Assessment, distribution, and ecological risk of contaminants of emerging concern in a surface water-sediment-fish system impacted by wastewater.

The presence of contaminants of emerging concern in aquatic ecosystems represents an ever-increasing environmental problem. Aquatic biota is exposed to these contaminants, which can be absorbed and distributed to their organs. This study focused on the assessment, distribution, and ecological risk of 32 CECs in a Spanish river impacted by effluents from a wastewater treatment plant, analyzing the organs and plasma of common carp. Environmental concentrations in water and sediment were examined at sites upstream and downstream of the wastewater treatment plant. The two downstream sites showed 15 times higher total concentrations (12.4 µg L-1 and 30.1 µg L-1) than the two upstream sites (2.08 µg L-1 and 1.66 µg L-1). Half of the CECs were detected in fish organs, with amantadine having the highest concentrations in the kidney (158 ng g-1 w.w.) and liver (93 ng g-1 w.w.), followed by terbutryn, diazepam, and bisphenol F in the brain (50.2, 3.82 and 1.18 ng g-1 w.w.). The experimental bioaccumulation factors per organ were compared with the bioconcentration factors predicted by a physiologically based pharmacokinetic model, obtaining differences of one to two logarithmic units for most compounds. Risk quotients indicated a low risk for 38 % of the contaminants. However, caffeine and terbutryn showed an elevated risk for fish. The mixed risk quotient revealed a medium risk for most of the samples in the three environmental compartments: surface water, sediment, and fish.

Octanol/Air Partition Coefficient─A General-Purpose Fragment Model to Predict Log Koa from Molecular Structure.

The octanol/air partition coefficient Koa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, Koa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient Kow and the air/water partition coefficient Kaw for multimedia fate modeling. Experimental log Koa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting log Koa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms: 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne: -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe: 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (80:20%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-Koa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding Koa indirectly determined through Kow and Kaw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) Koa.

Henry's Law Constant─A General-Purpose Fragment Model to Predict Log Kaw from Molecular Structure.

Henry's law constant is important for assessing the environmental fate of organic compounds, including polar accumulation, indoor contamination, and the impact of airborne predominance on persistence. Moreover, it can be used in the context of alternative 3R bioassays to inform about the compound loss through volatilization as a confounding factor. For 2636 compounds, curated experimental log Kaw (air/water partition coefficient) data at 25° covering 23.6 orders of magnitude (from -18.6 to 5.0) have been collected from the literature. Subsequently, a new fragment model for predicting log Kaw from molecular structures has been developed. According to the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe), this general-purpose model outperforms COSMOtherm, EPISuite HENRYWIN, OPERA, and LSER with calculated input parameters significantly (rms 0.50 vs 0.92 vs 1.25 vs 1.28 vs 1.38, mne -2.74 vs -6.78 vs -9.11 vs -6.24 vs -6.27, mpe 2.25 vs 6.22 vs 8.27 vs 11.5 vs 7.69 log units). Initial separation into a training and prediction set (80%:20%), mutual leave-50%-out validation, and target value scrambling (temporarily wrong compound-Kaw allocations) demonstrate the prediction capability, statistical robustness, and mechanistically sound basis of the fragment scheme. The new model is available to the public in fully computerized form through the ChemProp software, and can be combined with a separate existing model to extend the log Kaw prediction to temperatures different from 25 °C.

Inhalation threshold of toxicological concern (TTC) - Structural alerts discriminate high from low repeated-dose inhalation toxicity.

The threshold of toxicological concern (TTC) of a compound represents an exposure value below which the associated human health risk is considered negligible. As such, this approach offers assessing the risk of potential toxicants when little or no toxicological information is available. For the inhalation repeated-dose TTC, the goal was to derive structural alerts that discriminate between high- and low-toxic compounds. A further aim was to identify physicochemical parameters related to the inhalation-specific bioavailability of the compounds, and to explore their use as predictors of high vs low toxicity. 296 compounds with subacute, subchronic and chronic inhalation toxicity NOEC (no-observed effect concentration) values were subdivided into three almost equal-sized high-, medium- and low-toxic (HTox, MTox, LTox) potency classes. Whereas the derived 14 HTox and 7 LTox structural alerts yield an only moderate discrimination between these three groups, the high-toxic vs low-toxic mis-classification is very low: LTox-predicted compounds are not HTox to 97.5%, and HTox-predicted compounds not LTox to 88.6%. The probability of a compound being HTox vs LTox is triggered further by physicochemical properties encoding the tendency to evaporate from blood. The new structural alerts may aid in the predictive inhalation toxicity assessment of compounds as well as in designing low-toxicity chemicals, and provide a rationale for the chemistry underlying the toxicological outcome that can also be used for scoping targeted experimental studies.

