Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across.

Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as humans. Considering this scenario, it is an obligatory task to explore the toxicity data of PCNs more deeply for the species of an aquatic ecosystem (green algae-Daphnia magna-fish), and to extrapolate those data for humans. But the toxicity data for different aquatic species are quite limited. The laboratory experimentations are complicated and ethically troublesome to fill toxicity data gaps; therefore, different in silico methods (e.g., QSAR, quantitative read-across predictions) are emerging as crucial ways to fill the data gaps and hazard assessments. In the present study, we developed individual toxicity models as well as interspecies models from the 75 PCN toxicity data against three aquatic species (green algae-Daphnia magna-fish) by employing easily interpretable 2D descriptors; these models were validated rigorously employing different globally accepted internal and external validation metrics. Then we interpreted the modelled descriptors mechanistically with the endpoint values for better understanding. And finally, we endeavored to improve the prediction quality in terms of external validation metrics by employing a novel quantitative read-across approach by pooling the descriptors from the developed individual QSAR models.

Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica.

Chemicals used in our daily life show different toxic effects to the aquatic and terrestrial species and thus hamper the ecological balance. In the present time, amphibians are one of them, which are threatened to be extinct. Quantitative structure-activity relationship (QSAR) is an useful tool for prediction involving less time, money and manpower without requiring any animal experiments to assess the unavailable acute toxicity data for the untested molecules. In this study, we have developed QSAR models for ecotoxicity of some waterborne diverse aromatic compounds on an amphibian species Rana japonica (Japanese brown frog) employing Genetic Algorithm (GA) for variable selection followed by Partial Least Squares (PLS) regression method following recommendations of the Organization for Economic Co-operation and Development (OECD) for QSAR model development. Double cross-validation (DCV) followed by Best Subset Selection (BSS) were employed to select suitable models. The models displayed promising statistical quality in terms of R2 (= 0.837-0.841), Q2LOO (= 0.782-0.787), R2pred or Q2F1 (= 0.802-0.82) and some other internal and external validation metrics for tadpoles of Rana japonica (NTraining = 44, NTest = 14). These models can be applied for data gap filling for a new untested compound falling within the applicability domain (AD) of the models.

QSAR modelling of inhalation toxicity of diverse volatile organic molecules using no observed adverse effect concentration (NOAEC) as the endpoint.

Nowadays, air pollution due to urbanization and reduction of forestry is emerging as a serious threat to humans and the environment. According to the World Health Organization, respiratory diseases are the third most mortality factor in the world. Chemical research organizations and industries are producing a large number of new chemical compounds continuously. Although toxicity testing of those chemicals on animals is costly, resource and time consuming, these data cannot be properly extrapolated to humans and other animals, and also these raise ethical issues. In this background, we have developed Quantitative Structure-Activity Relationship (QSAR) models using the No Observed Adverse Effect Concentration (NOAEC) as the endpoint to assess inhalation toxicity of diverse organic chemicals, commonly used and exposed by us in our daily life. No Observed Adverse Effect Concentration (NOAEC) can be used for long term toxicity studies towards the human inhalation risk assessment, as recommended by Organization for Economic Co-operation and Development (OECD) in guidance document 39. A particular QSAR model may not be equally effective for prediction of all query compounds from a given set of compounds; therefore, we have developed multiple models, which are robust, sound and well predictive from the statistical point of view to forecast the NOAEC values for the new untested compounds. Subsequently the validated individual models were employed to generate consensus models, in order to improve the quality of predictions and to reduce prediction errors. We have investigated some crucial structural features from these models which may regulate inhalation toxicity for newly produced molecules. Thus, our developed models may help in toxicity assessment towards reducing the health hazards for new chemicals.

In silico modelling of acute toxicity of 1, 2, 4-triazole antifungal agents towards zebrafish (Danio rerio) embryos: Application of the Small Dataset Modeller tool.

Nowadays, there is a widespread use of triazole antifungal agents to kill broad classes of fungi in farming lands and to protect herbs, fruits and grains. These agents further deposit into the aquatic systems causing toxicity to the living aquatic creatures, which can then affect human beings. Considering this issue, risk assessment of these toxic chemicals is a very essential task. Due to the inadequate experimental data on acute toxicity of antifungal agents containing the 1, 2, 4-triazole ring, higher testing costs along with the regulatory restrictions and the international regulations to lessen animal testing emphasize on in silico techniques such as quantitative structure-activity relationship (QSAR) studies. The application of QSAR modelling has created an easier avenue to predict activity/property/toxicity of newly synthesized compounds. In the present study, we have used 23 antifungal agents containing the 1, 2, 4-triazole ring to develop 2D-QSAR models and explored their structural attributes crucial for acute toxicity towards embryonic phase of zebrafish (Danio rerio). Here, we have employed simple 2D descriptors to develop the QSAR models. The models were evolved by executing the Small Dataset Modeller tool (https://dtclab.webs.com/software-tools), and the validation of the models was achieved by employing different precise validation principles. The statistical validation metrics confirm that built models are robust, useful and well predictive to forecast the acute toxicity of new compounds.