Investigating landfill leachate toxicity in vitro: A review of cell models and endpoints

Landfill leachate is a complex mixture characterized by high toxicity and able to contaminate soils and waters surrounding the dumpsite, especially in developing countries where engineered landfills are still rare. Leachate pollution can severely damage natural ecosystems and harm human health. Traditionally, the hazard assessment of leachate is based on physicochemical characterization but the toxicity is not considered. In the last few decades, different bioassays have been used to assess the toxicity of this complex matrix, including human-related in vitro models. This article reviews the cell bioassays successfully used for the risk assessment of leachate and to evaluate the efficiency of toxicity removal of several processes for detoxification of this wastewater. Articles from 2003 to 2018 are covered, focusing mainly on studies that used human cell lines, highlighting the usefulness and adequacy of in vitro models for assessing the hazard involved with exposure to leachate, particularly as an integrative supporting tool for chemical-based risk assessment. Leachate is generally toxic, mutagenic, genotoxic and estrogenic in vitro, and these effects can be measured in the cells exposed to already low concentrations, confirming the serious hazard of this wastewater for human health. Keywords: Landfill leachate, In vitro models, Estrogenicity, Genotoxicity, Human cell line

ERICA: A multiparametric toxicological risk index for the assessment of environmental healthiness

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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 (H-1 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

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

International audienceSeveral 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

Skin sensitization quantitative QSAR models based on mechanistic structural alerts.

peer reviewedAllergic 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

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

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