Read-Across Prediction of the Acute Toxicity of Organic Compounds toward the Water Flea Daphnia magna.

According to the European REACH Directive, the acute daphnid toxicity needs to be assessed for industrial chemicals with market volumes ≥1 t/a. Employing a data set of 1365 organic compounds with experimental 48-h LC50 data for Daphnia magna, a read-across approach has been developed that makes use of the atom-centered fragment (ACF) method as quantitative measure for structural similarity. Both quantitative log LC50 predictions and a discrimination between narcosis-level and excess toxicity can be obtained, augmented by similarity-triggered information that characterizes a compound as inside or outside the quantitative or qualitative model domain. Reading across proceeds as interpolation of the toxicity enhancement (Te ) over predicted narcosis-level toxicity, taking experimental log Te values from similarity-selected reference compounds as input. The resultant decision tree model yields r(2) =0.85 and rms=0.66 for the subset of 757 compounds (56 %) identified as inside the quantitative model domain, and can handle further 318 compounds (23 %) with the categorical submodel, with 290 compounds (21 %) being outside its domain. The new in silico approach appears useful as ITS (Integrated Testing Strategy) tool for the daphnid toxicity assessment. The discussion includes a comparison of Kow - and LSER-predicted narcosis-level toxicity in the read-across context.

Prediction of the dissociation constant pKa of organic acids from local molecular parameters of their electronic ground state.

A quantum chemical method has been developed to estimate the dissociation constant pK(a) of organic acids from their neutral molecular structures by employing electronic structure properties. The data set covers 219 phenols (including 29 phenols with intramolecular H-bonding), 150 aromatic carboxylic acids, 190 aliphatic carboxylic acids, and 138 alcohols, with pK(a) varying by 16 units (0.38-16.80). Optimized ground-state geometries employing the semiempirical AM1 Hamiltonian have been used to quantify the site-specific molecular readiness to donate or accept electron charge in terms of both charge-associated energies and energy-associated charges, augmented by an ortho substitution indicator for aromatic compounds. The resultant regression models yield squared correlation coefficients (r(2)) from 0.82 to 0.90 and root-mean-square errors (rms) from 0.39 to 0.70 pK(a) units, corresponding to an overall (subset-weighted) r(2) of 0.86. Simulated external validation, leave-10%-out cross-validation and target value scrambling demonstrate the statistical robustness and prediction power of the derived model suite. The low intercorrelation with prediction errors from the commercial ACD package provides opportunity for a consensus model approach, offering a pragmatic way for further increasing the confidence in prediction significantly. Interestingly, inclusion of calculated free energies of aqueous solvation does not improve the prediction performance, probably because of the limited precision provided by available continuum-solvation models.

Quantitative read-across for predicting the acute fish toxicity of organic compounds.

Read-across enables the interpolation of a property for a target chemical from respective experimental data of sufficiently similar compounds. Employing a set of 692 organic compounds with experimental values for the 96 h fish toxicity toward the fathead minnow in terms of LC(50) (lethal concentration 50%) values, a read-across method has been developed that is based on atom-centered fragments (ACFs) for evaluating chemical similarity. Prediction of log LC(50) proceeds through reading across the toxicity enhancement over predicted narcosis-level toxicity in terms of the respective logarithmic ratio, log T(e), and adding the respective baseline narcosis LC(50) estimated from log K(ow) (octanol/water partition coefficient). Depending on the minimum similarity imposed on a compound to serve as read-across basis for the target chemical, three different standard settings have been introduced, allowing one to perform screening-level estimations as well as predictions with intermediate and good confidence. The respective squared correlation coefficients (r(2)) are 0.73, 0.78, and 0.87, with root-mean square errors (rms) of 0.73, 0.60, and 0.39 log units, respectively. As a general trend, increasing the ACF minimum similarity increases the prediction quality at the cost of decreasing the application range. The method has the potential to assist in the predictive evaluation of fish toxicity for regulatory purposes such as under the REACH legislation.

A new risk assessment approach for the prioritization of 500 classical and emerging organic microcontaminants as potential river basin specific pollutants under the European Water Framework Directive.

