Henk J. M. Verhaar

Classifying environmental pollutants

Abstract In this paper a scheme is presented that makes it possible to classify a large number of organic pollutants into one of four classes, viz: [1] inert chemicals, [2] less inert chemicals, [3] reactive chemicals and [4] specifically acting chemicals. For chemicals that are thus classified as belonging to one of these four classes it is possible to calculate either an expected effect concentration (inert chemicals), such as the LC 50 , or an expected range of possible effect concentrations, from a compounds octanol/water partition coefficient (Log K ow ). For chemicals that cannot be classified as belonging to one of these four classes no prediction can be made. This approach can be implemented to estimate aquatic effect concentrations, which can be used to derive preliminary environmental quality objectives, or for the prioritisation of chemicals for subsequent testing. Moreover, these estimates could be of great value in risk and hazard assessment. To our opinion, this approach represents the current state-of-the-art in estimation methods for aquatic toxicology. This paper is especially focused on identifying the limits of applicability of Quantitative Structure-Activity Relationships for predicting aquatic toxicity, as, according to Hart (1991) it “…is important not to exaggerate the accuracy of prediction for models…” [and] “…to make clear to the user the limitations of the relationship.”

QSAR modelling of soil sorption. Improvements and systematics of log KOC vs. log KOW correlations

A systematic study was performed to evaluate the quality and reliability of the quantitative relationships between the soil sorption coefficients and the n-octanol/water partition coefficients (log KOC vs. log KOW). A system of QSAR models has been derived which is based on a reliable set of experimental log KOW data (LOGPSTAR data) or reliable estimates calculated with the latest version of the ClogP algorithm. Particular emphasis has been made to clearly define the boundaries for application of developed models as well as the quality of estimates. Thus, for each developed model the application domain is clearly defined through the chemical (structural) domain, substituents domain, and X-variable domain. Finally, the QSAR model with the first-order molecular connectivity indices has been incorporated in the derived system of QSAR models since the soil sorption estimates of the predominantly hydrophobic chemicals based on the log KOW data have large uncertainties, particularly in the log KOW data range from 4 to 7.5.

Multivariate analysis of aquatic toxicity data with PLS

A common task in data analysis is to model the relationships between two sets of variables, the descriptor matrixX and the response matrixY. A typical example in aquatic science concerns the relationships between the chemical composition of a number of samples (X) and their toxicity to a number of different aquatic species (Y). This modelling is done in order to understand the variation ofY in terms of the variation ofX, but also to lay the ground for predictingY of unknown observations based on their knownX-data. Correlations of this type are usually expressed as regression models, and are rather common in aquatic science. Often, however, the multivariateX andY matrices invalidate the use of multiple linear regression (MLR) and call for methods which are better suited for collinear data. In this context, multivariate projection methods represent a highly useful alternative, in particular, partial least squares projections to latent structures (PLS). This paper introduces PLS, highlights its strengths and presents applications of PLS to modelling aquatic toxicity data. A general discussion of regression, comparing MLR and PLS, is provided.

A quantitative property-property relationship (QPPR) approach to estimate in vitro tissue-blood partition coefficients of organic chemicals in rats and humans.

Abstract The present study describes quantitative property-property relationships (QPPRs) for the partitioning of organic chemicals between blood and tissue homogenates from both rats and humans. The n-octanol/water partition coefficient (Kow) is used as a non-biological descriptor. QPPRs for human tissue-blood partition coefficients (PCs) were derived from a dataset of 24 volatile organic compounds in blood, liver, muscle, fat, kidney and brain tissue homogenates. QPPRs were also derived for the PCs of rat tissues, using a dataset of 42 volatile organic compounds in blood, liver, muscle and fat tissue homogenates. These QPPRs were evaluated using a test set of 10 compounds for human tissues and a test set of 14 compounds for rat tissues. For both human and rat test sets, it was generally observed that most estimated PCs were within a range of 50–200% of their experimental values. The present approach is concluded to offer a rapid means for the estimation of tissue-blood PCs of compounds on the basis of Kow values. In addition, indications for a possible role of tissue components other than lipid and water in the tissue-blood partitioning process of compounds were observed from the calibration results of the model.

Classifying environmental pollutants. 2: Separation of class 1 (baseline toxicity) and class 2 (‘polar narcosis’) type compounds based on chemical descriptors

A large part of the xenobiotics that are encountered as aquatic pollutants can be regarded as belonging to the so‐called class 1 or baseline toxicity compounds. It is generally accepted that these compounds act through or can be considered to act through one and the same mechanism. A second important class of aquatic pollutants is formed by the slightly more toxic class 2 or polar narcosis compounds; this class of compounds is made up of, among others, phenols, anilines and similar slightly polar species and can also be considered to act through a single toxic mechanism.

Classifying environmental pollutants: Part 3. External validation of the classification system.

