QSAR models for the (eco-)toxicological characterization and prioritization of emerging pollutants: case studies and potential applications within REACH.

Under the European REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemical substances - (EC) No 1907/2006), there is an urgent need to acquire a large amount of information necessary to assess and manage the potential risk of thousands of industrial chemicals. Meanwhile, REACH aims at reducing animal testing by promoting the intelligent and integrated use of alternative methods, such as in vitro testing and in silico techniques. Among these methods, models based on quantitative structure-activity relationships (QSAR) are useful tools to fill data gaps and to support the hazard and risk assessment of chemicals. The present thesis was performed in the context of the CADASTER Project (CAse studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment), which aims to integrate in-silico models (e.g. QSARs) in risk assessment procedures, by showing how to increase the use of non-testing information for regulatory decision-making under REACH. The aim of this thesis was the development of QSAR/QSPR models for the characterization of the (eco-)toxicological profile and environmental behaviour of chemical substances of emerging concern. The attention was focused on four classes of compounds studied within the CADASTER project, i.e. brominated flame retardants (BFRs), fragrances, prefluorinated compounds (PFCs) and (benzo)-triazoles (B-TAZs), for which limited amount of experimental data is currently available, especially for the basic endpoints required in regulation for the hazard and risk assessment. Through several case-studies, the present thesis showed how QSAR models can be applied for the optimization of experimental testing as well as to provide useful information for the safety assessment of chemicals and support decision-making. In the first case-study, simple multiple linear regression (MLR) and classification models were developed ad hoc for BFRs and PFCs to predict specific endpoints related to endocrine disrupting (ED) potential (e.g. dioxin-like activity, estrogenic and androgenic receptor binding, interference with thyroxin transport and estradiol metabolism). The analysis of modelling molecular descriptors allowed to highlight some structural features and important structural alerts responsible for increasing specific ED activities. The developed models were applied to screen over 200 BFRs and 33 PFCs without experimental data, and to prioritize the most hazardous chemicals (on the basis of ED potency profile), which have been then suggested to other CADASTER partners in order to focus the experimental testing. In the second case-study, MLR models have been developed, specifically for B-TAZs, for the prediction of three key endpoints required in regulation to assess aquatic toxicity, i.e. acute toxicity in algae (EC50 72h Pseudokirchneriella subcapitata), daphnids (EC50 48h Daphnia magna) and fish (LC50 96h Onchorynchus mykiss). Also in this case, the developed QSARs were applied for screening purposes. Among over 350 B-TAZs lacking experimental data, 20 compounds, which were predicted as toxic (EC(LC)50 64 10 mg/L) or very toxic (EC(LC)50 64 1 mg/L) to the three aquatic species, were prioritized for further experimental testing. Finally, in the third case-study, classification QSPR models were developed for the prediction of ready biodegradability of fragrance materials. Ready biodegradation is among the basic endpoints required for the assessment of environmental persistence of chemicals. When compared with some existing models commonly used for predicting biodegradation, the here proposed QSPRs showed higher classification accuracy toward fragrance materials. This comparison highlighted the importance of using local models when dealing with specific classes of chemicals. All the proposed QSARs have been developed on the basis of the OECD principles for QSAR acceptability for regulatory purposes, paying particular attention to the external validation procedure and to the statistical definition of the applicability domain of the models. QSAR models based on molecular descriptors generated by both commercial (DRAGON) and freely-available (PaDELDescriptor, QSPR-Thesaurus) software have been proposed. The use of free tool allows for a wider applicability of the here proposed QSAR models. Concluding, the QSAR models developed within this thesis are useful tools to support hazard and risk assessment of specific classes of emerging pollutants, and show how non-testing information can be used for regulatory decisions, thus minimizing costs, time and saving animal lives. Beyond their use for regulatory purposes, the here proposed QSARs can find application in the rational design of new safer compounds that are potentially less hazardous for human health and environment

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values\ua0in silico\ua0from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance

Investigation of Critical Body Residues and Modes of Toxic Action Based on Injection and Aquatic Exposure in Fish

The internal concentration represented by the critical body residue (CBR) is an ideal indicator to reflect the intrinsic toxicity of a chemical. Whilst some studies have been performed on CBR, the effect of exposure route on internal toxicity has not been investigated for fish. In this paper, acute toxicity data to fish comprising LC50 and LD50 values were used to investigate CBR. The results showed that exposure route can significantly affect the internal concentration. LD50 and CBR calculated from LC50 and BCF both vary independently of hydrophobicity as expressed by log Kow; conversely, LC50 is related to log Kow. A poor relationship was observed between LC50 and LD50, but the relationship can be improved significantly by introduction of log Kow because log CBR is positively related to log LD50. The parallel relationship of log CBR-log Kow and log LD50-log Kow indicates that LD50 does not reflect the actual internal concentration. The average LD50 is close to the average CBR for less inert and reactive compounds, but greater than the average CBR for baseline compounds. This difference is due to the lipid fraction being the major storage site for most of the baseline compounds. Investigation on the calculated and observed CBRs shows that calculated CBRs are close to observed CBRs for most of compounds. However, systemic deviations of calculated CBRs have been observed for some compounds. The reasons for these systemic deviations may be attributed to BCF, equilibrium time and experimental error of LC50. These factors are important and should be considered in the calculation of CBRs

Review of Data Sources, QSARs and Integrated Testing Strategies for Aquatic Toxicity

