Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals.

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725-742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

Development of models to predict fish early-life stage toxicity from acute Daphnia magna toxicity$.

Herein, we propose models for predicting fish early-life stage (ELS) toxicity from acute Daphnia magna toxicity and various molecular descriptors. Specifically, eight models were developed with fathead minnow (Pimephales promelas) data and were validated against Japanese medaka (Oryzias latipes) data because the quantity of available Japanese medaka data is much smaller than the quantity of fathead minnow data. The training data set for the models consisted of ELS fathead minnow toxicity data for 77 chemicals; data for 67 of the 77 chemicals originated from the OPP Pesticide Ecotoxicity Database of the US Environmental Protection Agency. The training data were biased toward pesticides. A simple quantitative activity-activity relationship (QAAR) model based on the correlation between fish ELS and acute Daphnia magna toxicities showed good predictivity for the chemicals in the external validation data set relative to the predictivities of the other models in this study. However, goodness-of-fit and robustness were better for quantitative structure-activity-activity relationship (QSAAR) models that included molecular descriptors (such as pesticide-related atoms and substructures as well as molecular weight and three-dimensional-structure-based parameters). A battery approach involving the use of both the QAAR and the QSAARs might enhance the reliability of the estimated values and prevent underestimates.

External validation of acute-to-chronic models for estimation of reproductive toxicity to Daphnia magna.

We evaluated the predictivity and applicability of previously proposed models for the reproductive toxicity of chemicals to Daphnia magna [SAR QSAR Environ. Res. 27:10, 833-850] by using external data from the United States Environmental Protection Agency database ECOTOX. These models were based on quantitative structure-activity-activity relationships (QSAARs) and a quantitative activity-activity relationship (QAAR): the models can be categorized as acute-to-chronic models with (QSAAR) and without (QAAR) structural and physicochemical (e.g. distribution coefficients, log D) descriptors. We found that the QSAAR models were suitable for chemicals with an '-NH2 attached to aromatic carbon' sub-structure, whereas the QAAR model was better for multicomponent compounds, coordination complexes, tin compounds and straight-chain primary amines. For chemicals with a known specific mode of action (e.g. pesticides and antibacterial agents and their derivatives), toxicity estimation within the acute-to-chronic framework requires special attention. We evaluated the applicability of the models on the basis of the descriptors in the models. We recommend that chemicals be pre-screened before their toxicities are estimated with these models: pre-screening enabled the estimation of the toxicities of some chemicals within the applicability domains of the models.

Acute to chronic estimation of Daphnia magna toxicity within the QSAAR framework.

We constructed models for acute to chronic estimation of the Daphnia magna reproductive toxicities of chemical substances from their Daphnia magna acute immobilization toxicities. The models combined the acute toxicities with structural and physicochemical descriptors. We used multiregression analysis and selected the descriptors for the models by means of a genetic algorithm. Of the best 100 models (as indicated by the lack of fit score), 90% included the following descriptors: acute toxicity (i.e. an activity parameter), distribution coefficient (log D) and structural indicator variables that indicate the presence of -NH2 attached to aromatic carbon and the presence of a chlorine atom. We compared the predictive abilities of five of these quantitative structure-activity-activity relationship (QSAAR) acute to chronic estimation models with the predictive ability of a simple linear regression model. The comparison revealed that inclusion of structural and physicochemical descriptors such as those in QSAAR models can improve models for extrapolation from acute to chronic toxicity. Our results also provide a QSAAR framework that is expected to be useful for the further development of chronic toxicity estimation models.

Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties.

