Abatement of the ecotoxicological risk of landfill leachate by heterogeneous Fenton-like oxidation.

Landfill leachates are highly contaminated liquid waste, and their treatment and detoxification are a challenging task. The current system of ecotoxicological risk assessment is complex and time-consuming. It is of fundamental importance to develop simpler and faster tools for the evaluation of the treated liquid waste and for an easier preliminary screening of the most active catalytic formulation/reaction conditions of the Fenton-like process. Here, several analytical techniques have been used for the assessment of the reduction of toxicity of the landfill leachate after Fenton process over copper-zirconia catalyst (ZrCu). Ultraviolet-visible (UV-vis) spectroscopy and absorbable organic halogens (AOX) analysis have been coupled to achieve further insight into the degradation of contaminants. In addition, for the first time, the qualitative abatement of organic compounds is monitored through proton nuclear magnetic resonance (1H NMR) analysis, providing a new method for evaluating the effectiveness of the treatment. Spectroscopic techniques reveal that the Fenton process induces a significant abatement of the aromatic and halogen compounds (51%) in the landfill leachate with a reduction of the toxicity that has been confirmed by ecotoxicological test with algae. These results validate the investigated tool for a simple rapid preliminary evaluation of the detoxification efficacy.

Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity.

The risk-characterization of chemicals requires the determination of repeated-dose toxicity (RDT). This depends on two main outcomes: the no-observed-adverse-effect level (NOAEL) and the lowest-observed-adverse-effect level (LOAEL). These endpoints are fundamental requirements in several regulatory frameworks, such as the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and the European Regulation of 1223/2009 on cosmetics. The RDT results for the safety evaluation of chemicals are undeniably important; however, the in vivo tests are time-consuming and very expensive. The in silico models can provide useful input to investigate sub-chronic RDT. Considering the complexity of these endpoints, involving variable experimental designs, this non-testing approach is challenging and attractive. Here, we built eight in silico models for the NOAEL and LOAEL predictions, focusing on systemic and organ-specific toxicity, looking into the effects on the liver, kidney and brain. Starting with the NOAEL and LOAEL data for oral sub-chronic toxicity in rats, retrieved from public databases, we developed and validated eight quantitative structure-activity relationship (QSAR) models based on the optimal descriptors calculated by the Monte Carlo method, using the CORAL software. The results obtained with these models represent a good achievement, to exploit them in a safety assessment, considering the importance of organ-related toxicity.

In Silico Methods for Chromosome Damage.

Due to the link with serious adverse health effects, genotoxicity is an important toxicological endpoint in each regulatory setting with respect to human health, including for pharmaceuticals. To this extent, a compound potential to induce gene mutations as well as chromosome damage needs to be addressed. For chromosome damage, i.e., the induction of structural or numerical chromosome aberrations, several in vitro and in vivo test methods are available. In order to rapidly collect toxicological data without the need for test material, several in silico tools for chromosome damage have been developed over the last years. In this chapter, a battery of freely available in silico chromosome damage prediction tools for chromosome damage is applied on a dataset of pharmaceuticals. Examples of the different outcomes obtained with the in silico battery are provided and briefly discussed. Furthermore, results for coumarin are presented in more detail as a case study. Overall, it can be concluded that although they are in general less developed than those for mutagenicity, in silico tools for chromosome damage can provide valuable information, especially when combined in a battery.

Skin sensitization quantitative QSAR models based on mechanistic structural alerts.

Allergic contact dermatitis is increasingly of interest for the hazard characterization of chemicals. in vivo animal testing is usually adopted but in silico approaches are becoming the new frontier due to their swiftness and economic efficiency. Indeed, in silico models can rationalise the experimental outcomes besides having predictive ability. The aim of the present work was to explore the electrophilic chemical behaviour responsible for allergic contact dermatitis using quantitative QSAR regression models. Eight models were proposed, using an experimental LLNA dataset of 366 chemicals. Each model is unique to encode a type of electrophilic reactivity domain. The models were obtained using autocorrelation, electro-topological and atom centered fragment based on two-dimensional descriptors, which incorporated the electronic and stereochemical features of substances interacting with skin proteins to induce skin cell proliferation. Finally, simple steps were proposed to integrate the eight models for the application on the test chemicals.

QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida.