This review collects information on sources of aquatic toxicity data and computational tools for estimation of chemical toxicity aquatic to aquatic organisms, such as expert systems and quantitative structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models and for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), which entered into force on 1 June 2007. Effects on organisms from three trophic levels (fish, Daphnia and algae) were subject of this review. In addition to traditional data sources such as databases, papers publishing experimental data are also identified. Models for narcoses, general (global) models as well as models for specific chemical classes and mechanisms of action are summarised. Where possible, models were included in a form allowing reproduction without consultation with the original paper. This review builds on work carried out in the framework of the REACH Implementation Projects, and was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Toxicology and chemical substance

A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Exposure to chemicals generally occurs in the form of mixtures. However, the great majority of the toxicity data, upon which chemical safety decisions are based, relate only to single compounds. It is currently unfeasible to test a fully representative proportion of mixtures for potential harmful effects and, as such, in silico modelling provides a practical solution to inform safety assessment. Traditional methodologies for deriving estimations of mixture effects, exemplified by principles such as concentration addition (CA) and independent action (IA), are limited as regards the scope of chemical combinations to which they can reliably be applied. Development of appropriate quantitative structure-activity relationships (QSARs) has been put forward as a solution to the shortcomings present within these techniques – allowing for the potential formulation of versatile predictive tools capable of capturing the activities of a full contingent of possible mixtures. This review addresses the current state-of-the-art as regards application of QSAR towards mixture toxicity, discussing the challenges inherent in the task, whilst considering the strengths and limitations of existing approaches. Forty studies are examined within – through reference to several characteristic elements including the nature of the chemicals and endpoints modelled, the form of descriptors adopted, and the principles behind the statistical techniques employed. Recommendations are in turn provided for practices which may assist in further advancing the field, most notably with regards to ensuring confidence in the acquired predictions.publishedVersio

Data quality in the human and environmental health sciences: Using statistical confidence scoring to improve QSAR/QSPR modeling

A greater number of toxicity data are becoming publicly available allowing for in silico modeling. However, questions often arise as how to incorporate data quality and how to deal with contradicting data if more than a single datum point is available for the same compound. In this study, two well-known and studied QSAR/QSPR models for skin permeability and aquatic toxicology have been investigated in the context of statistical data quality. In particular, the potential benefits of the incorporation of the statistical Confidence Scoring (CS) approach within modelling and validation. As a result, robust QSAR/QSPR models for the skin permeability coefficient and the toxicity of nonpolar narcotics to Aliivibrio fischeri assay were created. CSweighted linear regression for training and CS-weighted root mean square error (RMSE) for validation were statistically superior compared to standard linear regression and standard RMSE. Strategies are proposed as to how to interpret data with high and low CS, as well as how to deal with large datasets containing multiple entries

Environmental, health, and safety assessment of chemical alternatives during early process design: The role of predictive modeling and streamlined techniques

Industrial chemicals are important for many aspects of modern life, though they can be harmful to the environment and human health. Environmental or safety concerns identified during the early design and selection of chemicals could motivate choices as to safer alternatives and process setups. There is a growing interest in developing more rapid, and streamlined assessment methods to obtain a first indication of the potential impacts linked to the nature and use of industrial chemicals. This work applies predictive modeling and streamlined techniques to estimate the potential environmental, health, and safety hazards associated with specific chemical structures. The assessment is performed during the design and selection of promising candidates for a particular process as part of the computer-aided molecular design (CAMD) and process setup. The case of phase-change solvents used for post-combustion carbon capture is examined. Furthermore, the refinement of predictive models through the incorporation of knowledge already existing in the field (prior knowledge) is investigated. A procedure for knowledge extraction from scientific articles that applies text mining is proposed. The results show that incorporating impact assessment criteria into the CAMD facilitates the molecular design by enriching the Pareto front of candidates. The use of predictive models that estimate molecular properties, such as acute aquatic toxicity, bioconcentration, and persistency are found to support the identification of the optimal solvents for CO2 capture. Given the role of sustainability-related properties in tasks such as CAMD, the improved performance and the interpretability of the aquatic toxicity predictive models developed here and using prior knowledge are important. The process level assessment of the phase-change solvent systems indicated that phase-change solvent alternatives could provide benefits, not only in terms of reduced energy consumption but also lower impacts on human health and the environment. \ua0However, the degradation behaviors of these compounds should be properly assessed and controlled to ensure beneficial performances compared to conventional carbon capture solvents. Overall, predictive modeling and streamlined life-cycle assessments (LCAs), as well as environmental, health, and safety evaluation methods were revealed to be valuable for defining the critical aspects that influence the potential impacts of chemicals and in supporting decisions concerning the molecular and process designs

In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC50) in marine crustaceans using a set of calculated 2D molecular descriptors. The models were built following the guidelines recommended by the OECD (Organization for Economic Cooperation and Development) and validated through stringent processes (internal and external validation). Six machine learning (ML) models were built and compared (linear regression: LR; support vector machine: SVM; random forest: RF; feed-forward backpropagation-based artificial neural network: ANN; decision trees: DT and naïve Bayes: NB) for regression and classification analysis to predict toxicities. All the models displayed encouraging results with high generalisability: the feed-forward-based backpropagation method showed the best results with determination coefficient R2 values of 0.82 and 0.94, respectively, for training set (TS) and validation set (VS). For classification-based modelling, the DT model performed the best with an accuracy (ACC) of 100 % and an area under curve (AUC) value of 1 for both TS and VS. These models showed the potential to replace animal testing for the chemical hazard assessment of untested biocides if they fall within the applicability domain of the proposed models. In general, the models are highly interpretable and robust, with good predictive performance. The models also displayed a trend indicating that toxicity is largely influenced by factors such as lipophilicity, branching, non-polar bonding and saturation of molecules