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Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials., (© 2024. Springer Nature Limited.)

In silico approaches are now increasingly accepted in several areas of toxicology to rapidly assess chemical hazard without the need for animal testing. Among in silico tools, quantitative and qualitative structure-activity approaches ((Q)SARs) are the most typically applied methods to predict hazard in the absence of experimental data. This paper provides an overview of different protocols that can be applied while dealing with (Q)SARs in different scenarios, namely, (Q)SAR development, use, and validation. Examples of protocols adopted in the three scenarios are reported, derived from the authors' experience in working at the Predictive Toxicology unit of the Italian National Institute of Health, focusing on the endpoints of carcinogenicity and genotoxicity.The illustrated activities are in line with the Institute's mission, the main center of research, control, and technical-scientific advice on public health in Italy., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

(Quantitative) structure-activity relationship ([Q]SAR) methodologies are widely applied to predict the (eco)toxicological effects of chemicals, and their use is envisaged in different regulatory frameworks for filling data gaps of untested substances. However, their application to the risk assessment of nanomaterials is still limited, also due to the scarcity of large and curated experimental datasets. Despite a great amount of nanosafety data having been produced over the last decade in international collaborative initiatives, their interpretation, integration and reuse has been hampered by several obstacles, such as poorly described (meta)data, non-standard terminology, lack of harmonized reporting formats and criteria. Recently, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles have been established to guide the scientific community in good data management and stewardship. The EU H2020 Gov4Nano project, together with other international projects and initiatives, is addressing the challenge of improving nanosafety data FAIRness, for maximizing their availability, understanding, exchange and ultimately their reuse. These efforts are largely supported by the creation of a common Nanosafety Data Interface, which connects a row of project-specific databases applying the eNanoMapper data model. A wide variety of experimental data relating to characterization and effects of nanomaterials are stored in the database; however, the methods, protocols and parameters driving their generation are not fully mature. This article reports the progress of an ongoing case study in the Gov4nano project on the reuse of in vitro Comet genotoxicity data, focusing on the issues and challenges encountered in their FAIRification through the eNanoMapper data model. The case study is part of an iterative process in which the FAIRification of data supports the understanding of the phenomena underlying their generation and, ultimately, improves their reusability., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2018. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society.)

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.

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

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., (Copyright © 2015 Elsevier B.V. All rights reserved.)

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.

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., (Copyright © 2013 Elsevier B.V. All rights reserved.)

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.

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.

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.

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.

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 PCDD's; 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.

A chemical plant located in Brescia, an industrial city in North-Western Italy, produced polychlorobiphenyls (PCBs) during a 30-50 year period, causing widespread pollution of the surrounding agricultural area. This area contains several small farms, which principally produce veal meat for private consumption of the farmers' families. The pollution went undiscovered for many years, during which period contaminated food was regularly consumed. This paper reports the polychlorodibenzodioxin (PCDD), polychlorodibenzofuran (PCDF) and PCB levels of a serum sample pooled from the consumers of contaminated food, compared to six population groups of the city of Brescia. Four of these groups were selected in order to represent, respectively, the local general population and the residents of three zones of the polluted area, while the last two groups represented, respectively, the present and the former workers of the plant. One human milk sample from one of the consumers of contaminated food was also analyzed. Results show that the consumers of the contaminated food and the former workers of the plant display considerably higher levels than all other groups. The levels of general population and of all other groups were generally similar both to each other and to the range of literature values for unexposed populations. The respective contribution of PCDDs, PCDFs, mono-ortho and non-ortho PCBs (dioxin-like PCBs) to (Toxicity Equivalents) TEQ of the population groups of this study were also compared to literature data: the two groups with a high contamination level, together with the human milk sample, displayed a higher incidence of mono-ortho PCBs and a lower contribution of PCDD, possibly correlated with the source of contamination.

