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.

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.

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.

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.

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.

Protocol for designing INVITES-IN, a tool for assessing the internal validity of in vitro studies.

This protocol describes the design and development of a tool for evaluation of the internal validity of in vitro studies, which is needed to include the data as evidence in systematic reviews and chemical risk assessments. The tool will be designed specifically to be applied to cell culture studies, including, but not restricted to, studies meeting the new approach methodology (NAM) definition. The tool is called INVITES-IN (IN VITro Experimental Studies INternal validity). In this protocol, three of the four studies that will be performed to create the release version of INVITES-IN are described. In the first study, evaluation of existing assessment tools will be combined with focus group discussions to identify how characteristics of the design or conduct of an in vitro study can affect its internal validity. Bias domains and items considered to be of relevance for in vitro studies will be identified. In the second study, group agreement on internal validity domains and items of importance for in vitro studies will be identified via a modified Delphi methodology. In the third study, the draft version of the tool will be created, based on the data on relevance and importance of bias domains and items collected in Studies 1 and 2. A separate protocol will be prepared for the fourth study, which includes the user testing and validation of the tool, and collection of users' experience.

The new ISSMIC database on in vivo micronucleus and its role in assessing genotoxicity testing strategies.

This paper presents a new curated database on in vivo micronucleus mutagenicity results, called ISSMIC. It is freely available at: http://www.iss.it/ampp/dati/cont.php?id=233&lang=1&tipo=7. The experimental results were critically reviewed, and evidence on target cell exposure was considered as well. The inspection of ISSMIC demonstrates that a large proportion of reported negative results in the literature (231 out 566 ISSMIC chemicals) lack a clear-cut, direct demonstration of toxicity at the target cells. Using this updated database, the predictive value of a compilation of Structural Alerts (SA) for in vivo micronucleus recently implemented in the expert system Toxtree was investigated. Individually, most of the SA showed a high Positive Predictivity (∼80%), but the need for further expanding the list of alerts was pointed out as well. The role of in vivo micronucleus in strategies for carcinogenicity prediction was re-evaluated. In agreement with previous analyses, the data point to a low overall correlation with carcinogenicity. In addition, given the cost in animal lives and the time required for the experimentation, in many programs, the in vivo tests are used only to assess in vitro positive results. The ability of in vivo micronucleus to identify real positives (i.e. carcinogens) among chemicals positive in Salmonella or among chemicals inducing in vitro chromosomal aberrations was studied. It appears that the in vivo micronucleus test does not have added value and rather impairs the prediction ability of the in vitro tests alone. The overall evidence indicates that in vivo micronucleus--in its present form--cannot be considered an useful tool for routine genotoxicity testing but should be used in targeted mechanistic studies.

Molecular characterization of rotavirus strains from children with diarrhea in Italy, 2007-2009.

The surveillance network RotaNet-Italia was established in 2007 in order to investigate the diversity of co-circulating rotavirus strains in Italy, and to provide a baseline for future assessment of possible effects of vaccine implementation in selecting novel versus common rotavirus strains. A total of 2,645 rotavirus strains from pediatric patients with acute diarrhea were collected over three consecutive seasons from September 2006 through August 2009, and partially characterized by standardized multiplex RT-PCR. Most of strains (89.1%) belonged to genotypes G1-G4, and G9, associated with either P[8] or P[4], commonly found in humans worldwide. However, in at least 2.0% of cases, viruses exhibited either a G or P type typical of animal viral strains, suggesting gene reassortment events between rotaviruses of different origin. Mixed infections with two or more rotavirus strains were observed frequently (7.6% of patients), and depended on the frequencies of co-circulating rotaviruses of one particular genotype. The numbers and genotypes of likely natural reassortants of common genotype rotaviruses were found to be correlated with the observed numbers and genotypes of mixed infections. Large variation in the relative frequency of different rotavirus genotypes was observed between different seasons and/or areas of Italy, suggesting independent evolution or differential introduction of viral strains with respect to both time and space.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity.

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

Exploring in vitro/in vivo correlation: lessons learned from analyzing phase I results of the US EPA's ToxCast Project.

The research on alternative toxicological methods provides, among other things, a privileged viewpoint on one of the central issues of modern biomedical research--the relationship between (a) biological phenomena observed at the level of tissues and organisms and (b) their cellular and molecular bases as studied in isolated systems in vitro. The newly released ToxCast Phase 1 results, subject to initial analysis, converge with evidence from other fields (e.g., research on drug design with intensive use of omics technologies, traditional research on alternative tests) in indicating a low degree of the in vitro/in vivo correlation overall. In addition, this and other approaches point to the need for combining biological and chemical information in exploring the in vitro to in vivo connection.

