Home About us Contact
|
Home About us Contact | ||
|
|
||
Mechanistic Data (mechanistic + data)
Selected AbstractsUse of mechanistic data in IARC evaluationsENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Issue 2 2008Vincent James Cogliano Abstract Consideration of mechanistic data has the potential to improve the analysis of both epidemiologic studies and cancer bioassays. IARC has a classification system in which mechanistic data can play a pivotal role. Since 1991, IARC has allowed an agent to be classified as carcinogenic to humans (Group 1) when there is less than sufficient evidence in humans but there is sufficient evidence in experimental animals and "strong evidence in exposed humans that the agent acts through a relevant mechanism of carcinogenicity." Mechanistic evidence can also substitute for conventional cancer bioassays when there is less than sufficient evidence in experimental animals, just as mechanistic evidence can substitute for conventional epidemiologic studies when there is less than sufficient evidence in humans. The IARC Monographs have used mechanistic data to raise or lower a classification that would be otherwise based on epidemiologic studies and cancer bioassays only. Recently, the IARC Monographs have evaluated several agents where mechanistic data were pivotal to the overall evaluation: benzo[a]pyrene, carbon black and other poorly soluble particles, ingested nitrates and nitrites, and microcystin-LR. In evaluating mechanistic data, it is important to consider alternative mechanistic hypotheses, because an agent may induce tumors through multiple mechanisms. Environ. Mol. Mutagen., 2008. © 2008 Wiley-Liss, Inc. [source] Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessmentENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 3 2010Gerald T. Ankley Abstract Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk,effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework. Environ. Toxicol. Chem. 2010;29:730,741. © 2009 SETAC [source] | ||||||||||