Integration of Toxicity Data from Experiments and Non-Testing Methods within a Weight of Evidence Procedure

Assessment of human health and environmental risk is based on multiple sources of information, requiring the integration of the lines of evidence in order to reach a conclusion. There is an increasing need for data to fill the gaps and new methods for the data integration. From a regulatory point of view, risk assessors take advantage of all the available data by means of weight of evidence (WOE) and expert judgement approaches to develop conclusions about the risk posed by chemicals and also nanoparticles. The integration of the physico-chemical properties and toxicological effects shed light on relationships between the molecular properties and biological effects, leading us to non-testing methods. (Quantitative) structure-activity relationship ((Q)SAR) and read-across are examples of non-testing methods. In this dissertation, (i) two new structure-based carcinogenicity models, (ii) ToxDelta, a new read-across model for mutagenicity endpoint and (iii) a genotoxicity model for the metal oxide nanoparticles are introduced. Within the latter section, best professional judgement method is employed for the selection of reliable data from scientific publications to develop a data base of nanomaterials with their genotoxicity effect. We developed a decision tree model for the classification of these nanomaterials. The (Q)SAR models used in qualitative WOE approaches mainly lack transparency resulting in risk estimates needing quantified uncertainties. Our two structure-based carcinogenicity models, provide transparent reasoning in their predictions. Additionally, ToxDelta provides better supported techniques in read-across terms based on the analysis of the differences of the molecules structures. We propose a basic qualitative WOE framework that couples the in silico models predictions with the inspections of the similar compounds. We demonstrate the application of this framework to two realistic case studies, and discuss how to deal with different and sometimes conflicting data obtained from various in silico models in qualitative WOE terms to facilitate structured and transparent development of answers to scientific questions

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals

Classification nano-SAR modeling of metal oxides nanoparticles genotoxicity based on comet assay data

In nearly a decade of vigorous attempt in the toxicology and exposure research carried out to provide evidence for the assessment of health and environmental risks of nanomaterials (NMs), some progress has been made in generating the health effects and exposure data needed to perform risk assessment and develop risk management guidance. Quantitative Structure Activity Relationship ((Q)SAR) models are a powerful tool for rapid screening of large numbers and types of materials with advantage of saving time, funds and animal suffering. In this work we present first (Q)SAR models developed to predict genotoxicity of metal oxide NMs by using large initial sets of nano descriptors. We used a dataset containing in vitro comet assay genotoxicity for 16 nano metal oxides with different chemical core composition. This multi-source data was retrieved from genotoxicity profiles collected in our previous work. To properly analyse the data, we used a weight of evidence approach for evaluation of quality of the comet in vitro data for (Q)SAR modelling. Subsequently, based on the quality of checked dataset, we assigned genotoxic or non-genotoxic property to each metal core composition. By employing orthogonal partial least squares–discriminant analysis (OPLS-DA) method, nano-(Q)SAR models were derived with significant predictive power: accuracy 0.83 and 1. Conventional molecular descriptors and quantum chemical descriptors together with descriptors based on metal-ligand binding properties have been analysed to discuss the key factors affecting genotoxicity of metal oxide NMs. All derived models involve descriptors that describe possible structural factors influencing genotoxic behaviour of metal oxide NMs

Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties

<p>The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials’ data is resulting from <i>in vitro</i> comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure–activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.</p

Different KNIME workflows for read-across and successive use for weight-of-evidence strategy

The evaluation of the toxic effects of substances is a complex task, due to the huge amount of factors involved in the biological processes at the basis of the effect. This requires taking advantage of all elements that can be used in the assessment of the property values. The read-across approach and the in silico methods, collectively called non-testing methods, can be integrated within a weight-ofevidence strategy. This integration is typically performed manually. Furthermore, also the read-across process in most of the cases relies on expert decisions, which may be subjective, and based on some initial choices. In this approach, there is a risk of making poorly reproducible results besides losing important pieces of information. In addition, a main shortcoming in read-across is that the process may not identify some of the relevant source compounds. In order to cope with these problems, we explored software tools able to assist the expert. The factors related to similarity which we used to select source compounds were: structural, physico-chemical, toxicological and pharmacokinetic features. These tools analyse the similarities of the compounds in “full or partial” way, i.e. merging all the features or selecting only those more relevant. Furthermore, the steps of the process can be done in a parallel or sequential way. Finally, we combined the results of the read-across procedure with those from in silico models. We will describe the added value of these programs, implemented in KNIME. We acknowledge the project EU-ToxRisk (a project funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 681002)