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

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

Abstract In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results

Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data

Background: The potential for a compound to cause hepatotoxicity and nephrotoxicity is a matter of extreme interest for human health risk assessment. To assess liver and kidney toxicity, repeated-dose toxicity (RDT) studies are conducted mainly on rodents. However, these tests are expensive, time-consuming and require large numbers of animals. For early toxicity screening, in silico models can be applied, reducing the costs, time and animals used. Among in silico approaches, structure-activity relationship (SAR) methods, based on the identification of chemical substructures (structural alerts, SAs) related to a particular activity (toxicity), are widely employed. Results: We identified and evaluated some SAs related to liver and kidney toxicity, using RDT data on rats taken from the hazard evaluation support system (HESS) database. We considered only SAs that gave the best percentages of true positives (TP). Conclusions: It was not possible to assign an unambiguous mode of action for all the SAs, but a mechanistic explanation is provided for some of them. Such achievements may help in the early identification of liver and renal toxicity of substances

Predicting toxicity through computers: a changing world

The computational approaches used to predict toxicity are evolving rapidly, a process hastened on by the emergence of new ways of describing chemical information. Although this trend offers many opportunities, new regulations, such as the European Community's 'Registration, Evaluation, Authorisation and Restriction of Chemicals' (REACH), demand that models be ever more robust

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

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QSAR Model for Cytotoxicity of Silica Nanoparticles on Human Embryonic Kidney Cells1

Abstract A predictive model for cytotoxicity of 20 and 50 nm silica nanoparticles has been built using so-called optimal descriptors as mathematical functions of size, concentration and exposure time. These parameters have been encoded into 31 combinations 'concentration-exposure-size'. The calculation has been carried out by means of the CORAL software ( http://www.insilico.eu/coral/ ) using three random splits of the obtained systems into training and test sets. The statistical quality of the best model for cell viability (%) of cultured human embryonic kidney cells (HEK293) exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory

A generalizable definition of chemical similarity for read-across

Background: Methods that provide a measure of chemical similarity are strongly relevant in several fields of chemoinformatics as they allow to predict the molecular behavior and fate of structurally close compounds. One common application of chemical similarity measurements, based on the principle that similar molecules have similar properties, is the read-across approach, where an estimation of a specific endpoint for a chemical is provided using experimental data available from highly similar compounds. Results: This paper reports the comparison of multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity in the context of two different applications of the read-across technique. Conclusions: Our analysis demonstrates that the classical similarity measurements can be improved with a generalizable model of similarity. The proposed approach has already been used to build similarity indices in two open-source software tools (CAESAR and VEGA) that make several QSAR models available. In these tools, the similarity index plays a key role for the assessment of the applicability domain.Pubblicat

Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms.

Abstract The purpose of this study was to determine the acute toxicity in aquatic organisms of one biocidal active substance and six metabolites derived from biocidal active substances and to assess the suitability of available QSAR models to predict the obtained values. We have reported the acute toxicity in sewage treatment plant (STP) microorganisms, in the freshwater microalgae Pseudokirchneriella subcapitata and in Daphnia magna following OECD test methods. We have also identified in silico models for acute toxicity of these trophic levels currently available in widely recognized platforms such as VEGA and the OECD QSAR ToolBox. A total of six, four and two models have been selected for Daphnia, algae and microorganisms, respectively. Finally, we have compared the in silico and in vivo data for the seven compounds plus two previously assayed biocidal substances. None of the compounds tested were toxic for Daphnia and STP microorganisms. For microalgae, CGA71019 (1,2,4 triazole) presented an ErC50 value of 38.3 mg/L. The selected in silico models have provided lower EC50 values and are therefore more conservative. Models from the OECD QSAR ToolBox predicted values for 7 out of 9 and for 4 out of 9 chemicals for Daphnia and P. subcapitata, respectively. No predictive models were identified in such platform for STP microorganism's acute effects. In terms of models's specificity, biocide-specific models, developed from curated datasets integrated by biocidal active substances and implemented in VEGA, perform better in the case of microalgae but for Daphnia an alternative, non biocide-specific has revealed a better performance. For STP microorganisms only biocide-specific models have been identified

QSPR modelling of normal boiling points and octanol/water partition coefficient for acyclic and cyclic hydrocarbons using SMILES-based optimal descriptors

AbstractPredictive quantitative structure - property relationships (QSPR) have been established for normal boiling points and octanol/water partition coefficient for acyclic and cyclic hydrocarbons using optimal descriptors calculated with simplified molecular input line entry system (SMILES). The probabilistic criteria for a rational definition of the domain of applicability of these models are discussed