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

Sale-based estimation of pharmaceutical concentrations and associated environmental risk in the Japanese wastewater system

Information on sales and emission of selected pharmaceuticals were used to predict their concentrations in Japanese wastewater influent through a >300 of pharmaceuticals data sink. A combined wastewater-based epidemiology and environmental risk analysis follow was established. By comparing predicted environmental concentrations (PECs) of pharmaceuticals in wastewater influent against measured environmental concentrations (MECs) reported in previous studies, it was found that the model gave accurate results for 17 pharmaceuticals (0.5 < PEC/MEC < 2), and acceptable results for 32 out of 40 pharmaceuticals (0.1 < PEC/MEC < 10). Although the majority of pharmaceuticals considered in the model were antibiotics and analgesics, pranlukast, a receptor antagonist, was predicted to have the highest concentration in wastewater influent. With regard to the composition of wastewater effluent, the Estimation Program Interface (EPI) suite was used to predict pharmaceutical removal through activated sludge treatment. Although the performance of the EPI suite was variable in terms of accurate prediction of the removal of different pharmaceuticals, it could be an efficient tool in practice for predicting removal under extreme scenarios. By using the EPI suite with input data on PEC in the wastewater influent, the PEC values of pharmaceuticals in wastewater effluent were predicted. The concentrations of 26 pharmaceuticals were relatively high (>1 μg/L), and the PECs of 6 pharmaceuticals were extremely high (>10 μg/L) in wastewater effluent, which could be attributed to their high usage rates by consumers and poor removal rates in wastewater treatment plants (WWTPs). Furthermore, environmental risk assessment (ERA) was carried out by calculating the ratio of predicted no effect concentration (PNEC) to PEC of different pharmaceuticals, and it was found that 9 pharmaceuticals were likely to have high toxicity, and 54 pharmaceuticals were likely to have potential toxicity. It is recommended that this is further investigated in detail. The priority screening and environmental risk assessment results on pharmaceuticals can provide reliable basis for policy-making and environmental management.

Computational approaches to chemical hazard assessment

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

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,β-unsaturated carbonyl compounds. After examining electrostatic effects on the calculated ecotoxicities of 10 α,β-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,β-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,β-unsaturated carbonyl ketones and aldehydes

To understand the key factor for fish toxicity of 11 α,β-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,β-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.