Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity

This review of QSARs for genotoxicity and carcinogenicity was performed in a broad sense, considering both models available in software tools and models that are published in the literature. The review considered the potential applicability of diverse models to pesticides as well as to other types of regulated chemicals and pharmaceuticals. The availability of models and information on their applicability is summarised in tables, and a range of illustrative or informative examples are described in more detail in the text. In many cases, promising models were identified but they are still at the research stage. For routine application in a regulatory setting, further efforts will be needed to explore the applicability of such models for specific purposes, and to implement them in a practically useful form (i.e. user-friendly software). It is also noted that a range of software tools are research tools suitable for model development, and these require more specialised expertise than other tools that are aimed primarily at end-users such as risk assessors. It is concluded that the most useful models are those which are implemented in software tools and associated with transparent documentation on the model development and validation process. However, it is emphasised that the assessment of model predictions requires a reasonable amount of QSAR knowledge, even if it is not necessary to be a QSAR practitioner.JRC.DG.I.6-Systems toxicolog

Integrating computational methods to predict mutagenicity of aromatic azo compounds

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds

The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

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

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts

Identification of \u27structural alerts\u27 and associated mechanisms of action of mammary gland carcinogens in female rodents

A new structure-activity relationship (SAR) approach to modeling was utilized to study mammary gland carcinogens. A set of chemicals tested for mammary tumorigenesis that have been analyzed in the Carcinogenic Potency Database (CPDB) were subjected to several computational analyses in an attempt to predict each chemical’s carcinogenic potential. A total of six learning sets (rat and mouse mammary gland carcinogen, CPDB rat and mouse, and female-specific rodent models) were developed and validated using a SAR modeling algorithm called categorical-SAR (cat-SAR). The predictive cat-SAR program evaluates active and inactive compounds of known biological activity and predicts their biological activity from this categorical data. Overall, this study demonstrates the usefulness of cat-SAR and its successful application in developing ‘structural alerts’ to breast carcinogenicity. The resulting rat and mouse mammary carcinogen models achieved an 82.0% (sensitivity 76.7%; specificity 87.5%) and 80.6% (sensitivity 80%; specificity 81.8%) concordance between experimental and predicted results, respectively. Likewise, the general CPDB mouse and rat models were both 70% predictive. Corresponding sensitivity and specificity values were 74.2 and 66.7% and 70.4 and 68.5%, respectively. The analyses indicate the capability of cat-SAR in identifying molecular fragments that potentially interact with cellular components present only in the targeted cell type (e.g., breast tissue cells). Moreover, this method is expected to help pre-determine structural alerts to carcinogen-induced mammary cancer. Identification of these ‘structural alerts’ can assist in understanding mechanisms involved in making a normal breast cell cancerous. Using the results of these analyses, it is possible to classify and rank structurally diverse chemicals as to their potential to induce mammary gland cancer

Analytical strategies for the screening of microcontaminants and transformation products in aquatic environment

