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- Brack, Werner, et al.
(författare)
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The SOLUTIONS project : Challenges and responses for present and future emerging pollutants in land and water resources management
- 2015
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 503, s. 22-31
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Tidskriftsartikel (refereegranskat)abstract
- SOLUTIONS (2013 to 2018) is a European Union Seventh Framework Programme Project (EU-FP7). The project aims to deliver a conceptual framework to support the evidence-based development of environmental policies with regard to water quality. SOLUTIONS will develop the tools for the identification, prioritisation and assessment of those water contaminants that may pose a risk to ecosystems and human health. To this end, a new generation of chemical and effect-based monitoring tools is developed and integrated with a full set of exposure, effect and risk assessment models. SOLUTIONS attempts to address legacy, present and future contamination by integrating monitoring and modelling based approaches with scenarios on future developments in society, economy and technology and thus in contamination. The project follows a solutions-oriented approach by addressing major problems of water and chemicals management and by assessing abatement options. SOLUTIONS takes advantage of the access to the infrastructure necessary to investigate the large basins of the Danube and Rhine as well as relevant Mediterranean basins as case studies, and puts major efforts on stakeholder dialogue and support. Particularly, the EU Water Framework Directive (WFD) Common Implementation Strategy (CIS) working groups, International River Commissions, and water works associations are directly supported. with consistent guidance for the early detection, identification, prioritisation, and abatement of chemicals in the water cycle. SOLUTIONS will give a specific emphasis on concepts and tools for the impact and risk assessment of complex mixtures of emerging pollutants, their metabolites and transformation products. Analytical and effect-based screening tools will be applied together with ecological assessment tools for the identification of toxicants and their impacts. The SOLUTIONS approach is expected to provide transparent and evidence-based candidates or River Basin Specific Pollutants in the case study basins and to assist future review of priority pollutants under the WFD as well as potential abatement options.
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| 2. |
- Honma, Masamitsu, et al.
(författare)
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Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity : outcomes of the Ames/QSAR International Challenge Project
- 2019
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Ingår i: Mutagenesis. - Oxford : Oxford University Press (OUP). - 0267-8357 .- 1464-3804. ; 34:1, s. 3-16
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Tidskriftsartikel (refereegranskat)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.
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