Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches

Addressing data gaps in deriving aquatic life ambient water quality criteria for contaminants of emerging concern: Challenges and the potential of in silico methods

The international community is becoming increasingly aware of the threats posed by contaminants of emerging concern (CECs) for ecological security. Aquatic life ambient water quality criteria (WQC) are essential for the formulation of risk prevention and control strategies for pollutants by regulatory agencies. Accordingly, we systematically evaluated the current status of WQC development for typical CECs through literature review. The results revealed substantial disparities in the WQC for the same chemical, with the coefficients of variation for all CECs exceeding 0.3. The reliance on low-quality data, high-uncertainty derivation methods, and limited species diversity highlights a substantial data gap. Newly developed in silico methods, with potential to predict the toxicity of untested chemicals, species, and conditions, were classified and integrated into a traditional WQC derivation framework to address the data gap for CECs. However, several challenges remain before such methods can achieve widespread acceptance. These include unstable model performance, the inability to predict chronic toxicity, undefined model applicability, difficulties in specifying toxicity effects and predicting toxicity for certain key species. Future research should prioritize: 1) improving model accuracy by developing specialized models trained with relevant, chemical-specific data or integrating chemical-related features into interspecies models; 2) enhancing species generalizability by developing multispecies models; 3) facilitating the derivation of environmentally relevant WQC by incorporating condition-related features into models; and 4) improving the regulatory acceptability of in silico methods by evaluating the reliability of "black-box" models.

Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling

The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines. Algae, daphnia, and fish, recognized as standard organisms in toxicity testing, are crucial bio-indicators due to their size, transparency, adaptability, and regulatory acceptance. We used partial least squares (PLS) and multiple linear regression (MLR) methods for model development alongside machine learning techniques such as Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbor (kNN), and Neural Networks (NN) to enhance the predictivity. Lipophilicity, electronegativity, unsaturation, a molecular cyclized degree in molecular structure, large fragments, aliphatic secondary C(sp2), and R-CR-R groups were identified as critical biomarkers for API toxicity. Screening of the PPDB (pesticide properties databases) and DrugBank validated the practical application of these models, offering valuable tools for regulatory decisions, safer API design, and the preservation of aquatic biodiversity.

Comparison of modes of toxic action between Rana chensinensis tadpoles and Limnodrilus hoffmeisteri worms based on interspecies correlation, excess toxicity and QSAR for class-based compounds

Insecticides, fungicides, dinitrobenzenes, resorcinols, phenols and anilines are widely used in agricultural and industrial productions. However, their modes of toxic action are unclear in some nontarget organisms, such as worms and tadpoles. In this study, acute toxicity data was experimentally collected for Limnodrilus hoffmeisteri worms and Rana chensinensis tadpoles, respectively. Interspecies correlation and excess toxicity were calculated to determine modes of action (MOAs) between the two species for class-based compounds. The result showed that, although the interspecies correlation of toxicity between the tadpoles and worms is significant with a coefficient of determination (R²) of 0.83, tadpoles are more sensitive than the worms and toxicity values between these two species are not identical with an overall 0.43 log unit difference. Regression analysis revealed that the toxicity of nonpolar narcotics or baseline compounds is linearly related to hydrophobicity for both the tadpoles and worms and the two baseline models are parallel, suggesting that these nonpolar narcotics share the same MOA between the two species. The difference of baseline toxicities between the two species is attributed to differences in bioconcentration factors. Analysis of the excess toxicity calculated from the toxicity ratio (TR) suggested that phenols and anilines can be classified as polar narcotics, not only to fish, but also to the tadpoles and worms. These compounds are more toxic than the baseline compounds and quantitative structure-activity relationship (QSAR) models show that their toxicity is linearly related to chemical hydrophobicity and polarity. Analysis of the excess toxicity reveals that aminophenols and resorcinols can be classified as reactive compounds, and insecticides and fungicides can be classified as specifically-acting compounds for both species. These compounds exhibited significantly greater toxic effect to both the tadpoles and worms. QSAR models have been developed to describe the toxic mechanisms for nonpolar narcotics, polar narcotics, reactive chemicals and specifically-acting compounds, and a theoretical equation has been derived to explain the effect of bio-uptake and interaction of the chemical with target receptors for both tadpole and worm toxicity. Our study reveals that tadpole toxicity can be estimated from worm toxicity data and the two species can serve as surrogates for each other in the safety evaluation of organic pollutants.

Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use

In silico models are used to predict toxicity and molecular properties in chemical safety assessment, gaining widespread regulatory use under a number of legislations globally. This study has rationalised previously published criteria to evaluate quantitative structure-activity relationships (QSARs) in terms of their uncertainty, variability and potential areas of bias, into ten assessment components, or higher level groupings. The components have been mapped onto specific regulatory uses (i.e. data gap filling for risk assessment, classification and labelling, and screening and prioritisation) identifying different levels of uncertainty that may be acceptable for each. Twelve published QSARs were evaluated using the components, such that their potential use could be identified. High uncertainty was commonly observed with the presentation of data, mechanistic interpretability, incorporation of toxicokinetics and the relevance of the data for regulatory purposes. The assessment components help to guide strategies that can be implemented to improve acceptability of QSARs through the reduction of uncertainties. It is anticipated that model developers could apply the assessment components from the model design phase (e.g. through problem formulation) through to their documentation and use. The application of the components provides the possibility to assess QSARs in a meaningful manner and demonstrate their fitness-for-purpose against pre-defined criteria.

Virtual screening and assessment of anticancer potential of selenium-based compounds against HL-60 and MCF7 cells

Aim: Selenium-based compounds have antitumor potential. We used a ligand-based virtual screening analysis to identify selenoglycolicamides with potential antitumor activity. Results & Conclusion: Compounds 3, 6, 7 and 8 were selected for in vitro cytotoxicity tests against various cell lines, according to spectrophotometry results. Compound 3 presented the best cytotoxicity results against a promyelocytic leukemia line (HL-60) and was able to induce cell death at a frequency similar to that observed for doxorubicin. The docking study showed that compound 3 has good interaction energies with the targets caspase-3, 7 and 8, which are components of the apoptotic pathway. These results suggested that selenium has significant pharmacological potential for the selective targeting of tumor cells, inducing molecular and cellular events that culminate in tumor cell death.

Evaluation of modes of action of pesticides to Daphnia magna based on QSAR, excess toxicity and critical body residues

Agricultural pesticides serve as effective controls of unwanted weeds and pests. However, these same chemicals can exert toxic effects in non-target organisms. To determine chemical modes of action, the toxicity ratio (TR) and critical body residues (CBRs) of 57 pesticides were calculated for Daphnia magna. Results showed that the CBR values of inert compounds were close to a constant while the CBR values of pesticides varied over a wider range. Although herbicides are categorized as specifically-acting compounds to plants, herbicides did not exhibit excess toxicity to Daphnia magna and were categorized as inert compounds with an average logTR = 0.41, which was less than a threshold of one. Conversely, fungicides and insecticides exhibited strong potential for toxic effects to Daphnia magna with an average logTR >2. Many of these chemicals act via disruption of the nervous, respiratory, or reproductive system, with high ligand-receptor binding activity which leads to higher toxicity for Daphnia magna. Molecular docking using acetylcholinesterase revealed that fungicides and insecticides bind more easily with the biological macromolecule when compared with inert compounds. Quantitative structure-activity relationship (QSAR) analysis revealed that the toxicity of fungicides was mainly dependent upon the heat of formation and polar surface area, while the toxicity of insecticides was more related to hydrogen-bond properties. This comprehensive analysis reveals that there are specific differences in toxic mechanisms between fungicides and insecticides. These results are useful for determining relative risk associated with pesticide exposure to aquatic crustaceans, such as Daphnia magna.

