A new classification algorithm based on mechanisms of action

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.

Effect of monosodium glutamate on serum sex hormones and uterine histology in female rats along with its molecular docking and in-silico toxicity

Monosodium glutamate (MSG) is commonly used worldwide as a food flavour enhancer by the food industry. The current study investigated the in vivo toxic effects of MSG on the uterus in adult female Sprague Dawley rats and in vitro using MCF-7 and MDA-MB-231 cells, computational toxicity and molecular docking. The average levels of progesterone and oestrogen in the MSG-treated animals significantly altered. Besides, the average uterine lumen area (μm²) was smaller than the control group. MSG showed high-affinity binding to acetylcholine receptors and disrupted the normal nerve signal with a predicted LD50 of 4500 mg/kg. MSG also demonstrated good binding affinity to human oestrogen receptors beta and some other proteins that have an oxidative stress role in the female reproductive organs. Therefore, a precaution should be taken when utilising this compound, especially for females under the risk factor of hormonal abnormality.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

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.

Aquatic Toxicity Calculation of Mixtures: A Chemical Activity Approach Incorporating a Bioavailability Reduction Concept

A calculation estimating the effect concentration (EL/LL50) of a water-accommodated fraction (WAF) for mixture toxicity is proposed. The method is based on chemical activity where the activity of a molecule is its effective concentration taking into account intermolecular interactions. First, the thermodynamic influence of each constituent on the solubility of the others within the mixture (i.e. the concentration of each constituent in the "loading rate") is determined. Then, the non-bioavailable fraction is determined and removed to calculate the true concentration of each constituent exerting toxicity. Finally, the loading rate is adjusted until the sum of activities of the bioavailable fractions is equal to the fraction-weighted average of toxic activity of each constituent. This process is a mechanistic interpretation of experimental WAF tests. The methodology has been validated comparing toxic loading rates of 13 reliable experimental WAF studies on fish, daphnids, and algae. The predictions were all within a factor of 2 of the study outcomes and can be considered as accurate as the laboratory studies. This is in contrast to the standard additivity method which consistently overestimates the toxicity of these mixtures by at least a factor of 2 up to over an order of magnitude or even more.

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.

How in silico and QSAR approaches can increase confidence in environmental hazard and risk assessment

In silico methods are typically underrated in the current risk assessment paradigm, as evidenced by the recent document from the European Chemicals Agency (ECHA) on animal alternatives, in which quantitative structure-activity relationships (QSARs) were practically used only as a last resort. Their primary use is still to provide supporting evidence for read-across strategies or to add credence to experimental results of unknown or limited validity (old studies, studies without good laboratory practices [GLPs], limited information reported, etc.) in hazard assessment, but under the pressure of increasing burdens of testing, industry and regulators alike are at last warming to them. Nevertheless, their true potential for data-gap filling and for resolving sticking points in risk assessment methodology and beyond has yet to be recognized. We postulate that it is possible to go beyond the level of simply increasing confidence to the point of using in silico approaches to accurately predict results that cannot be resolved analytically. For example, under certain conditions it is possible to obtain meaningful results by in silico extrapolation for tests that would be technically impossible to conduct in the laboratory or at least extremely challenging to obtain reliable results. The following and other concepts are explored in this article: the mechanism of action (MechoA) of the substance should be determined, as an aid verifying that the QSAR model is applicable to the substance under review; accurate QSARs should be built with high-quality data that were not only curated but also validated with expert judgment; although a rule of thumb for acute to chronic ratios appears applicable for nonpolar narcotics, it seems unlikely that a "one-value-fits-all" answer exists for other MechoAs; a holistic approach to QSARs can be employed (via reverse engineering) to help validate or invalidate an experimental endpoint value on the basis of multiple experimental studies. Integr Environ Assess Manag 2019;15:40-50. © 2018 SETAC.