In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided.

Genotoxicity assessment of chemicals has a crucial role in most regulations. Due to labor, time, cost, and animal welfare issues, attention is being given to (Q)SAR methods. A strategic application of alternative methods is to first use a sequence of conservative (very sensitive) (Q)SARs and/or in vitro models to arrive at the conclusion that no further testing is necessary for negatives, and to use mechanistically based, Weight-Of-Evidence approach to evaluate the chemicals showing positive results. The ICH M7 guideline to detect DNA-reactive impurities in drugs follows these lines (recommending solely (Q)SAR in step 1). However, ICH M7 focuses only on Ames test. Here a large database of more than 6000 chemicals positive in at least one endpoint (in vitro gene mutations or chromosomal aberrations, in vivo micronucleus, aneugenicity) were analyzed with structural alerts implemented in the OECD QSAR Toolbox, resulting in maximum 3% false negatives. These promising results indicate that it may be possible to extend the approach to the whole range of genotoxicity endpoints required by regulations. Since structural alerts may generate false positives, cautious follow-up of positives is recommended (with e.g., statistically based QSARs, read across of similar chemicals, expert judgement, and experimentation when necessary).

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project.

To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including literature survey, application of QSARs and development of Read Across methodologies. This paper summarizes the main results. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test. The reliability of the models for other assays/endpoints appears to be still far from optimality. Two new Read Across approaches were evaluated: Read Across was largely successful for predicting the Ames test results, but less for in vitro Chromosomal Aberrations. The worse results for non-Ames endpoints may be attributable to the several revisions of experimental protocols and evaluation criteria of results, that have made the databases qualitatively non-homogeneous and poorly suitable for modeling. Last, Parent/Metabolite structural differences (besides known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. The findings from this work are suitable for being integrated into Weight-of-Evidence and Tiered evaluation schemes. Areas needing further developments are pointed out.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

In silico approaches to genetic toxicology: progress and future.

Computational toxicology, also called 'in silico toxicology', is based on scientific knowledge gained from different scientific fields and on the premise that the toxicity of a chemical, depending on its intrinsic nature, can be predicted from its molecular structure and inferred from the properties of similar compounds whose activities are known. With the aim of providing faster, more economical, animal-free tools for predicting toxicity, the 'old' and well established science of Structure-Activity Relationships plays a crucial role, with increasing applications to the assessment of chemical genotoxicity and carcinogenicity. The development of the Structure-Activity Relationships algorithms is a continuous process, and new models, as well as newer versions of applications, are continuously becoming available. This Mutagenesis Special Issue presents a collection of papers on the recent advances in the field, and provides a precious snapshot in time with the most updated information available today.

Data-based review of QSARs for predicting genotoxicity: the state of the art.

With the aim of providing faster, more economical, animal-free tools to predict toxicity, quantitative structure-activity relationships (QSAR) approaches are increasingly applied to the chemical risk assessment-in particular genotoxicity and carcinogenicity. The more recent period of time has witnessed refinements of the predictive systems, with the collection of larger training sets and continued fine-tuning, together with an increased interest for the use of QSAR by regulatory authorities. This literature review provides an updated snapshot of the present state of the art in the evaluation of QSAR methods as applied to genotoxicity. Overall, the abilities of software tools to predict Ames test mutagenicity were comparable with previously published evaluations, with sensitivity ranging 0.72-0.96, and specificity ranging 0.65-0.86 in applications to public data sets. These values compare quite fairly with the intrinsic variability of the Ames test. A preliminary analysis indicated a consistency with the results of the Japan Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) Ames/QSAR international collaborative project. Applications to a variety of external test sets pointed to the need of further improvements of the coverage/representation of the whole chemical space. Combinations of tools showed that sensitivity is usually increased at the expense of a decrease in specificity, whereas the supervision by expert judgement generated more equilibrated results. This points to the existence of a large area of context-dependent expert knowledge, which has not been formalised yet and has the potential to substantially improve the prediction systems. The overall evidence suggests that (Q)SARs for the Ames test have sufficient reliability for use in prioritisation processes as well as to support regulatory decisions in combination with other evidence. The use of highly sensitive genotoxicity QSARs in tiered integrations with other tools is suggested as a mean to shortlist chemicals for which no further testing is necessary.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Towards quantitative read across: Prediction of Ames mutagenicity in a large database.