The characterization of anthropogenic contamination and understanding of the associated risks for humans and the environment is a challenge, since tens of thousands of compounds are constantly discharged into different environmental compartments. The hydrosphere has a very powerful potential to disseminate contaminants of emerging concern (CECs), which can then reach other compartments such as soil, plants, and sediments, so evaluation of its contamination is essential. The identification of CECs in aquatic systems is analytically difficult, since there is a need to achieve increasingly low detection limits (µg L-1 and ng L-1) and cover the widest possible range of compounds. Expanding knowledge about aquatic contamination requires the use of sensitive methods that allow unequivocal identification of CECs, which may be achieved by methods using liquid or gas chromatography coupled with high resolution mass spectrometry. In addition, sensitive analytical methods should be associated with in silico prediction by (quantitative) structure-activity relationship ((Q)SAR) tools and multi-criteria decision analysis ranking methods, in order to not only obtain conclusions about contaminants present in the environment, but also to identify those of most concern. Considering these issues, the present thesis is divided into three chapters. Chapter 1 describes an adapted analytical method for the identification of pharmaceuticals and metabolites in raw hospital wastewater, using three different identification strategies: i) for confirmed compounds (when analytical standards are available); ii) for suspect compounds (when analytical standards are not available); and iii) for metabolites by common fragmentation profile. The method employed a custom database containing up to 1380 compounds. Six samples collected monthly were analyzed by liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC- QTOF MS). A total of 35 metabolites and 43 pharmaceuticals were identified. Risk assessment of the identified compounds was performed using in silico (Q)SAR prediction methods. Chapter 2 presents a study of the degradation of diazepam (DZP), a pharmaceutical identified in all the samples analyzed, as described in Chapter 1, by solar photo-Fenton treatment, which is an advanced oxidation process (AOP). The identification of previously reported and new transformation products (TPs) formed during DZP degradation was performed by LC-QTOF MS analysis. In addition, a methodfor the preconcentration of DZP and its TPs was developed, based on dispersive liquid- liquid microextraction (DLLME). The extraction method was fast, cheap, easy, and efficient. In the absence of this preconcentration step, it was not possible to identify one of the TPs formed during the solar photo-Fenton process. In this study, (Q)SAR tools were also used to predict some of the toxicological parameters of DZP and its TPs. These predictions showed mutagenicity alerts for two TPs, reflecting their higher toxicity, compared to DZP itself. Chapter 3 describes a more embracing approach. Surface water analysis was carried out by LC-QTOF MS, with application of a screening methodology using a database containing information about 3250 compounds belonging to different CEC classes. After LC-QTOF MS screening analyses of 27 river samples, it was possible to identify 150 compounds (133 compounds as suspects, and 17 compounds as confirmed). In silico predictions for the identified compounds were performed using (Q)SAR tools, providing information about eight different selected endpoints. The great number of compounds and predicted endpoints hindered the general evaluation of toxicity. Therefore, in order to obtain a better understanding of the risk of each identified compound, two different multi-criteria decision analysis ranking methods (toxicological priority index (ToxPi) and technique for order of preference by similarity to ideal solution (TOPSIS)) were used, considering a different weight for each endpoint. After ranking, the ToxPi and TOPSIS results were evaluated and showed similarity for the first 20 priority compounds. TOPSIS showed high robustness in sensitivity tests, indicating its suitability as an appropriate tool for use in association with screening results, which could support quantitative analytical methods performed subsequently. Throughout the different studies, it was possible to propose strategies for identification, degradation, extraction, toxicity evaluation, and ranking of microcontaminants present in aquatic environments. It was possible to obtain new results never previously reported, highlighting the contribution and importance of the study for research concerning contamination of the aquatic environment and possible treatment methods.A caracterização e compreensão da contaminação antropogênica e dos seus riscos para o homem e o meio ambiente é um desafio, uma vez que dezenas de milhares de compostos são constantemente despejados em diferentes compartimentos ambientais. A hidrosfera tem potencial muito poderoso para disseminar contaminantes de preocupação emergente (CECs), os quais podem atingir outros compartimentos como solo, plantas e sedimentos. Portanto, a avaliação de sua contaminação é essencial. A identificação de CECs em sistemas aquáticos é analiticamente complexa, sendo necessário atingir limites de detecção cada vez mais baixos (μg L-1 e ng L-1) e abranger a maior gama possível de compostos. Tal necessidade requer o uso de métodos sensíveis que permitem a identificação inequívoca de CECs e, para isso, uma possibilidade é o uso da cromatografia líquida ou a gás associada a espectrometria de massa de alta resolução. Além disso, os métodos analíticos podem ser associados a predições in silico por métodos de relações quantitativas entre a estrutura e atividade ((Q)SAR) e métodos de tomada de decisão multicritério, a fim de não apenas obter conclusões sobre os contaminantes presentes no ambiente, mas também para identificar aqueles que merecem maior atenção. Considerando essas questões, a presente tese está dividida em três capítulos. O Capítulo 1 descreve um método analítico para a identificação de fármacos e metabólitos em efluente hospitalar bruto, usando três estratégias de identificação: i) compostos confirmados (quando padrões analíticos estão disponíveis); ii) para compostos suspeitos (quando padrões analíticos não estão disponíveis); e iii) para metabólitos com perfil de fragmentação comum. O método empregou uma base de dados personalizada contendo 1380 compostos. Seis amostras coletadas mensalmente foram analisadas por cromatografia líquida acoplada à espectrometria de massa por tempo de vôo (LC-QTOF MS). Um total de 35 metabólitos e 43 fármacos foram identificados. A avaliação de risco dos compostos identificados foi realizada usando métodos de predição in silico (Q)SAR. O Capítulo 2 apresenta um estudo da degradação do diazepam (DZP), fármaco identificado em todas as amostras analisadas no Capítulo 1, através do processo de foto- Fenton solar, que é um processo avançado de oxidação (AOP). A identificação de produtos de transformação (TPs) formados durante a degradação do DZP foi realizada pela análise em um sistema LC-QTOF MS. Além disso, um método para a pré-concentração de DZP e seus TPs foi desenvolvido, baseado em microextração líquido- líquido dispersiva (DLLME). O método de extração proposto é rápido, barato, fácil e eficiente. Na ausência desta etapa de pré-concentração, não foi possível identificar um dos TPs formados durante o processo de foto-Fenton solar. Neste estudo, métodos (Q)SAR também foram usados para predizer alguns dos parâmetros toxicológicos do DZP e seus TPs. Essas predições mostraram alertas de mutagenicidade para dois TPs, refletindo sua maior toxicidade, em comparação com o próprio DZP. O Capítulo 3 descreve uma abordagem mais abrangente. Análise de águas superficiais, realizada por LC-QTOF MS, com aplicação de uma metodologia de screening utilizando bases de dados contendo informações sobre 3250 compostos pertencentes a diferentes classes de CEC. Após análise screening de 27 amostras de rios, foi possível identificar 150 compostos (133 compostos suspeitos e 17 compostos confirmados). As predições in silico dos compostos identificados foram realizadas usando métodos (Q)SAR, para oito variáveis selecionadas. O grande número de compostos e as diferentes variáreis preditas dificultaram a avaliação geral da toxicidade. Portanto, a fim de obter uma melhor compreensão do risco de cada composto identificado, foram utilizados dois métodos de tomada de decisão multicritério (toxicological priority index (ToxPi) e technique for order of preference by similarity to ideal solution (TOPSIS)), considerando diferentes pesos para cada uma das variáveis. Após a classificação, os resultados de ToxPi e TOPSIS foram avaliados e mostraram similaridade para os 20 compostos mais preocupantes. O TOPSIS mostrou alta robustez em testes de sensibilidade, indicando ser uma ferramenta apropriada para uso em associação com resultados de análise screening, o que pode apoiar e direcionar o desenvolvimento de métodos analíticos quantitativos como segunda etapa. Ao longo dos diferentes estudos, foi possível propor estratégias de identificação, degradação, extração, avaliação de toxicidade e classificação de microcontaminantes presentes em ambientes aquáticos. Foi possível obter novos resultados nunca antes reportados, evidenciando a contribuição e importância do estudo para a pesquisa sobre contaminação do meio aquático e possíveis métodos de tratamento