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

Intra- and interlaboratory calibration of the DR CALUX® bioassay for the analysis of dioxins and dioxin-like chemicals in sediments

In the Fourth National Policy Document on Water Management in The Netherlands, it is defined that in 2003, in addition to the assessment of chemical substances, special guidelines for the assessment of dredged material should be recorded. The assessment of dredged material is based on integrated chemical and biological effect measurements. Among others, the DR CALUX (dioxin responsive-chemically activated luciferase expression) bioassay has tentatively been recommended for inclusion in the dredged material assessment. To ensure the reliability of this bioassay, an intra- and interlaboratory validation study, or ring test, was performed, organized by the Dutch National Institute for Coastal and Marine Management (RIKZ) in cooperation with BioDetection Systems BV (BDS). The intralaboratory repeatability and reproducibility and the limit of detection (LOD) and quantification (LOQ) of the DR CALUX bioassay were determined by analyzing sediment extracts and dimethyl sulfoxide (DMSO) blanks. The highest observed repeatability was found to be 24.1%, whereas the highest observed reproducibility was calculated to be 19.9%. Based on the obtained results, the LOD and LOQ to be applied for the bioassay are 0.3 and 1.0 pM, respectively. The interlaboratory calibration study was divided into three phases, starting with analyzing pure chemicals. During the second phase, sediment extracts were analyzed, whereas in the third phase, whole sediments had to be extracted, cleaned, and analyzed. The average interlaboratory repeatability increased from 14.6% for the analysis of pure compound to 26.1% for the analysis of whole matrix. A similar increase in reproducibility with increasing complexity of handlings was observed with the interlaboratory reproducibility of 6.5% for pure compound and 27.9% for whole matrix. The results of this study are intended as a starting point for implementing the integrated chemical-biological assessment strategy and for systematic monitoring of dredged materials and related materials in the coming years.

Polar narcosis: Designing a suitable training set for QSAR studies

Substituted phenols, anilines, pyridines and mononitrobenzenes can be classified as polar narcotics. These chemicals differ from non-polar narcotic compounds not only in their toxic potency (normalized by log Kow), but also in their Fish Acute Toxicity Syndrome profiles, together suggesting a different mode of action.For 97 polar narcotics, which are not ionized under physiological conditions, 11 physico-chemical and quantum-chemical descriptors were calculated. Using principal component analysis, 91 % of the total variance in this descriptor space could be explained by three principal components which were subsequently used as factors in a statistical design. Eleven compounds were selected based on a two-level full factorial design including three compounds near the center of the chemical domain (a 23+3 design).QSARs were developed for both the design set and the whole set of 63 polar narcotics for which guppy and/or fathead minnow data were available in the literature. Both QSARs, based on partial least squares regression (3 latent variables), resulted in good models (R2=0.96 and Q2=0.82; R2=0.86 and Q2=0.83 respectively) and provided similar pseudo-regression coefficients. In addition, the model based on the design chemicals was able to predict the toxicity of the 63 compounds (R2 =0.85).Models show that acute fish toxicity is determined by hydrophobicity, HOMO-LUMO energy gap and hydrogen-bond acceptor capacity.

Predicting reductive transformation rates of halogenated aliphatic compounds using different QSAR approaches

The kinetics of the reductive transformation rates of a set of 17 halogenated aliphatic hydrocarbons in anaerobic sediment-water mixtures are examined using different QSAR methods. Statistical experimental design in combination with multivariate chemical characterization of the compounds was used to select a representative training and validation set. The aim of the QSARs is to generate predictions for priority setting and risk assessment purposes, and to better understand the kinetics of the dehalogenation of aliphatic hydrocarbons. The first QSAR was constructed with multiple linear regression using readily available descriptors. Subsequently, a multivariate QSAR was constructed using the partial least squares (PLS) method with 36 (physico)-chemical descriptors. Finally, a transition state approach has been used in which quantum chemically calculated activation energies for the transition state of the most probable reaction mechanism are used to model the reaction rate constantsk. Because of the relatively small size of the training set (10 compounds) the linear regression QSAR using multiple descriptors does not show good predictive capabilities on the validation set. The PLS relationship and the transition state QSAR are both capable of generating predictions of rate constants within one order of magnitude. Moreover, the transition state QSAR closely follows, and thus corroborates the assumed reaction mechanism for reductive dehalogenation. Predictions for 23 non tested halogenated aliphatics are given and compared using both the PLS and the transition state model.

Fast screening of large databases using clustering and PCA based on structure fragments

Jarvis‐Patrick clustering based on structural fragments with the Tanimoto coefficient as the similarity measure provides a fast tool for classification of large amounts of chemicals. This clustering technique was applied to chemicals in relation to their acute fish toxicity (LC50). Correlation analysis with log LC50 as the response variable and log Kow as the predictor variable resulted in good models for several clusters. Benzylic chemicals were not recognized as separate clusters. Including them in the training set resulted in models without any predictive capability. Based on statistical and chemical criteria, they were rejected, improving the final model substantially. The toxicological response of phenols and some organophosphates was found to fit well into one model. The clustering resulted in smaller groupings than those listed by Verhaar et al. but were only in dispute for a minority of chemicals. PCA allowed a quick visual inspection of the application limits of the models for the HPVCs and the EINECS. The models performed well for the HPVCs but could only be used to estimate a fraction of the EINECS. PCA showed that in some cases subclusters were present.