MOA-based linear and nonlinear QSAR models for predicting the toxicity of organic chemicals to Vibrio fischeri

Risk assessment of pollutants to humans and ecosystems requires much toxicological data. However, experimental testing of compounds expends a large number of animals and is criticized for ethical reasons. The in silico method is playing an important role in filling the data gap. In this paper, the acute toxicity data of 1221 chemicals to Vibrio fischeri were collected. The global models obtained showed that there was a poor relationship between the toxicity data and the descriptors calculated based on linear and nonlinear regression analysis. This is due to the fact that the studied compounds contain not only non-reactive compounds but also reactive and specifically acting compounds with different modes of action (MOAs). MOAs are fundamental for the development of mechanistically based QSAR models and toxicity prediction. To investigate MOAs and develop MOA-based prediction models, the compounds were classified into baseline, less inert, reactive, and specifically acting compounds based on the modified Verhaar's classification scheme. Satisfactory models were established by multivariate linear regression (MLR) and support vector machine (SVM) analysis not only for baseline and less inert chemicals, but also for reactive and specifically acting compounds. Compared with linear models obtained by the MLR method, the nonlinear models obtained by the SVM method had better performance. The cross validation proved that all of the models were robust except for those for reactive chemicals with nN (number of nitrogen atoms) = 0 and n(C=O) (number of carbonyl groups) > 0 (Q²_ext < 0.5). The application domains and outliers are discussed for those MOA-based models. The models developed in this paper are significantly helpful not only because the application domains for baseline and less inert compounds have been expended, but also the toxicity of reactive and specifically acting compounds can be successfully predicted. This work will promote understanding of toxic mechanisms and toxicity prediction for the chemicals with structural diversity, especially for reactive and specifically acting compounds.

Computational prediction of toxicity of small organic molecules: state-of-the-art

The field of computational prediction of various toxicity end-points has evolved over last two decades significantly. Availability of newer modelling techniques, powerful computational resources and good-quality data have made it possible to generate reliable predictions for new chemical entities, impurities, chemicals, natural products and a lot of other substances. The field is still undergoing metamorphosis to take into account molecular complexities underlying toxicity end-points such as teratogenicity, mutagenicity, carcinogenicity, etc. Expansion of the applicability domain of these predictive models into areas other than life sciences, such as environmental and materials sciences have received a great deal of attention from all walks of life, fuelling further development and growth of the field. The present chapter discusses the state-of-the-art computational prediction of toxicity end-points of small organic molecules to balance the trade-off between the molecular complexity and the quality of such predictions, without compromising their immense utility in many fields.

Use of the index of ideality of correlation to improve models of eco-toxicity

Persistent organic pollutants are compounds used for various everyday purposes, such as personal care products, food, pesticides, and pharmaceuticals. Decomposition of considerable part of the above pollutants is a long-time process. Under such circumstances, estimation of toxicity for large arrays of organic substances corresponding to the above category of pollutants is a necessary component of theoretical chemistry. The CORAL software is a tool to establish quantitative structure-activity relationships (QSARs). The index of ideality of correlation (IIC) was suggested as a criterion of predictive potential of QSAR. The statistical quality of models for eco-toxicity of organic pollutants, which are built up, with use of the IIC is better than statistical quality of models, which are built up without use of data on the IIC.

Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints

The CORAL software is a tool to build up quantitative structure-property/activity relationships (QSPRs/QSARs). The project of updated version of the CORAL software is discussed in terms of practical applications for building up various models. The updating is the possibility to improve the predictive potential of models using the so-called Index of Ideality of Correlation (IIC) as a criterion of the predictive potential for QSPR/QSAR models. Efficacy of the IIC is examined with three examples of building up QSARs: (i) models for anticancer activity; (ii) models for mutagenicity; and (iii) models for toxicity of psychotropic drugs. The validation of these models has been carried out with several splits into the training, invisible training, calibration, and validation sets. The ability of IIC to be an indicator of predictive potential of QSAR models is confirmed. The updated version of the CORAL software (CORALSEA-2017, http://www.insilico.eu/coral ) is available on the Internet.