A pre-existing population of ZEB2+ quiescent cells with stemness and mesenchymal features dictate chemoresistance in colorectal cancer.

BACKGROUND: Quiescent/slow cycling cells have been identified in several tumors and correlated with therapy resistance. However, the features of chemoresistant populations and the molecular factors linking quiescence to chemoresistance are largely unknown. METHODS: A population of chemoresistant quiescent/slow cycling cells was isolated through PKH26 staining (which allows to separate cells on the basis of their proliferation rate) from colorectal cancer (CRC) xenografts and subjected to global gene expression and pathway activation analyses. Factors expressed by the quiescent/slow cycling population were analyzed through lentiviral overexpression approaches for their ability to induce a dormant chemoresistant state both in vitro and in mouse xenografts. The correlation between quiescence-associated factors, CRC consensus molecular subtype and cancer prognosis was analyzed in large patient datasets. RESULTS: Untreated colorectal tumors contain a population of quiescent/slow cycling cells with stem cell features (quiescent cancer stem cells, QCSCs) characterized by a predetermined mesenchymal-like chemoresistant phenotype. QCSCs expressed increased levels of ZEB2, a transcription factor involved in stem cell plasticity and epithelial-mesenchymal transition (EMT), and of antiapototic factors pCRAF and pASK1. ZEB2 overexpression upregulated pCRAF/pASK1 levels resulting in increased chemoresistance, enrichment of cells with stemness/EMT traits and proliferative slowdown of tumor xenografts. In parallel, chemotherapy treatment of tumor xenografts induced the prevalence of QCSCs with a stemness/EMT phenotype and activation of the ZEB2/pCRAF/pASK1 axis, resulting in a chemotherapy-unresponsive state. In CRC patients, increased ZEB2 levels correlated with worse relapse-free survival and were strongly associated to the consensus molecular subtype 4 (CMS4) characterized by dismal prognosis, decreased proliferative rates and upregulation of EMT genes. CONCLUSIONS: These results show that chemotherapy-naive tumors contain a cell population characterized by a coordinated program of chemoresistance, quiescence, stemness and EMT. Such population becomes prevalent upon drug treatment and is responsible for chemotherapy resistance, thus representing a key target for more effective therapeutic approaches.

PROFALIGN algorithm identifies the regions containing folding determinants by scoring pairs of hydrophobic profiles of remotely related proteins.

Profile comparison methods have been shown to be very powerful in creating accurate alignments of protein sequences, especially in the case of remotely related proteins (RRP). These methods take advantage of the observation that hydrophobic profiles are more conserved than the corresponding amino acid sequences. Here, we present the PROFALIGN algorithm, which allows one to perform a detailed comparative analysis, at both local and global levels of two protein sequence profiles. The user can either choose among four different hydrophobic scales (Miyazawa-Jernigan, Eisenberg, Engelman-Steiz, and Kyte-Doolittle) or can add a personal scale. The interface is designed for a wide range of users, including those who are not involved in protein research. It allows one to vary the alignment parameters (such as gap penalties, embedding, and profile smoothness). Secondary structure propensity is added as an optional alignment filter. Similar segments of two proteins are singled out on the basis of score. We have tested the algorithm with different Src homology 3 (SH3) domain fragments sharing low sequence homology but very similar three-dimensional (3D) structures. By using the Miyazawa-Jernigan hydrophobic scale, PROFALIGN was able to detect the strong correlation between the regions that are known to be crucial for SH3 transition state topology. PROFALIGN seems able to identify most of the mutual alignment of structures on the basis of their hydrophobic profiles, delimiting the regions containing the key determinants of folding. Therefore, the present methodology may be useful for the detection of the most structurally relevant positions inside remote related proteins.

(Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks.

Knowledge of the genotoxicity and carcinogenicity potential of chemical substances is one of the key scientific elements able to better protect human health. Genotoxicity assessment is also considered as prescreening of carcinogenicity. The assessment of both endpoints is a fundamental component of national and international legislations, for all types of substances, and has stimulated the development of alternative, nontesting methods. Over the recent decades, much attention has been given to the use and further development of structure-activity relationships-based approaches, to be used in isolation or in combination with in vitro assays for predictive purposes. In this chapter, we briefly introduce the rationale for the main (Q)SAR approaches, and detail the most important regulatory initiatives and frameworks. It appears that the existence and needs of regulatory frameworks stimulate the development of better predictive tools; in turn, this allows the regulators to fine-tune their requirements for an improved defense of human health.