This study deals with a PCB, PCDD and PCDF contamination in Brescia, a city in the North-West of Italy, affecting an area with about 11000 inhabitants. The area is close to an industrial plant that produced, in total, some 31,000 ton of PCB. A relevant part of the polluted area is agricultural soil, where cattle were fed with polluted forage and farmers were consuming their own products, so that contamination led eventually to human exposure. Total levels of PCDD/Fs varied from 8 to 592 pgTE(WHO)/g for soil samples and when the dioxin-like PCBs (dl-PCBs) are included, the levels varied from 14.6 to 1033.7 pgTE(WHO)/g. In several cases, the legal limit was exceeded by more than one order of magnitude, with the highest contamination in some agricultural areas and in the surrounding zones. For the forage samples, total levels of PCDD/Fs varied from 0.29 to 2.04 pgTE(WHO)/g and, when dl-PCBs are included, this range increased from 2.04 to 4.75 pgTE(WHO)/g. PCB contamination of the forage through vapor condensation seemed to be relevant. The toxic contribution of dl-PCBs is always relevant and must be considered for risk management. The main component of the contamination source is probably a heavy PCB mixture, such as Aroclor 1262. The study dealt generally with the contamination transfer of PCBs, PCDDs and PCDFs from soil up to humans across the food chain. Results on soils and forages are shown, while measurements concerning the contamination of the animals fed with contaminated forage, and the exposure of the farmers (through human serum analyses), as compared to general population, will be reported in a dedicated paper.

Emissions from a spark-ignition (SI) heavy-duty (HD) urban bus engine with a three-way catalyst (TWC), fuelled with compressed natural gas (CNG), were chemically analyzed and tested for genotoxicity. The results were compared with those obtained in a previous study on an equivalent diesel engine, fuelled with diesel oil (D) and a blend of the same with 20% vegetable oil (B20). Experimental procedures were identical, so that emission levels of the CNG engine were exactly comparable to the ones of the diesel engine. The experimental design was focused on carcinogenic compounds and genotoxic activity of exhausts. The results obtained show that the SI CNG engine emissions, with respect to the diesel engine fuelled with D, were nearly 50 times lower for carcinogenic polycyclic aromatic hydrocarbons (PAHs), 20 times lower for formaldehyde, and more than 30 times lower for particulate matter (PM). A 20-30 fold reduction of genotoxic activity was estimated from tests performed. A very high reduction of nitrogen oxides (NO(X)) was also measured. The impact of diesel powered transport on urban air quality, and the potential benefits deriving from the use of CNG for public transport, are discussed.

The chemical and toxicological characteristics of emissions from an urban bus engine fueled with diesel and biodiesel blend were studied. Exhaust gases were produced by a turbocharged EURO 2 heavy-duty diesel engine, operating in steady-state conditions on the European test 13 mode cycle (ECE R49). Regulated and unregulated pollutants, such as carcinogenic polycyclic aromatic hydrocarbons (PAHs) and nitrated derivatives (nitro-PAHs), carbonyl compounds and light aromatic hydrocarbons were quantified. Mutagenicity of the emissions was evaluated by the Salmonella typhimurium/mammalian microsome assay. The effect of the fuels under study on the size distribution of particulate matter (PM) was also evaluated. The use of biodiesel blend seems to result in small reductions of emissions of most of the aromatic and polyaromatic compounds; these differences, however, have no statistical significance at 95% confidence level. Formaldehyde, on the other hand, has a statistically significant increase of 18% with biodiesel blend. In vitro toxicological assays show an overall similar mutagenic potency and genotoxic profile for diesel and biodiesel blend emissions. The electron microscopy analysis indicates that PM for both fuels has the same chemical composition, morphology, shape and granulometric spectrum, with most of the particles in the range 0.06-0.3 microm.

Pressurized Fluid Extraction (PFE) was evaluated for the extraction of polycyclic aromatic hydrocarbons (PAHs) and nitro-derivatives from diesel particulate matter. Extraction conditions were set up by performing several tests in which temperature, solvent strength, pressure, and static time were gradually increased. The results obtained on a laboratory test material made of a "lean" (low content of soluble fraction) Diesel particulate matter indicate that very severe conditions were needed in order to obtain better recoveries of the higher molecular weight molecules. Moreover, extraction efficiency seems to be influenced by the amount of soluble matter in the particulate, so that a "lean" particulate appears more difficult to extract. Recoveries of the deuterated standards of certain PAHs (i.e. indeno[1,2,3- cd]pyrene) were incomplete even with the toughest conditions tested. Experiments carried out on a certified material (SRM 1650 from NIST) also indicate that PFE can perform a better extraction of some of the PAHs than the method used for certification, but still incomplete. Comparison of results obtained on the SRM with different extraction techniques suggests that the composition of the extract varies considerably with the extraction technique and conditions. It is relevant to notice that recent Diesel engines produce leaner particulate: for future materials more drastic extraction conditions will be required.