Given the huge number of chemicals released into the environment and existing time and budget constraints, there is a need to prioritize chemicals for risk assessment and monitoring in the context of the European Union Water Framework Directive (EU WFD). This study is the first to assess the risk of 500 organic substances based on observations in the four European river basins of the Elbe, Scheldt, Danube and Llobregat. A decision tree is introduced that first classifies chemicals into six categories depending on the information available, which allows water managers to focus on the next steps (e.g. derivation of Environmental Quality Standards (EQS), improvement of analytical methods, etc.). The priority within each category is then evaluated based on two indicators, the Frequency of Exceedance and the Extent of Exceedance of Predicted No-Effect Concentrations (PNECs). These two indictors are based on maximum environmental concentrations (MEC), rather than the commonly used statistically based averages (Predicted Effect Concentration, PEC), and compared to the lowest acute-based (PNEC(acute)) or chronic-based thresholds (PNEC(chronic)). For 56% of the compounds, PNECs were available from existing risk assessments, and the majority of these PNECs were derived from chronic toxicity data or simulated ecosystem studies (mesocosm) with rather low assessment factors. The limitations of this concept for risk assessment purposes are discussed. For the remainder, provisional PNECs (P-PNECs) were established from read-across models for acute toxicity to the standard test organisms Daphnia magna, Pimephales promelas and Selenastrum capricornutum. On the one hand, the prioritization revealed that about three-quarter of the 44 substances with MEC/PNEC ratios above ten were pesticides. On the other hand, based on the monitoring data used in this study, no risk with regard to the water phase could be found for eight of the 41 priority substances, indicating a first success of the implementation of the WFD in the investigated river basins.

Comparative analysis of QSAR models for predicting pK(a) of organic oxygen acids and nitrogen bases from molecular structure.

For 1143 organic compounds comprising 580 oxygen acids and 563 nitrogen bases that cover more than 17 orders of experimental pK(a) (from -5.00 to 12.23), the pK(a) prediction performances of ACD, SPARC, and two calibrations of a semiempirical quantum chemical (QC) AM1 approach have been analyzed. The overall root-mean-square errors (rms) for the acids are 0.41, 0.58 (0.42 without ortho-substituted phenols with intramolecular H-bonding), and 0.55 and for the bases are 0.65, 0.70, 1.17, and 1.27 for ACD, SPARC, and both QC methods, respectively. Method-specific performances are discussed in detail for six acid subsets (phenols and aromatic and aliphatic carboxylic acids with different substitution patterns) and nine base subsets (anilines, primary, secondary and tertiary amines, meta/para-substituted and ortho-substituted pyridines, pyrimidines, imidazoles, and quinolines). The results demonstrate an overall better performance for acids than for bases but also a substantial variation across subsets. For the overall best-performing ACD, rms ranges from 0.12 to 1.11 and 0.40 to 1.21 pK(a) units for the acid and base subsets, respectively. With regard to the squared correlation coefficient r², the results are 0.86 to 0.96 (acids) and 0.79 to 0.95 (bases) for ACD, 0.77 to 0.95 (acids) and 0.85 to 0.97 (bases) for SPARC, and 0.64 to 0.87 (acids) and 0.43 to 0.83 (bases) for the QC methods, respectively. Attention is paid to structural and method-specific causes for observed pitfalls. The significant subset dependence of the prediction performances suggests a consensus modeling approach.

Tautomer identification and tautomer structure generation based on the InChI code.

An algorithm is introduced that enables a fast generation of all possible prototropic tautomers resulting from the mobile H atoms and associated heteroatoms as defined in the InChI code. The InChI-derived set of possible tautomers comprises (1,3)-shifts for open-chain molecules and (1,n)-shifts (with n being an odd number >3) for ring systems. In addition, our algorithm includes also, as extension to the InChI scope, those larger (1,n)-shifts that can be constructed from joining separate but conjugated InChI sequences of tautomer-active heteroatoms. The developed algorithm is described in detail, with all major steps illustrated through explicit examples. Application to approximately 72,500 organic compounds taken from EINECS (European Inventory of Existing Commercial Chemical Substances) shows that around 11% of the substances occur in different heteroatom-prototropic tautomeric forms. Additional QSAR (quantitative structure-activity relationship) predictions of their soil sorption coefficient and water solubility reveal variations across tautomers up to more than two and 4 orders of magnitude, respectively. For a small subset of nine compounds, analysis of quantum chemically predicted tautomer energies supports the view that among all tautomers of a given compound, those restricted to H atom exchanges between heteroatoms usually include the thermodynamically most stable structures.

Chemical domain of QSAR models from atom-centered fragments.