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance, thus stimulating an increased interest for the use of Structure-Activity based approaches by regulatory authorities, particularly QSAR and Read Across. Whereas the longer history of QSAR led to recognize the crucial requirements for predictivity, there are still challenges faced by adopting Read Across to a larger extent in a regulatory setting, namely standardization and objective criteria. In previous research, suitable conditions for applying Read Across to the prediction of the Ames mutagenicity of metabolites and degradation products of pesticides were established: a standardized similarity criterion based simultaneously on basic molecular properties and Structural Similarity was successfully applied to a number of case studies. Here the investigation is extended to a large database of curated Ames mutagenicity results. For around 2,000 chemicals for which the similarity criterion was applicable, the predictivity of Read Across was high: specificity 0.72, sensitivity 0.90, accuracy 0.85. This compares favourably with the Ames test intra-assay variability, and with the predictivity of QSAR models. The need for standardization and rigorous validation of Read Across is emphasized.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

In silico toxicology protocols.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Endocrine Disruptors: Data-based survey of in vivo tests, predictive models and the Adverse Outcome Pathway.

The protection from endocrine disruptors is a high regulatory priority. Key issues are the characterization of in vivo assays, and the identification of reference chemicals to validate alternative methods. In this exploration, publicly available databases for in vivo assays for endocrine disruption were collected and compared: Rodent Uterotrophic, Rodent Repeated Dose 28-day Oral Toxicity, 21-Day Fish, and Daphnia magna reproduction assays. Only the Uterotrophic and 21-Day Fish assays results correlated with each other. The in vivo assays data were viewed in relation to the Adverse Outcome Pathway, using as a probe 18 ToxCast in vitro assays for the ER pathway. These are the same data at the basis of the EPA agonist ToxERscore model, whose good predictivity was confirmed. The multivariate comparison of the in vitro/in vivo assays suggests that the interaction with receptors is a major determinant of in vivo results, and is the critical basis for building predictive computational models. In agreement with the above, this work also shows that it is possible to build predictive models for the Uterotrophic and 21-Day Fish assays using a limited selection of Toxcast assays.

A data-based exploration of the adverse outcome pathway for skin sensitization points to the necessary requirements for its prediction with alternative methods.

This paper presents new data-based analyses on the ability of alternative methods to predict the skin sensitization potential of chemicals. It appears that skin sensitization, as shown in humans and rodents, can be predicted with good accuracy both with in vitro assays and QSAR approaches. The accuracy is about the same: 85-90%. Given that every biological measure has inherent uncertainty, this performance is quite remarkable. Overall, there is a good correlation between human data and experimental in vivo systems, except for sensitizers of intermediate potency. This uncertainty/variability is probably the reason why alternative methods are quite efficient in predicting both strong and non-sensitizers, but not the intermediate potency sensitizers. A detailed analysis of the predictivity of the individual approaches shows that the biological in vitro assays have limited added value in respect to the in chemico/QSAR ones, and suggests that the primary interaction with proteins is the rate-limiting step of the entire process. This confirms evidence from other fields (e.g., carcinogenicity, QSAR) indicating that successful predictive models are based on the parameterization of a few mechanistic features/events, whereas the consideration of all events supposedly involved in a toxicity pathway contributes to increase the uncertainty of the predictions.

Alternative Toxicity Testing: Analyses on Skin Sensitization, ToxCast Phases I and II, and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways.

This article studies alternative toxicological approaches, with new (skin sensitization, ToxCast) and previous (carcinogenicity) analyses. Quantitative modeling of rate-limiting steps in skin sensitization and carcinogenicity predicts the majority of toxicants. Similarly, successful (Quantitative) Structure-Activity Relationships models exploit the quantification of only one, or few rate-limiting steps. High-throughput assays within ToxCast point to promising associations with endocrine disruption, whereas markers for pathways intermediate events have limited correlation with most endpoints. Since the pathways may be very different (often not simple linear chains of events), quantitative analysis is necessary to identify the type of mechanism and build the appropriate model.

Establishing the level of safety concern for chemicals in food without the need for toxicity testing.

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.

