A Protocol to Select High Quality Datasets of Ecotoxicity Values for Pesticides

Abstract

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.     

A novel approach to predict aquatic toxicity from molecular structure

The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respectively. In addition, a validation through an external test set was performed, which yields significant values of Rpred2 of 0.762 and 0.797. A brief study of the influence of the statistical outliers in QSAR's model development was also carried out. Finally, our method was compared with other approaches implemented in the Dragon software achieving better results. The non-stochastic and stochastic linear indices appear to provide an interesting alternative to costly and time-consuming experiments for determining toxicity.     

Estimating pesticide runoff in small streams

Surface runoff is one of the most important pathways for pesticides to enter surface waters. Mathematical models are employed to characterize its spatio-temporal variability within landscapes, but they must be simple owing to the limited availability and low resolution of data at this scale. This study aimed to validate a simplified spatially-explicit model that is developed for the regional scale to calculate the runoff potential (RP). The RP is a generic indicator of the magnitude of pesticide inputs into streams via runoff. The underlying runoff model considers key environmental factors affecting runoff (precipitation, topography, land use, and soil characteristics), but predicts losses of a generic substance instead of any one pesticide. We predicted and evaluated RP for 20 small streams. RP input data were extracted from governmental databases. Pesticide measurements from a triennial study were used for validation. Measured pesticide concentrations were standardized by the applied mass per catchment and the water solubility of the relevant compounds. The maximum standardized concentration per site and year (runoff loss, R_Loss) provided a generalized measure of observed pesticide inputs into the streams. Average RP explained 75% (p < 0.001) of the variance in R_Loss. Our results imply that the generic indicator can give an adequate estimate of runoff inputs into small streams, wherever data of similar resolution are available. Therefore, we suggest RP for a first quick and cost-effective location of potential runoff hot spots at the landscape level.     

Quantitative structure-activity relationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs) dissipation in rhizosphere based on molecular structure and effect size

Rhizoremediation is a significant form of bioremediation for polycyclic aromatic hydrocarbons (PAHs). This study examined the role of molecular structure in determining the rhizosphere effect on PAHs dissipation. Effect size in meta-analysis was employed as activity dataset for building quantitative structure-activity relationship (QSAR) models and accumulative effect sizes of 16 PAHs were used for validation of these models. Based on the genetic algorithm combined with partial least square regression, models for comprehensive dataset, Poaceae dataset, and Fabaceae dataset were built. The results showed that information indices, calculated as information content of molecules based on the calculation of equivalence classes from the molecular graph, were the most important molecular structural indices for QSAR models of rhizosphere effect on PAHs dissipation. The QSAR model, based on the molecular structure indices and effect size, has potential to be used in studying and predicting the rhizosphere effect of PAHs dissipation.     

QSAR trout toxicity models on aromatic pesticides

The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC50 concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.     

QSAR trout toxicity models on aromatic pesticides

The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC₅₀ concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.     

Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity

A QSAR model is successfully proposed to predict the toxicity effect on Photobacterium phosphoreum by nonpolar-narcotic-chemical mixtures and/or polar-narcotic-chemical mixtures. For nonpolar-narcotic-chemical mixtures and polar-narcotic-chemical mixtures, their corresponding hydrophobicity-based QSAR models are derived from regression analysis. Comparison of these two QSAR models make us believe that it is the joint effect of hydrogen bond in polar-narcotic-chemical mixture that leads to the difference between these two models. Such joint effect of hydrogen bond can be quantified as AMH and BMH by using the different partition coefficients of mixtures in various organic phase/water systems. And the regression analysis results convinced us that the introduction of AMH does improve the quality of the QSAR model with r2=0.948, S.E.=0.166 and F=745.201 at P=0.000 for total 84 mixtures.     

Probabilistic approaches in the effect assessment of toxic chemicals. What are the benefits and limitations?

There is an ongoing discussion whether in the environmental risk assessment for chemicals the so called 'deterministic' approach using point estimates of exposure and effect concentrations is still appropriate. Instead, the more detailed and scientifically sounder probabilistic methods that have been developed over the last years are widely recommended. Here, we present the results of a probabilistic effect assessment for the aquatic environment performed for the pesticide methyl parathion and compare them with the results obtained with the common deterministic approach as described in the EU Technical Guidance Document. Methyl parathion was chosen because a sufficient data set (acute toxicity data for about 70 species) was available. The assumptions underlying the probabilistic effect assessment are discussed in the light of the results obtained for methyl parathion. Two important assumptions made by many studies are: (i) a sufficient number of ecologically relevant toxicity data is available, (ii) the toxicity data follow a certain distribution such as log-normal. Considering the scarcity of data for many industrial chemicals, we conclude that these assumptions would not be fulfilled in many cases if the probabilistic assessment was applied to the majority of industrial chemicals. Therefore, despite the well-known limitations of the deterministic approach, it should not be replaced by probabilistic methods unless the assumptions of these methods are carefully checked in each individual case, which would significantly increase the effort for the assessment procedure.     