We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities against Pseudokirchneriella subcapitata. In Step 1, using a log D-based criterion and structural alerts, we produced an interspecies QSAR between algal and acute daphnid toxicities for initial screening of chemicals. In Step 2, we categorized chemicals according to the Verhaar scheme for aquatic toxicity, and we developed QSARs for toxicities of Class 1 (non-polar narcotic) and Class 2 (polar narcotic) chemicals by means of simple regression with a hydrophobicity descriptor and multiple regression with a hydrophobicity descriptor and a quantum chemical descriptor. Using the algal toxicities of the Class 1 chemicals, we proposed a baseline QSAR for calculating their excess toxicities. In Step 3, we used structural profiles to predict toxicity either quantitatively or qualitatively and to assign chemicals to the following categories: Pesticide, Reactive, Toxic, Toxic low and Uncategorized. Although this three-step strategy cannot be used to estimate the algal toxicities of all chemicals, it is useful for chemicals within its domain. The strategy is also applicable as a component of Integrated Approaches to Testing and Assessment.

Interspecies quantitative structure-activity relationships (QSARs) for eco-toxicity screening of chemicals: the role of physicochemical properties.

In addition to molecular structure profiles, descriptors based on physicochemical properties are useful for explaining the eco-toxicities of chemicals. In a previous study we reported that a criterion based on the difference between the partition coefficient (log POW) and distribution coefficient (log D) values of chemicals enabled us to identify aromatic amines and phenols for which interspecies relationships with strong correlations could be developed for fish-daphnid and algal-daphnid toxicities. The chemicals that met the log D-based criterion were expected to have similar toxicity mechanisms (related to membrane penetration). Here, we investigated the applicability of log D-based criteria to the eco-toxicity of other kinds of chemicals, including aliphatic compounds. At pH 10, use of a log POW - log D > 0 criterion and omission of outliers resulted in the selection of more than 100 chemicals whose acute fish toxicities or algal growth inhibition toxicities were almost equal to their acute daphnid toxicities. The advantage of log D-based criteria is that they allow for simple, rapid screening and prioritizing of chemicals. However, inorganic molecules and chemicals containing certain structural elements cannot be evaluated, because calculated log D values are unavailable.

Interspecies quantitative structure-activity-activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols.

We propose interspecies quantitative structure-activity-activity relationships (QSAARs), that is, QSARs with descriptors, to estimate species-specific acute aquatic toxicity. Using training datasets consisting of more than 100 aromatic amines and phenols, we found that the descriptors that predicted acute toxicities to fish (Oryzias latipes) and algae were daphnia toxicity, molecular weight (an indicator of molecular size and uptake) and selected indicator variables that discriminated between the absence or presence of various substructures. Molecular weight and the selected indicator variables improved the goodness-of-fit of the fish and algae toxicity prediction models. External validations of the QSAARs proved that algae toxicity could be predicted within 1.0 log unit and revealed structural profiles of outlier chemicals with respect to fish toxicity. In addition, applicability domains based on leverage values provided structural alerts for the predicted fish toxicity of chemicals with more than one hydroxyl or amino group attached to an aromatic ring, but not for fluoroanilines, which were not included in the training dataset. Although these simple QSAARs have limitations, their applicability is defined so clearly that they may be practical for screening chemicals with molecular weights of ≤364.9.

Development of ecotoxicity QSAR models based on partial charge descriptors for acrylate and related compounds.

Using Gasteiger's partial equalization of orbital electronegativity (PEOE) method, we constructed ecotoxicity prediction equations based on two-dimensional descriptors for α,ß-unsaturated carbonyl compounds. After examining electrostatic effects on the calculated ecotoxicities of 10 α,ß-unsaturated ketones and aldehydes (A-group compounds) by using the Mulliken atomic charges on the carbonyl oxygen atoms, we investigated the efficacy of the PEOE descriptors for the same 10 compounds and the correlation between the PEOE descriptors and the Mulliken charge. We then constructed QSAR models for acute fish and Daphnia toxicities by using the PEOE descriptors for acrylic acids and compounds with acrylate-like substructures (CH-group compounds). In the constructed models, the adjusted squared correlation coefficients between measured and calculated toxicities with the lowest Akaike information criterion were 0.77 and 0.79, respectively. The applicability of the constructed models was then evaluated for various methacrylates and similar compounds (CH(3)-group compounds). Both the fish and the Daphnia toxicities of some of the CH(3)-group compounds were underestimated by these models. Nevertheless, we concluded that the QSAR models based on the PEOE descriptors were practical for predicting acute toxicity, especially for α,ß-unsaturated carbonyl compounds with an α-hydrogen. Combining hydrophobicity and PEOE descriptors led to accurate predictions for fish toxicity.

