| Literature DB >> 24521051 |
Vicki Stone1, Stefano Pozzi-Mucelli, Lang Tran, Karin Aschberger, Stefania Sabella, Ulla Vogel, Craig Poland, Dominique Balharry, Teresa Fernandes, Stefania Gottardo, Steven Hankin, Mark G J Hartl, Nanna Hartmann, Danial Hristozov, Kerstin Hund-Rinke, Helinor Johnston, Antonio Marcomini, Oliver Panzer, Davide Roncato, Anne T Saber, Håkan Wallin, Janeck J Scott-Fordsmand.
Abstract
BACKGROUND: To assess the risk of all nanomaterials (NMs) on a case-by-case basis is challenging in terms of financial, ethical and time resources. Instead a more intelligent approach to knowledge gain and risk assessment is required.Entities:
Mesh:
Year: 2014 PMID: 24521051 PMCID: PMC3931673 DOI: 10.1186/1743-8977-11-9
Source DB: PubMed Journal: Part Fibre Toxicol ISSN: 1743-8977 Impact factor: 9.400
Figure 1A heat map illustrating the number and pattern of nanomaterial publications identified (December 2012) in Web of Science and PubMed. This particular example focuses on systemic effects identified in human toxicity studies using both in vitro and in vivo approaches. Black signifies more than 50 publications, grey represents 20–50 publications while white is less than 20. A full set of heat maps for local and systemic effects for human toxicity as well as for ecotoxicity is provided in the gap analysis at http://www.nano.hw.ac.uk/research-projects/itsnano.html.
Figure 2Proposed research prioritisation for generating an effective PC ID to inform an Intelligent Testing Strategy. The research priorities are graded across the diagram, with hexagons to the left being of short term-priority (< 5 years) stretching to longer term priorities on the right (> 15 years). Grey hexagons represent modelling components that will lead to the ITS. The short-term priorities should be considered in the context of the long-term priorities to ensure that they generate the information needed to provide robust foundations for the longer-term priorities.
Figure 3Proposed sequence of events for implementing an exposure testing strategy aimed at grouping and modelling NMs. The research priorities are graded across the diagram, with hexagons to the left being of short term-priority (< 5 years) stretching to longer term priorities on the right (> 15 years). Grey hexagons represent modelling components that will lead to the ITS. The short-term priorities should be considered in the context of the long-term priorities to ensure that they generate the information needed to provide robust foundations for the longer-term priorities.
Figure 4The research steps required to formulate a Hazard ID for incorporation into the ITS. The research priorities are graded across the diagram, with hexagons to the left being of short term-priority (< 5 years) stretching to longer term and distant priorities on the right (> 15 years). Grey hexagons represent modelling components that will lead to the ITS. The short-term priorities should be considered in the context of the long-term priorities to ensure that they generate the information needed to provide robust foundations for the longer-term priorities.
Figure 5The diagram identifies the components required for the development of a grouping/ranking approach for NMs. Hexagon colours relate to PC ID (blue), Exposure (brown), Hazard (green), Cross-cutting issues, implementation into a RA framework (grey) and the final goal of the ITS (white). The diagram is intended to start on the left (NM) and finish on the right, but there is no strict order of passage between the hexagons to achieve the final goal. The research priorities are graded across the diagram, with hexagons to the left being of short term-priority (< 5 years) stretching to longer term and distant priorities on the right (> 15 years). It is important to note that contrary to similar representations in preceding chapters, the hexagons for grouping/ranking are not necessarily intrinsically linked, but contribute to overall progress towards grouping and/or ranking of NMs as well as modelling. This example is dominated by hazard, but in other scenarios the exposure or physicochemical priorities may be more dominant.
Figure 6An overview of the risk assessment of NMs in the context of the ITS-NANO research strategy. The grey arrows indicate an iterative process and the boxes below represent the steps of data generation (for both hazard and exposure data), data collection, interpretation and integration as well as risk assessment method development and risk management.
Figure 7The diagram illustrates the connections between the identified research priorities, and the implementation of the subsequent acquired knowledge and methods in the risk evaluation process. Each hexagon represents a priority research need, and each interface a logical relationship; with black hexagons representing NMs around the outside and the ITS modelling tools in the centre. Between the three priority research areas (Physicochemical, Exposure and Hazard ID) and the central ITS are the grouping/ranking approaches (bold hexagons) needed to streamline the data requirements. The blue arrows indicate the direction of research progress over time (from the perimeter of the diagram towards the core). The outputs of the ITS feed into the risk assessment frameworks at the bottom of the diagram.