Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach.

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness [Formula: see text] and external predictivity [Formula: see text] The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?

The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.

Representing and describing nanomaterials in predictive nanoinformatics.

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.