Chiara Laura Battistelli

PCB, PCDD and PCDF contamination of food of animal origin as the effect of soil pollution and the cause of human exposure in Brescia

In Brescia a PCB production plant polluted soil and forage of the surrounding fields and caused a significant contamination of meat and milk of the cattle fed with local forage. This in turn induced elevated blood levels of PCDDs, PCDFs and PCBs in the consumers. The contamination levels and profiles measured in the perirenal fat, in the liver and in the milk of the overall 28 contaminated bovines are reported. TEQ levels varied from 30 to 81 pg WHO(2005)-TEQ g(-1) (38-103 pg WHO(1997)-TEQ) for perirenal fat, from 107 to 138 pg WHO(2005)-TEQ g(-1) fat (128-168 pg WHO(1997)-TEQ) for liver and from 45 to 50 pg WHO(2005)-TEQg(-1) fat (56-65pg WHO(1997)-TEQ) for milk; all these values are roughly tenfold higher than the European limits. Non-ortho dioxin-like (dl)PCBs are by far the largest contributors to TEQ and PCDF contribution also largely prevail over PCDDs; both these features are also present in both the contaminated forages and in the serum of consumers of contaminated food. The indicator PCB levels are in the following ranges: 226-664 ng g(-1) for perirenal fat; 929-1822 ng g(-1) fat for liver; 183-477 ng g(-1) fat for milk; their level is about 100 times higher than the regional background. The liver samples displayed an overall TEQ several times higher than the perirenal fat from either the same animal or the same pool of animals; the increase in liver concentration was significantly higher for PCDD and PCDF congeners than for dlPCBs, and it was maximum for OCCD.

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.

Human Milk as a Vector and an Indicator of Exposure to PCBs and PBDEs: Temporal Trend of Samples Collected in Rome

Thirty-seven polychlorobiphenyl (PCB) congeners and seven polybromodiphenylether (PBDE) congeners were measured in human milk samples collected in Rome between 2005 and 2007. The comparison of results with two previous studies performed in Rome in 1984 and in 2000–2001 indicates a 64% decrease of PCB levels, still in progress; profile differences with time were also evident as lighter congeners are less relevant now; data are in good agreement with recent European studies. PBDE contamination profiles were different in individual samples and a similar variability was observed in data from different countries, suggesting different exposure pathways and profiles.

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.

Mutagenicity, Carcinogenicity, and Other End points

Aiming at understanding the structural and physical chemical basis of the biological activity of chemicals, the science of structure-activity relationships has seen dramatic progress in the last decades. Coarse-grain, qualitative approaches (e.g., the structural alerts), and fine-tuned quantitative structure-activity relationship models have been developed and used to predict the toxicological properties of untested chemicals. More recently, a number of approaches and concepts have been developed as support to, and corollary of, the structure-activity methods. These approaches (e.g., chemical relational databases, expert systems, software tools for manipulating the chemical information) have dramatically expanded the reach of the structure-activity work; at present, they are powerful and inescapable tools for computer chemists, toxicologists, and regulators. This chapter, after a general overview of traditional and well-known approaches, gives a detailed presentation of the latter more recent support tools freely available in the public domain.

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.

The Syrian hamster embryo cells transformation assay identifies efficiently nongenotoxic carcinogens, and can contribute to alternative, integrated testing strategies.

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 chemical carcinogenicity. However, this strategy cannot detect nongenotoxic carcinogens. Since up to 25% of IARC human carcinogens are recognized to have nongenotoxic mechanisms of action, the risk they pose to human health cannot be disregarded, and it is urgent to fill the gap in the tools for alternative testing. In this paper, we analyze from different perspectives the ability of Cell Transformation Assays to identify nongenotoxic carcinogens, and we conclude that the Syrian hamster embryo cells test is able to identify nongenotoxic carcinogens with 80-90% efficiency, and thus, can play an important role in integrated, alternative testing strategies.

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

&NA; 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. HighlightsData from OECD‐recommended in vivo assays for Endocrine Disruptors were compared.Only the Uterotrophic and 21‐Day Fish assays results correlated with each other.Interaction with receptors is a major determinant of the Uterotrophic and 21‐Day Fish results.Predictive models can be built with a limited selection of HTS assays for receptor interaction.

(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.

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

Abstract 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.