Soil pollution is a critical environmental challenge: the substances released in the soil can adversely affect humans and the ecosystem. Several bioassays were developed to investigate the soil ecotoxicity of chemicals with soil microbes, plants, invertebrates and vertebrates. The 28-day collembolan reproduction test with the springtail Folsomia candida is a recently introduced bioassay described by OECD guideline 232. Although the importance of springtails for maintaining soil quality, toxicity data for Collembola are still limited. We have developed two QSAR models for the prediction of reproductive toxicity induced by organic compounds in Folsomia candida using 28 days NOEC data. We assembled a dataset with the highest number of compounds available so far: 54 compounds were collected from publicly available sources, including plant protection products, reactive intermediates and industrial chemicals, household and cosmetic ingredients, drugs, environmental transformation products and polycyclic aromatic hydrocarbons. The models were developed using partial least squares regression (PLS) and the Monte Carlo technique with respectively the open source tools Small Dataset Modeler and CORAL software. Both QSAR models gave good predictive performance even though based on a small dataset, so they could serve for the ecological risk assessment of chemicals for terrestrial organisms.

Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus.

Growing interest in environmental toxicity assessment using Thamnocephalus platyurus as organism has led to an increased availability of acute toxicity data. Despite this growing interest in tests with this organism, however, to the best of our knowledge there are no computational models to predict the acute toxicity in T. platyurus. In view of the limited number of in silico models for this crustacean, we developed Quantitative Structure-Activity Relationship (QSAR) models for the prediction of acute toxicity towards T. platyurus, reflected by the 24h LC50, using publicly available data according to the ISO 14380:2011 guideline. Two models were developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). We used partial least squares and gradient boosting machine techniques, which gave encouraging statistical quality in our data set.

SpheraCosmolife: a new tool for the risk assessment of cosmetic products.

A new, freely available software for cosmetic products has been designed that considers the regulatory framework for cosmetics. The software allows an overall toxicological evaluation of cosmetic ingredients without the need for additional testing and, depending on the product type, it applies defined exposure scenarios to derive risk for consumers. It takes regulatory thresholds into account and uses either experimental values, if available, or predictions. Based on the exper­imental or predicted no observed adverse effect level (NOAEL), the software can define a point of departure (POD), which is used to calculate the margin of safety (MoS) of the query chemicals. The software also provides other toxico­logical properties, such as mutagenicity, skin sensitization, and the threshold of toxicological concern (TTC) to provide an overall evaluation of the potential chemical hazard. Predictions are calculated using in silico models implemented within the VEGA software. The full list of ingredients of a cosmetic product can be processed at the same time, at the effective concentrations in the product as given by the user. SpheraCosmolife is designed as a support tool for safety assessors of cosmetic products and can be used to prioritize the cosmetic ingredients or formulations according to their potential risk to consumers. The major novelty of the tool is that it wraps a series of models (some of them new) into a single, user-friendly software system.

Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a "One Health" perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.

Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo.

Developmental toxicity refers to the occurrence of adverse effects on a developing organism as a consequence of exposure to hazardous chemicals. The assessment of developmental toxicity has become relevant to the safety assessment process of chemicals. The zebrafish embryo developmental toxicology assay is an emerging test used to screen the teratogenic potential of chemicals and it is proposed as a promising test to replace teratogenic assays with animals. Supported by the increased availability of data from this test, the developmental toxicity assay with zebrafish has become an interesting endpoint for the in silico modelling. The purpose of this study was to build up quantitative structure-activity relationship (QSAR) models. In this work, new in silico models for the evaluation of developmental toxicity were built using a well-defined set of data from the ToxCastTM Phase I chemical library on the zebrafish embryo. Categorical and continuous QSAR models were built by gradient boosting machine learning and the Monte Carlo technique respectively, in accordance with Organization for Economic Co-operation and Development principles and their statistical quality was satisfactory. The classification model reached balanced accuracy 0.89 and Matthews correlation coefficient 0.77 on the test set. The regression model reached correlation coefficient R2 0.70 in external validation and leave-one-out cross-validated Q2 0.73 in internal validation.

New in silico models to predict in vitro micronucleus induction as marker of genotoxicity.

The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.

Chemometric modeling to predict air half-life of persistent organic pollutants (POPs).

We have reported here a quantitative structure-property relationship (QSPR) model for prediction of air half-life of organic chemicals using a dataset of 302 diverse organic chemicals employing only two-dimensional descriptors with definite physicochemical meaning in order to avoid the computational complexity for higher dimensional molecular descriptors. The developed model was rigorously validated using the internationally accepted internal and external validation metrics. The final partial least squares (PLS) regression model obtained at three latent variables comprises six simple and interpretable 2D descriptors. The simple and highly robust model with good quality of predictions explains 66% for the variance of the training set (R2) (64% in terms of LOO variance (Q2)) and 76% for test set variance (R2pred) (prediction quality). This model might be applicable for data gap filling for determination of POPs in the environment, in case of new or untested chemicals falling within the applicability domain of the model. In general, the model indicates that the air half-life of organic chemicals increases with presence of H-bond acceptor atoms, number of halogen atoms and presence of the R-CH-X fragment and lipophilicity, and decreases with presence of a number of halogens on ring C(sp3) (substitution of halogen atoms on a ring).

QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors.

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure-activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.

Organoids are promising tools for species-specific in vitro toxicological studies.

Organoids are three-dimensional self-aggregating structures generated from stem cells (SCs) or progenitor cells in a process that recapitulates molecular and cellular stages of early organ development. The differentiation process leads to the appearance of specialized mature cells and is connected with changes in the organoid internal structure rearrangement and self-organization. The formation of organ-specific structures in vitro with highly ordered architecture is also strongly influenced by the extracellular matrix. These features make organoids as a powerful model for in vitro toxicology. Nowadays this technology is developing very quickly. In this review we present, from a toxicological and species-specific point of view, the state of the art of organoid generation from adult SCs and pluripotent SCs: embryonic SCs or induced pluripotent SCs. The current culture organoid techniques are discussed for their main advantages, disadvantages and limitations. In the second part of the review, we concentrated on the characterization of species-specific organoids generated from tissue-specific SCs of different sources: mammary (bovine), epidermis (canine), intestinal (porcine, bovine, canine, chicken) and liver (feline, canine).

Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis.

Organic compounds (OCs) constitute an enormously large class of highly persistent and toxic chemicals widely used for various purposes throughout the world. Their increased detection in water bodies, mainly sewage treatment plants via industrial discharge, has rendered them to become a cause for ecological concern. The limited availability of experimental toxicological data has necessitated development of models that can help us identify the most hazardous and potentially toxic compounds thus prioritizing the experiments on the selected chemicals. Computational tools such as quantitative structure-activity relationship (QSAR) can be used to predict the missing data and classify the chemicals based on their acute predicted responses for existing as well as not yet synthesized chemicals. In the current study, novel, externally validated, highly robust local QSAR models for different chemical classes and moderately robust global QSAR models were developed using partial least squares (PLS) regression technique using a large dataset of 1121 OCs for the fish mortality endpoint. For feature selection, genetic algorithm along with stepwise regression was used while model validation was performed using various stringent validation criteria following the strict rules of OECD guidelines of QSAR validation. The variables included in the models were obtained from simplex representation of molecular structures (SiRMS) (Version 4.1.2.270), Dragon (Version 7.0) and PaDEL-descriptor software (Version 2.20). The final developed models were robust, externally predictive and characterized by a large chemical as well as biological domain. The predictive efficiency of the developed models was then compared with the ECOSAR tool in order to justify the applicability of the developed models in ecotoxicological predictions for organic chemicals. Better predictive efficiency of the developed QSAR models compared to the ECOSAR derived predictions signifies their applicability in early risk assessment of known as well as untested chemicals in order to design safer alternatives to the environment.

QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors.

In the recent years, ecotoxicological hazard potential of biocidal products has been receiving increasing attention in the industries and regulatory agencies. Biocides/pesticides are currently one of the most studied groups of compounds, and their registration cannot be done without the empirical toxicity information. In view of limited experimental data available for these compounds, we have developed Quantitative Structure-Activity Relationship (QSAR) models for the toxicity of biocides to fish and Daphnia magna following principles of QSAR modeling recommended by the OECD (Organization for Economic Cooperation and Development). The models were developed using simple and interpretable 2D descriptors and validated using stringent tests. Both models showed encouraging statistical quality in terms of determination coefficient R2 (0.800 and 0.648), cross-validated leave-one-out Q2 (0.760 and 0.602) and predictive R2pred or Q2ext (0.875 and 0.817) for fish (nTraining = 66, nTest = 22) and Daphnia magna (nTraining = 100, nTest = 33) toxicity datasets, respectively. These models should be applicable for data gap filling in case of new or untested biocidal compounds falling within the applicability domain of the models. In general, the models indicate that the toxicity increases with lipophilicity and decreases with polarity, branching and unsaturation. We have also developed interspecies toxicity models for biocides using the daphnia and fish toxicity data and used the models for data gap filling.

Phytotoxicity of wear debris from traditional and innovative brake pads.

Traffic-related emissions include gas and particles that can alter air quality and affect human and environmental health. Limited studies have demonstrated that particulate debris thrown off from brakes are toxic to higher plants. The acute phytotoxicity of brake pad wear debris (BPWD) investigated using cress seeds grown in soil contaminated with increasing concentrations of debris. Two types of pads were used: a commercially available phenol based pad and an innovative cement-based pad developed within of the LIFE+ COBRA project. The results suggested that even through the BPWD generated by the two pads were similar in and morphology, debris from traditional pads were more phytotoxic than that from cementitious pads, causing significant alterations in terms of root elongation and loss of plasma membrane integrity.