Flavouring Group Evaluation 413 (FGE.413): Naringenin.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of naringenin [FL-no: 16.132] as a new flavouring substance, in accordance with Regulation (EC) No 1331/2008. No other substances with sufficient structural similarity have been identified in existing FGEs that could be used to support a read-across approach. The information provided on the manufacturing process, the composition and the stability of [FL-no: 16.132] was considered sufficient. From studies carried out with naringenin, the Panel concluded that there is no concern with respect to genotoxicity. The use of naringenin as a flavouring substance at added portions exposure technique (APET) exposure levels is unlikely to pose a risk for drug interaction. For the toxicological evaluation of naringenin, the Panel requested an extended one-generation toxicity study on naringenin, in line with the requirements of the Procedure and to investigate the consequence of a possible endocrine-disrupting activity. The Panel considered that changes in thymus weight, litter size, post-implantation loss and a consistent reduced pup weight in the high-dose F2 generation could not be dismissed and selected therefore, the mid-dose of 1320 mg/kg body weight (bw) per day for the parental males as the no observed adverse effect level (NOAEL) of the study. The exposure estimates for [FL-no: 16.132] (31,500 and 50,000 µg/person per day for children and adults, respectively) were above the threshold of toxicological of concern (TTC) for its structural class (III). Using the NOAEL of 1320 mg/kg bw per day at step A4 of the procedure, margins of exposure (MoE) of 1590 and 630 could be calculated for adults and children, respectively. Based on the calculated MoEs, the Panel concluded that the use of naringenin as a flavouring substance does not raise a safety concern.

Flavouring group evaluation 419 (FGE.419): 2-methyl-1-(2-(5-(p-tolyl)-1H-imidazol-2-yl)piperidin-1-yl)butan-1-one.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of 2-methyl-1-(2-(5-(p-tolyl)-1H-imidazol-2-yl)piperidin-1-yl)butan-1-one [FL-no: 16.134] as a new flavouring substance, in accordance with Regulation (EC) No 1331/2008. The substance has not been reported to occur naturally and is chemically synthesised. In food, it is intended to be used as a flavouring substance only in chewing gum. The chronic dietary exposure to [FL-no: 16.134] was estimated to be 45 µg/person per day for a 60-kg adult and 28.4 µg/person per day for a 15-kg 3-year-old child. [FL-no: 16.134] did not show genotoxicity in a bacterial reverse mutation test and an in vitro mammalian cell micronucleus assay. Based on the submitted toxicokinetic and metabolism data, it can be predicted that the flavouring substance is metabolised to innocuous products only. The Panel derived a lower confidence limit of the benchmark dose (BMDL) of 0.71 mg/kg bw per day for a 20% increase in the relative thyroid (including parathyroid) weight observed in a 90-day toxicity study in rats. Based on this BMDL, adequate margins of exposure of 887 and 374 could be calculated for adults and children, respectively. The Panel concluded that there is no safety concern for [FL-no: 16.134], when used as a flavouring substance at the estimated level of dietary exposure, based on the intended use and use levels as specified in Appendix B. The Panel further concluded that the combined exposure to [FL-no: 16.134] from its use as a food flavouring substance and from its presence in toothpaste and mouthwash is also not of safety concern.

In vitro cell transformation assays for an integrated, alternative assessment of carcinogenicity: a data-based analysis.

The study of the chemical carcinogenesis mechanisms and the design of efficient prevention strategies and measures are of crucial importance to protect human health. The long-term carcinogenesis bioassays have played a central role in protecting human health, but for ethical and practical reasons their use is dramatically diminishing, and the genotoxicity short-term tests have taken the pivotal role in the pre-screening of carcinogenicity. However, there is evidence that this strategy is not sensitive enough to detect all genotoxic carcinogens and it cannot detect nongenotoxic carcinogens. In a previous article, we have shown that an integrated strategy consisting of the in vitro Ames and Syrian Hamster Embryo cells transformation assays, combined with structure-activity relationships, is a valid alternative to the present pre-screening strategies. Here, we expand the previous investigation by (i) including results of cell transformation assays on inorganics, together with an additional assay (Bhas 42), and (ii) considering new structural alerts for nongenotoxic carcinogenicity. We also present a new analysis on global relationships between toxicological endpoints. The new results confirm that the previously proposed integrated, alternative strategy is an efficient tool to identify both genotoxic and nongenotoxic carcinogens, with an estimated 90-95% sensitivity.

New perspectives in toxicological information management, and the role of ISSTOX databases in assessing chemical mutagenicity and carcinogenicity.