Consideration of reactivity to acute fish toxicity of α,ß-unsaturated carbonyl ketones and aldehydes.

To understand the key factor for fish toxicity of 11 α,ß-unsaturated carbonyl aldehydes and ketones, we used quantum chemical calculations to investigate their Michael reactions with methanethiol or glutathione. We used two reaction schemes, with and without an explicit water molecule (Scheme-1wat and Scheme-0wat, respectively), to account for the effects of a catalytic water molecule on the reaction pathway. We determined the energies of the reactants, transition states (TS), and products, as well as the activation energies of the reactions. The acute fish toxicities of nine of the carbonyl compounds were evaluated to correlate with their hydrophobicities; no correlation was observed for acrolein and crotonaldehyde. The most toxic compound, acrolein, had the lowest activation energy. The activation energy of the reaction could be estimated with Scheme-1wat but not with Scheme-0wat. The complexity of the reaction pathways of the compounds was reflected in the difficulty of the TS structure searches when Scheme-1wat was used with the polarizable continuum model. The theoretical estimations of activation energies of α,ß-unsaturated carbonyl compounds with catalytic molecules or groups including hydrogen-bond networks may complement traditional tools for predicting the acute aquatic toxicities of compounds that cannot be easily obtained experimentally.

Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE).

The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure-Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructures under identical C- or log P-judgements in KATE. However, in the case of the Daphnia end-point this was not so, and the group of chemicals with reactive substructures did not always have higher RMSEs: the Schiff base mechanism did not function as a high error detector. In addition to the RMSE findings, the presence of outliers suggested that the KATE classification rules needs to be reconsidered, particularly for the amine group. Examination of the dependency of the organism on the toxic action of chemicals in fish and Daphnia revealed that some of the reactive substructures could be applied to the improvement of the KATE system. It was concluded that the reaction mechanistic domains of toxic action for skin sensitisation could provide useful complementary information in predicting acute aquatic ecotoxicity, especially at the fish end-point.

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version.

The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure-activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r(2 )> 0.7, RMSE 5, give acceptable q(2) values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.

Reactions associated with ionization in water: a direct ab initio dynamics study of ionization in (H2O)17.

Quasiclassical ab initio simulations of the ionization dynamics in a (H(2)O)(17) cluster, the first water cluster that includes a fourfold coordinated (internally solvated) water molecule, have been carried out to obtain a detailed picture of the elementary processes and energy redistribution induced by ionization in a model of aqueous water. General features observable from the simulations are the following: (i) well within 100 fs following the ionization, one or more proton transfers are seen to take place from the "ionized molecule" to neighboring molecules and beyond, forming a hydronium ion and a hydroxyl radical; (ii) two water molecules close to the ionized water molecule play an important role in the reaction, in what we term a "reactive trimer." The reaction time is gated by the encounter of the ionized water molecule with these two neighboring molecules, and this occurs anytime between 10 and 50 fs after the ionization. The distances of approach between the ionized molecule and the neighboring molecules indeed display best the time characteristics of the transfer of a proton, and thus of the formation of a hydronium ion and a OH radical. These findings are consistent with those for smaller cyclic clusters, albeit the dynamics of the proton transfer displays more varieties in the larger cluster than in the small cyclic clusters. We used a partitioning scheme for the kinetic energy in the (H(2)O)(17) system that distinguishes between the reactive trimer and the surrounding "medium." The analysis of the simulations indicates that the kinetic energy of the surrounding medium increases markedly right after the event of ionization, a manifestation of the local heating of the medium. The increase in kinetic energy is consistent with a reorganization of the surrounding medium, electrostatically forced in a very short time by the water cation and in a longer time by the formation of the hydronium ion.