a s-triazine herbicide widely used for controlling weeds for crop production. Its persistence and contamination in environment nagatively affect crop growth and food safety. Elimination of propazine residues in the environment is critical for safe crop production. This study identified a microbial community able to degrade propazine in a farmland soil. About 94% of the applied propazine was degraded within 11 days of incubation when soil was treated with 10mgkg^-1 propazine as the initial concentration. The process was accompanied by increased microbial biomass and activities of soil enzymes. Denaturing gradient gel electrophoresis (DGGE) revealed multiple bacterial strains in the community as well as dynamic change of the composition of microbial community with a reduced microbial diversity (H' from 3.325 to 2.78). Tracking the transcript level of degradative genes AtzB, AtzC and TrzN showed that these genes were induced by propazine and played important roles in the degradation process. The activities of catalase, dehydrogenase and phenol oxidase were stimulated by propazine exposure. Five degradation products (hydroxyl-, methylated-, dimeric-propazine, ammeline and ammelide) were characterized by UPLC-MS², revealing a biodegradation of propazine in soil. Several novel methylated and dimeric products of propazine were characterized in thepropazine-exposed soil. These data help understand the pathway, detailed mechanism and efficiency of propazine biodegradation in soil under realistic field condition.

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.

QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS

A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.

Adsorption and desorption of chlorpyrifos to soils and sediments

Chlorpyrifos, one of the most widely used insecticides, has been detected in air, rain, marine sediments, surface waters, drinking water wells, and solid and liquid dietary samples collected from urban and rural areas. Its metabolite, TCP, has also been widely detected in urinary samples collected from people of various age groups. With a goal of elucidating the factors that control the environmental contamination, impact, persistence, and ecotoxicity of chlorpyrifos, we examine, in this review, the peer-reviewed literature relating to chlorpyrifos adsorption and desorption behavior in various solid-phase matrices. Adsorption tends to reduce chlorpyrifos mobility, but adsorption to erodible particulates, dissolved organic matter, or mobile inorganic colloids enhances its mobility. Adsorption to suspended sediments and particulates constitutes a major off-site migration route for chlorpyrifos to surface waters, wherein it poses a potential danger to aquatic organisms. Adsorption increases the persistence of chlorpyrifos in the environment by reducing its avail- ability to a wide range of dissipative and degradative forces, whereas the effect of adsorption on its ecotoxicity is dependent upon the route of exposure. Chlorpyrifos adsorbs to soils, aquatic sediments, organic matter, and clay minerals to differing degrees. Its adsorption strongly correlates with organic carbon con- tent of the soils and sediments. A comprehensive review of studies that relied on the batch equilibrium technique yields mean and median Kd values for chlorpyrifos of 271 and 116 L/kg for soils, and 385 and 403 L/kg for aquatic sediments. Chlorpyrifos adsorption coefficients spanned two orders of magnitude in soils. Normalizing the partition coefficient to organic content failed to substantially reduce variability to commonly acceptable level of variation. Mean and median values for chlorpyrifos partition coefficients normalized to organic carbon, K, were 8,163 and 7,227 L/kg for soils and 13,439 and 15,500 L/kg for sediipents. This variation may result from several factors, including various experimental artifacts, variation in quality of soil organic matter, and inconsistencies in experimental methodologies. Based on this review, there appears to be no definitive quantification of chlorpyrifos adsorption or desorption characteristics. Thus, it is difficult to predict its adsorptive behavior with certainty, without resorting to experimental methods specific to the soil or sediment of interest. This limitation should be recognized in the context of current efforts to predict the risk, fate, and transport of chlorpyrifos based upon published partition coefficients. Based on a comprehensive review of the peer-reviewed literature related to adsorption and desorption of chlorpyrifos, we propose the following key areas for future research. From this review, it becomes increasingly evident that pesticide partitioning cannot be fully accounted for by the fraction of soil or solid-matrix organic matter or carbon content. Therefore, research that probes the variation in the nature and quality of soil organic matter on pesticide adsorption is highly desirable. Pesticide persistence and bioavailability depend on insights into desorption capacity. Therefore, understanding the fate and environmental impact of hydrophobic pesticides is incomplete without new research being performed to improve insights into pesticide desorption from soils and sediments. There is also a need for greater attention and consistency in developing experimental methods aimed at estimating partition coefficients. Moreover, in such testing, choosing initial concentrations and liquid-solid ratios that are more representative of environmental conditions could improve usefulness and interpretation of data that are obtained. Future monitoring efforts should include the sampling and analysis of suspended particulates to account for suspended solid-phase CPF, a commonly underestimated fraction in surface water quality monitoring programs. Finally, management practices related to the reduction of off-site migration of CPF should be further evaluated, including alternative agricultural practices leading to reduction in soil erosion and structural best management practices, such as sedimentation ponds, treatment wetlands, and vegetated edge-of-field strips.

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.

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.

Estimating persistence of brominated and chlorinated organic pollutants in air, water, soil, and sediments with the QSPR-based classification scheme

We have estimated degradation half-lives of both brominated and chlorinated dibenzo-p-dioxins (PBDDs and PCDDs), furans (PBDFs and PCDFs), biphenyls (PBBs and PCBs), naphthalenes (PBNs and PCNs), diphenyl ethers (PBDEs and PCDEs) as well as selected unsubstituted polycyclic aromatic hydrocarbons (PAHs) in air, surface water, surface soil, and sediments (in total of 1,431 compounds in four compartments). Next, we compared the persistence between chloro- (relatively well-studied) and bromo- (less studied) analogs. The predictions have been performed based on the quantitative structure-property relationship (QSPR) scheme with use of k-nearest neighbors (kNN) classifier and the semi-quantitative system of persistence classes. The classification models utilized principal components derived from the principal component analysis of a set of 24 constitutional and quantum mechanical descriptors as input variables. Accuracies of classification (based on an external validation) were 86, 85, 87, and 75% for air, surface water, surface soil, and sediments, respectively. The persistence of all chlorinated species increased with increasing halogenation degree. In the case of brominated organic pollutants (Br-OPs), the trend was the same for air and sediments. However, we noticed that the opposite trend for persistence in surface water and soil. The results suggest that, due to high photoreactivity of C-Br chemical bonds, photolytic processes occurring in surface water and soil are able to play significant role in transforming and removing Br-OPs from these compartments. This contribution is the first attempt of classifying together Br-OPs and Cl-OPs according to their persistence, in particular, environmental compartments.

Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach

Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.

A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds

Chemically reactive, alpha, beta-unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity. This toxic property can often be related to chemical structure, in particular to specific molecular substructures or fragments (alerts), which can then be used in specialized software or expert systems for predictive purposes. In the past, there have been many attempts to predict the mutagenicity of alpha, beta-unsaturated carbonyl compounds through quantitative structure activity relationships (QSAR) but considering only one exclusive endpoint: the Ames test. Besides, even though those studies give a comprehensive understanding of the phenomenon, they do not provide substructural information that could be useful forward improving expert systems based on structural alerts (SAs). This work reports an evaluation of classification models to probe the mutagenic activity of alpha, beta-unsaturated carbonyl compounds over two endpoints--the Ames and mammalian cell gene mutation tests--based on linear discriminant analysis along with the topological Substructure molecular design (TOPS-MODE) approach. The obtained results showed the better ability of the TOPS-MODE approach in flagging structural alerts for the mutagenicity of these compounds compared to the expert system TOXTREE. Thus, the application of the present QSAR models can aid toxicologists in risk assessment and in prioritizing testing, as well as in the improvement of expert systems, such as the TOXTREE software, where SAs are implemented.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.