Evidence-based tools in toxicological basic research

Basic research is critical to toxicology in that it contributes to the understanding of how biological systems respond to perturbation (mechanistic studies) and to advances in toxicity testing methodologies (e.g., toxicogenomics and in silico models). Basic research also advances the understanding of inter- and intraspecies variability, and improving the assessment human population responses (biomarker studies). The identification and validation of biomarkers (relevant to both exposure and outcome assessment) also draws upon basic research. Similarly, basic research is critical to the process of phenotypic anchoring for new computational and toxicogenomic methods. Evidence-based considerations can enhance the translational relationship between basic research and toxicology. These considerations include criteria for evaluating research data, the standardization of methodologies (e.g., platforms, procedures, genotypic characterization of cells, and quality control), the selection of relevant doses or concentrations to toxicological problems, and the provision of parameter information (e.g., free chemical concentrations in cell cultures). A number of tools and approaches relevant to basic research in an evidence-based toxicology were identified. With regard to data quality, criteria are required to evaluate evidence with regard to relevance and quality, and standardized data analysis and presentation. Data quality should also be evaluated by the level of experimental design detail available (in journals or otherwise), and relevance to the toxicological question. With regard to publication, editorial advice and good referee practice should also be encouraged. An evidence-based toxicology movement should also aim to improve data availability. Original data should be available whenever possible, public-access databases should be encouraged, and publication bias should be addressed (including the publication of negative results). To apply basic research to new toxicological methods it is important to understand variability and uncertainty, to apply criteria to data with respect to relevance to humans, and to design or select the most relevant experimental models. Available evidence-based tools includes established validation criteria (e.g., those developed by the Organization for Economic Cooperation and Development), and guidelines such as those for Good Cell Culture Practice.

GRIESINGER Claudius;  HOFFMANN Sebastian;  KINSNER-OVASKAINEN Agnieszka;  COECKE Sandra;  HARTUNG Thomas; 

2010-01-25

SAGE PUBLICATIONS LTD

JRC56784

0960-3271,   

https://publications.jrc.ec.europa.eu/repository/handle/JRC56784,   

10.1177/0960327109105777,