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.

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.

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.

Carcinogenicity and mutagenicity are toxicological end points posing considerable concern for human health. Due to the cost in animal lives, time and money, alternative approaches to the rodent bioassay were designed based on: i) identification of mutations and ii) structure-activity relationships. Evidence on i) and ii) is summarized, covering 4 decades (1971 - 2010). A comprehensive, state-of-the-art perspective on alternatives to the carcinogenicity bioassay. Research to develop mutagenicity-based tests to predict carcinogenicity has generated useful results only for a limited area of the chemical space, that is, for the DNA-reactive chemicals (able to induce cancer, together with a wide spectrum of mutations). The most predictive mutagenicity-based assay is the Ames test. For non-DNA-reactive chemicals, that are Ames-negative and mutagenic in other in vitro assays (e.g., clastogenicity), no correlation with carcinogenicity is apparent. The knowledge on DNA reactivity permits the identification of genotoxic carcinogens with the same efficiency of the Ames test. Thus, a chemical mutagenic in Salmonella and/or with structural alerts should be seriously considered as a potential carcinogen. No reliable mutagenicity-based alternative tools are available to assess the risk of non-DNA-reactive chemicals.

A surveillance network was implemented by the Istituto Superiore di Sanità of Rome in collaboration with laboratories of virology in Czech Republic, Slovenia, Croatia, Albania, and Bulgaria. About 1,500 rotavirus-positive stool samples were collected from children with severe gastroenteritis admitted to hospitals or outpatient wards between 2004 and 2006. The G and P genotypes were determined by reverse transcription-nested PCR. Significant differences were found in the geographical distributions of rotavirus genotypes between countries participating in the study. The prevalence of “common” G/P combinations, G1P[8], G3P[8], G4P[8], and G2P[4], ranged between 50 and 85%. The G9 genotype, which is emerging worldwide, was identified in 2 to 35% of all samples depending on the country. Unusual combinations, such as G1 or G4 associated with P[4] or G2 with P[8], which may have arisen by reassortment between human strains, were found in samples from 3 to 20% of patients. The uncommon genotypes G8P[8] and G10P[6], which may have an animal origin, were also identified. Double infections with two rotavirus strains were observed in between 1.7 and 14% of cases studied. Our findings might implicate challenges for rotavirus vaccine implementation in a wide geographic area of the Balkans and Central-Eastern Europe and underscore the importance of extensive strain surveillance for success in vaccine development.