Alternative methods for regulatory toxicology – a state-of-the-art review

This state-of-the art review is based on the final report of a project carried out by the European Commission’s Joint Research Centre (JRC) for the European Chemicals Agency (ECHA). The aim of the project was to review the state of the science of non-standard methods that are available for assessing the toxicological and ecotoxicological properties of chemicals. Non-standard methods refer to alternatives to animal experiments, such as in vitro tests and computational models, as well as animal methods that are not covered by current regulatory guidelines. This report therefore reviews the current scientific status of non-standard methods for a range of human health and ecotoxicological endpoints, and provides a commentary on the mechanistic basis and regulatory applicability of these methods. For completeness, and to provide context, currently accepted (standard) methods are also summarised. In particular, the following human health endpoints are covered: a) skin irritation and corrosion; b) serious eye damage and eye irritation; c) skin sensitisation; d) acute systemic toxicity; e) repeat dose toxicity; f) genotoxicity and mutagenicity; g) carcinogenicity; h) reproductive toxicity (including effects on development and fertility); i) endocrine disruption relevant to human health; and j) toxicokinetics. In relation to ecotoxicological endpoints, the report focuses on non-standard methods for acute and chronic fish toxicity. While specific reference is made to the information needs of REACH, the Biocidal Products Regulation and the Classification, Labelling and Packaging Regulation, this review is also expected to be informative in relation to the possible use of alternative and non-standard methods in other sectors, such as cosmetics and plant protection products.JRC.I.5-Systems Toxicolog

Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity

This evaluation of the non-commercial (Q)SARs for mutagenicity and carcinogenicity consisted of a preliminary survey (Phase I), and then of a more detailed analysis of short listed models (Phase II). In Phase I, the models were collected from the literature, and then assessed according to the OECD principles based on the information provided by the authors-. Phase I provided the support for short listing a number of promising models, that were analyzed more in depth in Phase II. In Phase II, the information provided by the authors was completed and complemented with a series of analyses aimed at generating an overall profile of each of the short listed models. The models can be divided into two families based on their target: a) congeneric; and b) non-congeneric sets of chemicals. The QSARs for congeneric chemicals include most of the chemical classes top ranking in the EU High Production Volume list, with the notable exception of the halogenated aliphatics. They almost exclusively aim at modeling Salmonella mutagenicity and rodent carcinogenicity, which are crucial toxicological endpoints in the regulatory context. The lack of models for in vivo genotoxicity should be remarked. Overall the short listed models can be interpreted mechanistically, and agree with, and/or support the available scientific knowledge, and most of the models have good statistics. Based on external prediction tests, the QSARs for the potency of congeneric chemicals are 30 to 70 % correct, whereas the models for discriminating between active and inactive chemicals have considerably higher accuracy (63 to 100 %), thus indicating that predicting intervals is more reliable than predicting individual data points. The internal validation procedures (e.g., cross-validation, etc...) did not seem to be a reliable measure of external predictivity. Among the non-local, or global approaches for non-congeneric data sets, four models based on the use of Structural Alerts (SA) were short listed and investigated in more depth. The four sets did not differ to a large extent in their performance. In the general databases of chemicals the SAs appear to agree around 65% with rodent carcinogenicity data, and 75% with Salmonella mutagenicity data. The SAs based models do not seem to work equally efficiently in the discrimination between active and inactive chemicals within individual chemical classes. Thus, their main role is that of preliminary, or large-scale screenings. A priority for future research on the SAs is their expansion to include alerts for nongenotoxic carcinogens. A general indication of this study, valid for both congeneric and noncongeneric models, is that there is uncertainty associated with (Q)SARs; the level of uncertainty has to be considered when using (Q)SAR in a regulatory context. However, (Q)SARs are not meant to be black-box machines for predictions, but have a much larger scope including organization and rationalization of data, contribution to highlight mechanisms of action, complementation of other data from different sources (e.g., experiments). Using only non-testing methods, the larger the evidence from QSARs (several different models, if available) and other approaches (e.g. chemical categories, read across) the higher the confidence in the prediction.JRC.I.3-Toxicology and chemical substance