A methodology to characterize the chemical domain of qualitative and quantitative structure-activity relationship (QSAR) models based on the atom-centered fragment (ACF) approach is introduced. ACFs decompose the molecule into structural pieces, with each non-hydrogen atom of the molecule acting as an ACF center. ACFs vary with respect to their size in terms of the path length covered in each bonding direction starting from a given central atom and how comprehensively the neighbor atoms (including hydrogen) are described in terms of element type and bonding environment. In addition to these different levels of ACF definitions, the ACF match mode as degree of strictness of the ACF comparison between a test compound and a given ACF pool (such as from a training set) has to be specified. Analyses of the prediction statistics of three QSAR models with their training sets as well as with external test sets and associated subsets demonstrate a clear relationship between the prediction performance and the levels of ACF definition and match mode. The findings suggest that second-order ACFs combined with a borderline match mode may serve as a generic and at the same time a mechanistically sound tool to define and evaluate the chemical domain of QSAR models. Moreover, four standard categories of the ACF-based membership to a given chemical domain (outside, borderline outside, borderline inside, inside) are introduced that provide more specific information about the expected QSAR prediction performance. As such, the ACF-based characterization of the chemical domain appears to be particularly useful for QSAR applications in the context of REACH and other regulatory schemes addressing the safety evaluation of chemical compounds.

Prediction of the intrinsic hydrogen bond acceptor strength of chemical substances from molecular structure.

Hydrogen bonding affects the partitioning of organic compounds between environmental and biological compartments as well as the three-dimensional shape of macromolecules. Using the semiempirical quantum chemical AM1 level of calculation, we have developed a model to predict the site-specific hydrogen bond (HB) acceptor strength from ground-state properties of the individual compounds. At present, the model parametrization is confined to compounds with one HB acceptor site of the following atom types: N, O, S, F, Cl, and Br that act as lone-pair HB acceptors, and pi-electron (aromatic or conjugated) systems with the associated C atoms as particularly weak HB acceptors. The HB acceptor strength is expressed in terms of the Abraham parameter B and calculated from local molecular parameters, taking into account electrostatic, polarizability, and charge transfer contributions according to the Morokuma concept. For a data set of 383 compounds, the squared correlation coefficient r2 is 0.97 when electrostatic potential (ESP) derived net atomic charges are employed, and the root-mean-square (rms) error is 0.04 that is in the range of experimental uncertainty. The model is validated using an extended leave-50%-out approach, and its performance is comparatively analyzed with the ones of earlier introduced ab initio (HF/6-31G**) and density functional theory (B3LYP/6-31G**) models as well as of two increment methods with respect to the total compound set as well as HB acceptor type subsets. The discussion includes an explorative model application to amides and organophosphates that demonstrates the robustness of the approach, and further opportunities for model extensions.

Prediction of the intrinsic hydrogen bond acceptor strength of organic compounds by local molecular parameters.

A quantum chemical model has been developed for predicting the hydrogen bond (HB) acceptor strength of monofunctional organic compounds from electronic ground-state properties of the single molecules. Local molecular parameters are used to quantify electrostatic, polarizability, and charge transfer components to hydrogen bonding, employing the ab initio and density functional theory levels HF/6-31G** and B3LYP/6-31G**. The model can handle lone pairs of intermediate and strong HB acceptor heteroatoms (N, O, S) as well as of weak HB acceptor halogens (F, Cl, Br) and includes also olefinic, alkyne, and aromatic pi-bonds as weak HB acceptor sites. The model calibration with 403 compounds and experimental values for the Abraham HB acceptor strength B yielded squared correlation coefficients r(2) around 0.95, outperforming existing fragment-based schemes. Model validation was performed applying a leave-50%-out procedure, yielding predictive squared correlation coefficients q(2) of around 0.95 for the subsets that both cover the whole chemical domain as well as (almost) the whole target value range of the data set.

Modeling the H bond donor strength of -OH, -NH, and -CH sites by local molecular parameters.

A quantum chemical model is introduced to predict the H-bond donor strength of monofunctional organic compounds from their ground-state electronic properties. The model covers -OH, -NH, and -CH as H-bond donor sites and was calibrated with experimental values for the Abraham H-bond donor strength parameter A using the ab initio and density functional theory levels HF/6-31G** and B3LYP/6-31G**. Starting with the Morokuma analysis of hydrogen bonding, the electrostatic (ES), polarizability (PL), and charge transfer (CT) components were quantified employing local molecular parameters. With hydrogen net atomic charges calculated from both natural population analysis and the ES potential scheme, the ES term turned out to provide only marginal contributions to the Abraham parameter A, except for weak hydrogen bonds associated with acidic -CH sites. Accordingly, A is governed by PL and CT contributions. The PL component was characterized through a new measure of the local molecular hardness at hydrogen, eta(H), which in turn was quantified through empirically defined site-specific effective donor and acceptor energies, EE(occ) and EE(vac). The latter parameter was also used to address the CT contribution to A. With an initial training set of 77 compounds, HF/6-31G** yielded a squared correlation coefficient, r(2), of 0.91. Essentially identical statistics were achieved for a separate test set of 429 compounds and for the recalibrated model when using all 506 compounds. B3LYP/6-31G** yielded slightly inferior statistics. The discussion includes subset statistics for compounds containing -OH, -NH, and active -CH sites and a nonlinear model extension with slightly improved statistics (r(2) = 0.92).