Prediction of mixture toxicity with its total hydrophobicity

Based on the C18 Empore disk/water partition coefficient of a mixture, quantitative structure-activity relationships (QSARs) are presented, which are used to predict the toxicity of mixed halogenated benzenes to P. phosphoreum. The predicted toxicity of 10 other related mixtures based on the QSAR model, agree well with the observed data with r2 = 0.973, SE = 0.113 and F = 287.785 at a level of significance P < 0.0001. The joint effect of these chemicals is simple similar action and the toxicity of the mixtures can be predicted from total hydrophobicity and is independent of hydrophobicity of the components or the ratio of the individual chemicals.     

Incorporating uncertainty in a chemical leaching assessment

Pesticide leaching models are being used to assist in the regulation and management of pesticides by indicating their potential for leaching to groundwater. Uncertainty in model input data is not, regrettably, included in most pesticide leaching assessments. In the work described here, we use logarithmic transformations of the attenuation factor (AF), a simple process-based index model, to represent uncertainty in a pesticide leaching assessment. Characterization of a wide range of pesticides as `leachers' or `non-leachers' for a specific Hawaii hydrogeological setting is facilitated by comparing the log-transformed AF, designated AFR, for each chemical with two reference chemicals for which leaching behavior in Hawaii is known. Defining a mean and uncertainty interval for the AFR index of each chemical being ranked provides a practical method of incorporating data uncertainty into a regulatory protocol.     

The effect of nitroaromatics' composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis

Novel 1D QSAR approach that allows analysis of non-additive effects of molecular fragments on toxicity has been proposed. Twenty-eight nitroaromatic compounds including some well-known explosives have been chosen for this study. The 50% lethal dose concentration for rats (LD50) was used as the estimation of toxicity in vivo to develop 1D QSAR models on the framework of Simplex representation of molecular structure. The results of 1D QSAR analysis show that even the information about the composition of molecules provides the main trends of toxicity changes. The necessity of consideration of substituents' mutual impacts for the development of adequate QSAR models of nitroaromatics' toxicity was demonstrated. Statistic characteristics for all the developed partial least squares QSAR models, except the additive ones are quite satisfactory (R2=0.81-0.92; Q2=0.64-0.83; R2 test=0.84-0.87). A successful performance of such models is due to their non-additivity i.e. possibility of taking into account the mutual influence of substituents in benzene ring which plays the governing role for toxicity change and could be mediated through the different C-H fragments of the ring. The correspondence between observed and predicted by these models toxicity values is good. This allowing combine advantages of such approaches and develop adequate consensus model that can be used as a toxicity virtual screening tool.     

Toxicology of benzyl alcohols: a QSAR analysis

There is an evidence that benzyl alcohols may exhibit toxicity via a radical mechanism. To test this possibility, we studied the toxicity of para substituted benzyl alcohols on rapidly dividing cancer cells (L1210 leukemia). This system has previously found utility in studying the apparent radical toxicity of a variety of phenols. However, no evidence could be found for an electronic effect and the cellular toxicity was associated primarily with hydrophobicity. Comparison of this quantitative structure-activity relationships (QSAR) with others for the reactions of benzyl alcohols in diverse systems provides insight into mechanisms of action. A QSAR for the interaction of benzyl alcohols with protozoa yields an equation that is dependent on both hydrophobicity and acidity of the OH group versus a mixture of bacteria and fungi, the critical dependence on hydrophobicity prevails with a small dependence on a resonance-stabilized, radical mediated electronic effect. The chloramphenicols provide an instructive example, where the radical mediated electronic effect overshadows the hydrophobic contribution to bacterial toxicity. These various QSAR for benzyl alcohols indicate that mechanisms of growth inhibition in vitro vary depending on cell/organism type, the strength of the bond and lability of the hydrogen, and the strength of the initiating radical reagent.     

Mechanism-based quantitative structure-activity relationships for the inhibition of substituted phenols on germination rate of Cucumis sativus

Comparative inhibition activity (GC50) of 42 structurally diverse substituted phenols on seed germination rate of Cucumis sativus was investigated. Quantitative structure-activity relationships (QSARs) were developed by using hydrophobicity (1-octanol/water partition coefficient, logKow) and electrophilicity (the energy of the lowest unoccupied molecule orbital, Eluma) for the toxicity of phenols according to their modes of toxic action. Most phenols elicited their response via a polar narcotic mechanism and a highly significant log Kow-based model was obtained (GC50 = 0.92 log Kow + 1.99, r2 0.84, n = 29). The inclusion of E(lumo) greatly improved the predictive power of the polar narcotic QSAR (GC50 = 0.88 log Kow - 0.30E(lumo) + 1.99, r2 = 0.93, n = 29). pKa proved to be an insignificant influencing factor in this study. Poor correlation with hydrophobicity and strong correlation with electrophilicity were observed for the nine bio-reactive chemicals. Their elevated toxicity was considerably underestimated by the polar narcotic logKow-dependent QSAR. The nine chemicals consist of selected nitro-substituted phenols, hydroquinone, catechol and 2-aminophenol. Their excess toxic potency could be explained by their molecular structure involving in vivo reaction with bio-macromolecules. Strong dissociation of carboxyl group of the four benzoic acid derivatives greatly decreased their observed toxicity. In an effort to model all chemicals including polar narcotics and bio-reactive chemicals, a response-surface analysis with the toxicity, logKow and E(lumo) was performed. This resulted in a highly predictive two-parameter QSAR for most of the chemicals (GC50 = 0. 70 logKow - 0.66E(lumo) + 2.17, r2 = 0.89, n = 36). Catechol and 2,4-dinitrophenol proved to be outliers of this model and their much high toxicity was explained.     

Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: a preliminary study

In ecotoxicology, mechanism-based quantitative structure-activity relationships (QSARs) are usually developed with higher quality than QSARs without regard to toxicity mechanism. Correctly determining the mechanism of a compound, which is not always easy, is required to use mechanism-based QSARs for toxicity prediction. The mechanism determination step may introduce extra errors in addition to the intrinsic prediction errors of mechanism-based QSARs, thus compromising these QSARs' performance compared with QSARs regardless of mechanism. In this study, the mechanism identification-toxicity prediction (MI-TP) approach was compared with the direct toxicity prediction (DTP) approach using a data set containing phenol toxicity to Tetrahymena pyriformis. A statistical mechanism classification model for mechanism prediction, four mechanism-based QSARs and a single QSAR without discriminating between mechanisms were developed for toxicity prediction. Toxicity of phenols in an external data set was predicted following the MI-TP and DTP approaches. Results indicated that the mechanisms of several phenols in the external test set were incorrectly predicted which led to significant over- or under-estimation of their toxicity. Overall, the MI-TP approach did not yield more accurate toxicity prediction than the DTP approach.     

The role of toxicology in the evaluation of new agrochemicals

Abstract

The use of agrochemicals like crop protecting agents, veterinary disinfectants, and wood preservatives may result in (un)intentional exposure of the environment, animals and man. This paper deals with current testing strategies to assess the potential health risks for humans exposed to these chemicals during production or application or via consumption of foods containing pesticide residues.

Principles and procedures for safety assessment of pesticide residues in food as developed by WHO/FAO are described. Different types of toxicity studies in mammalian test animal species are discussed and a strategy is outlined in order to characterize the toxicity profile of a compound and the relationship between applied doses and adverse effects. Safety testing of agrochemicals should be carried out in relation to its intended use, and in particular attention will be paid to toxicity testing of residues of pesticides in food. Extrapolation of results from animal studies to humans and the use of safety factors is discussed.

Besides the use of animal protocol studies for safety testing of agrochemicals, the potential use of in‐vitro models derived from organs and tissues of animals is discussed. Data on the in‐vitro metabolism of thiabendazole, aldicarb and alachlor are discussed in order to demonstrate that such data may complement or partly substitute whole animal experimentation.

Principles and procedures for safety testing of residues of agrochemicals in food as applied during the last three decades, constitute a ‘safety‐first’ approach, providing sufficient safety margins for the consumer of foods which may contain low levels of residues of agrochemicals.     

Quantitative structure-activity relationships for the inhibition toxicity to root elongation of Cucumis sativus of selected phenols and interspecies correlation with Tetrahymena pyriformis

The comparative toxicities of selected phenols to higher plants Cucumis sativus were measured and the negative logarithm molar concentration of the root elongation median inhibition (IRC50) were derived. Quantitative structure-activity relationships (QSARs) were developed to explore the toxicity influencing factors and for predictive purpose. The toxicity data, fell into two classes: polar narcosis and bio-reactive. For polar narcotic phenols, a highly significant two-parameter QSAR based on 1-octanol/water partition coefficient (logKow) and energy of the lowest unoccupied orbital (E(lumo)) was derived (IRC50 = 0.77 log Kow - 0.39E(lumo) + 2.36 n = 22 r2 = 0.89). The five bio-reactive chemicals proved to show elevated toxicity due to their typical substructure involved diverse reactive mechanisms. In an effort to model all chemicals, a robust multiple-variable QSAR combining logKow, E(lumo) and Qmax, the most negative net atomic charge, was developed (IRC50 = 0.65 logKow - 0.72E(lumo) + 0.23Qmax + 2.81 n = 27 r2 = 0.94), indicating that hydrophobicity, electrophilicity and hydrogen bond interaction contribute mainly to the phytotoxicity. The toxicological data was compared with Tetrahymena pyriformis 2-d population growth inhibition toxicity (IGC50) and excellent interspecies correlations were observed both for the polar narcotics and for five reactive chemicals (for polar narcotics: IRC50 = 0.95IGC50 + 1.07 n = 16 r2 = 0.89; for bio-reactive chemicals: IRC50 = 0.98IGC50 + 2.19 n = 5 r2 = 0.97; and for all: IRC50 = 0.93IGC50 + 1.63 n = 21 r2 = 0.87). This suggested that T pyriformis toxicity could serve as a surrogate of C. sativus toxicity for phenols and interspecies correlation also could be established for reactive chemicals.