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Mechanistic tools in contaminant monitoring and risk assessment - addressing the disconnect
Citation:
Ankley, G. Mechanistic tools in contaminant monitoring and risk assessment - addressing the disconnect. SETAC North America, Sacramento, CA, November 04 - 08, 2018.
Impact/Purpose:
Although there are many new molecular tools that could support chemical monitoring and risk assessments, many of these tools currently are underutilized. To ensure their use, work is needed to demonstrate via case studies that these newer tools actually enhance decision making.
Description:
In 1989 the Society of Environmental Toxicology and Chemistry sponsored a Pellston workshop focused on the use of biochemical, physiological, and histological measurements for monitoring the occurrence and effects of contaminants in the environment. The outcome of that workshop stressed the many advantages of these types of mechanistic endpoints (e.g., rapidity/ease of measurement, diagnosis of specific stressors, provision of an early warning system, etc.), and highlighted impediments to their practical adoption and widespread use. Over the past three decades, the scientific community has made huge strides in the ability to generate mechanistic data through advances in areas such as transcriptomics, proteomics, metabolomics, and high throughput (HTP) in vitro testing, but there has been alarmingly little progress in terms of actually using this type of knowledge. So, where have we fallen short? Some of the answers to this question are reasonably straightforward involving, for example, the need for reliable translation of mechanistic data into responses meaningful to risk assessments. Other challenges are less easily defined and addressed. For example, to employ novel mechanistic tools/concepts, case studies are needed to demonstrate that results/decisions using newer approaches are of a quality comparable to (or better) than current techniques. However, this type of work can be exceedingly resource-intensive and the studies difficult to design/interpret with a high degree of confidence. Contributing to the difficulty in effectively executing case studies is the understandable tendency by scientists to highlight the utility of novel mechanistic tools to the point that the “bar for success” is perhaps unrealistic and, hence, difficult to fully achieve.