Evaluation of in silico development of aquatic toxicity species sensitivity distributions
- 作者：Mace G. Barron ; barron.mace@epa.gov ; Barron.Mace@epamail.epa.gov ; Crystal R. Jackson ; Jill A. Awkerman
- 关键词：Acute toxicity ; Species sensitivity distribution ; QSAR ; Interspecies correlation estimation
- 刊名：Aquatic Toxicology
- 出版年：2012
- 期刊代码：19_0166445x
- 类别：abs
- 出版时间：15 July, 2012
- 卷：116-117
- 期：Complete
- 页码：1-7
- 文件大小：490 K
摘要
Determining the sensitivity of a diversity of species to environmental contaminants continues to be a significant challenge in ecological risk assessment because toxicity data are generally limited to a few standard test species. This study assessed whether species sensitivity distributions (SSDs) could be generated with reasonable accuracy using only in silico modeling of toxicity to aquatic organisms. Ten chemicals were selected for evaluation that spanned several modes of actions and chemical classes. Median lethal concentrations (LC50s) were estimated using three internet-based quantitative structure activity relationship (QSAR) tools that employ different computational approaches: ECOSAR (Ecological Structure Activity Relationships), ASTER (Assessment Tools for the Evaluation of Risk), and TEST (Toxicity Estimation Software Tool). Each QSAR estimate was then used as input into the SSD module of the internet-based toxicity estimation program Web-ICE to generate an in silico estimated fifth percentile hazard concentration (HC5) for each of the ten chemicals. The accuracy of the estimated HC5s was determined by comparison to measured HC5s developed from an independent dataset of experimental acute toxicity values for a diversity of aquatic species. Estimated HC5s showed generally poor agreement with measured HC5s determined for all available aquatic species, but showed better agreement when species composition of the chemical specific SSDs were identical. These results indicated that LC50 variability and species composition were large sources of error in estimated HC5s. Additional research is needed to reduce uncertainty in HC5s using only in silico approaches and to develop computational approaches for predicting species sensitivity.