Indirect photolysis of organic compounds: prediction of OH reaction rate constants through molecular orbital calculations.

The MOOH approach is a perturbational molecular orbital method to predict rate constants of indirect photolysis of organic compounds through reaction with OH radicals. It employs the semiempirical AM1 scheme as the underlying quantum chemical model. The original method introduced by Klamt has been reparametrized using an up-to-date set of 675 compounds with experimental rate constants and outperforms the prominent Atkinson increment scheme for this training set as well as for an extended set of 805 compounds, yielding an overall root-mean-square error of 0.32 log units. The discussion includes detailed comparative analyses of the model performances for individual compound classes. The present model calibration refers mainly to monofunctional compounds but performs already reasonably well for multifunctional compounds. For predictive applications, both the Atkinson scheme and the alternative, independent AM1-MOOH model can be used as components of a consensus modeling approach, arriving at increased confidence in cases where the different models agree.

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.

The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient, q (2). This index relates the predictive residual sum of squares, PRESS, to the activity sum of squares, SS, without postprocessing of the model output, the latter of which is automatically done when calculating the conventional squared correlation coefficient, r (2). According to the current OECD guidelines, q (2) for external validation should be calculated with SS referring to the training set activity mean. Our present findings including a mathematical proof demonstrate that this approach yields a systematic overestimation of the prediction capability that is triggered by the difference between the training and test set activity means. Example calculations with three regression models and data sets taken from literature show further that for external test sets, q (2) based on the training set activity mean may become even larger than r (2). As a consequence, we suggest to always use the test set activity mean when quantifying the external prediction capability through q (2) and to revise the respective OECD guidance document accordingly. The discussion includes a comparison between r (2) and q (2) value ranges and the q (2) statistics for cross-validation.

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.

Acute to chronic ratios in aquatic toxicity--variation across trophic levels and relationship with chemical structure.

For fish, daphnids, and algae, acute to chronic ratios (ACRs) have been determined from experimental data regarding new and existing chemicals. Only test results in accord with the European Union Technical Guidance Document (TGD) and validated by authorities were considered. Whereas the median ACRs of 10.5 (fish), 7.0 (daphnids), and 5.4 (algae) are well below the ACR safety factor of 100 as implied by the TGD, individual ACRs vary considerably and go up to 4400. The results suggest that a safety factor of 100 is not protective for all chemicals and trophic levels. Neither a correlation between ACR and baseline toxicity as modeled through the logarithmic octanol-water partition coefficient nor an ACR correlation across trophic levels exists. Narcosis is associated with a preference for a low ACR; nevertheless, low ACRs are frequently obtained for nonnarcotics. Analysis of chemical structures led to the derivation of structural alerts to identify compounds with a significantly increased potential for a high ACR, which may prove to be useful in setting test priorities. At present, however, life-cycle tests are the only way to conservatively predict long-term toxicity.

Model selection based on structural similarity-method description and application to water solubility prediction.

A method is introduced that allows one to select, for a given property and compound, among several prediction methods the presumably best-performing scheme based on prediction errors evaluated for structurally similar compounds. The latter are selected through analysis of atom-centered fragments (ACFs) in accord with a k nearest neighbor procedure in the two-dimensional structural space. The approach is illustrated with seven estimation methods for the water solubility of organic compounds and a reference set of 1876 compounds with validated experimental values. The discussion includes a comparison with the similarity-based error correction as an alternative approach to improve the performance of prediction methods and an extension that enables an ad hoc specification of the application domain.

Prediction of the temperature dependency of Henry's law constant from chemical structure.

A new model to estimate the temperature dependency of Henry's law constant in water for organic compounds from the two-dimensional structure is presented. Air/water partition enthalpies of 456 chemicals were fitted to 46 substructural parameters with a squared correlation coefficient r2 of 0.81 and a standard error of 7.1 kJ/mol. The compound set covers various organic compound classes with the atom types C, H, N, O, F, Cl, Br, I, and S. Application of the model together with experimental data for 25 degrees C to a set of 462 compounds with 2119 experimental Henry's law constants at temperatures below 20 degrees C yields a predictive squared correlation coefficient q2 of 0.99 and a standard error of 0.21 logarithmic units. The prediction capability is further evaluated using cross validation and permutation.