Currently, the public has access to a variety of databases containing mutagenicity and carcinogenicity data. These resources are crucial for the toxicologists and regulators involved in the risk assessment of chemicals, which necessitates access to all the relevant literature, and the capability to search across toxicity databases using both biological and chemical criteria. Towards the larger goal of screening chemicals for a wide range of toxicity end points of potential interest, publicly available resources across a large spectrum of biological and chemical data space must be effectively harnessed with current and evolving information technologies (i.e. systematised, integrated and mined), if long-term screening and prediction objectives are to be achieved. A key to rapid progress in the field of chemical toxicity databases is that of combining information technology with the chemical structure as identifier of the molecules. This permits an enormous range of operations (e.g. retrieving chemicals or chemical classes, describing the content of databases, finding similar chemicals, crossing biological and chemical interrogations, etc.) that other more classical databases cannot allow. This article describes the progress in the technology of toxicity databases, including the concepts of Chemical Relational Database and Toxicological Standardized Controlled Vocabularies (Ontology). Then it describes the ISSTOX cluster of toxicological databases at the Istituto Superiore di Sanitá. It consists of freely available databases characterised by the use of modern information technologies and by curation of the quality of the biological data. Finally, this article provides examples of analyses and results made possible by ISSTOX.

Evaluation of the toxicity forecasting capability of EPA's ToxCast Phase I data: can ToxCast in vitro assays predict carcinogenicity?

Long-term rodent bioassays have played a central role in protecting human health from carcinogens; for ethical and practical reasons their use is decreasing whereas genotoxicity testing has taken a pivotal role. However, this strategy--as presently implemented--is not sensitive enough to detect all genotoxic carcinogens, and cannot detect nongenotoxic carcinogens. Among the alternative approaches under study there is the ToxCast/Tox21 project. Following a previous study from our laboratory, here we present a new, more extensive analysis of ToxCast Phase I results, indicating that at the present state-of-art this approach is not able to predict the carcinogenicity of chemicals. Possible reasons for this mediocre performance are discussed, and opinions on ways to tune up the project in the next phases are presented.

IARC classes 1 and 2 carcinogens are successfully identified by an alternative strategy that detects DNA-reactivity and cell transformation ability of chemicals.

For decades, traditional toxicology has been the ultimate source of information on the carcinogenic potential of chemicals; however with increasing demand on regulation of chemicals and decreasing resources for testing, opportunities to accept "alternative" approaches have dramatically expanded. The need for tools able to identify carcinogens in shorter times and at a lower cost in terms of animal lives and money is still an open issue, and the present strategies and regulations for carcinogenicity pre-screening do not adequately protect human health. In previous papers, we have proposed an integrated in vitro/in silico strategy that detects DNA-reactivity and tissue disorganization/disruption by chemicals, and we have shown that the combination of Salmonella and Structural Alerts for the DNA-reactive carcinogens, and in vitro cell transformation assays for nongenotoxic carcinogens permits the identification of a very large proportion (up to 95%) of rodent carcinogens, while having a considerable specificity with the rodent noncarcinogens. In the present paper we expand the previous investigation and show that this alternative strategy identifies correctly IARC Classes 1 and 2 carcinogens. If implemented, this alternative strategy can contribute to improve the protection of human health while decreasing the use of animals.

A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH.

This paper presents an inventory of in silico screening tools to identify substance properties of concern under the European chemicals' legislation REACH. The objective is to support the selection and implementation of appropriate tools as building blocks within integrated testing strategies (ITS). The relevant concerns addressed are persistence, bioaccumulation potential, acute and long-term aquatic toxicity, PBT/vPvB properties ((very) persistent, (very) bioaccumulative, toxic), CMR (carcinogenicity, mutagenicity, reproductive toxicity), endocrine disruption and skin sensitisation. The inventory offers a comparative evaluation of methods with respect to the underlying algorithms (how does the method work?) and the applicability domains (when does the method work?) as well as their limitations (when does the method not work?). The inventory explicitly addresses the reliability of predictions of different in silico models for diverse chemicals by applicability domain considerations. The confidence in predictions can be greatly improved by consensus modelling that allows for taking conflicting results into account. The inventory is complemented by a brief discussion of socio-economic tools for assessing the potential efficiency gains of using in silico methods compared to traditional in vivo testing of chemical hazards.