A 21st century roadmap for human health risk assessment. (August 2014)

• Record Type: Journal Article
• Authors: Pastoor, Timothy P.; Bachman, Ammie N.; Bell, David R.; Cohen, Samuel M.; Dellarco, Michael; Dewhurst, Ian C.; Doe, John E.; Doerrer, Nancy G.; Embry, Michelle R.; Hines, Ronald N.; Moretto, Angelo; Phillips, Richard D.; Rowlands, J. Craig; Tanir, Jennifer Y.; Wolf, Douglas C.; Boobis, Alan R.
• Is Part Of: Critical reviews in toxicology. Volume 44:Number 3(2014:Mar.)
• Page Start: 1
• Page End: 5
• Publication Date: 2014-08
• Subjects: Toxicology -- Periodicals; Poisons -- Physiological effect -- Periodicals; Toxicology -- Periodicals; 615.9
• DOI: 10.3109/10408444.2014.931923
• Languages: English
• ISSNs: 1040-8444

Abstract

<abstract> <title>Abstract</title> <p>The Health and Environmental Sciences Institute (HESI)-coordinated Risk Assessment in the 21st Century (RISK21) project was initiated to develop a scientific, transparent, and efficient approach to the evolving world of human health risk assessment, and involved over 120 participants from 12 countries, 15 government institutions, 20 universities, 2 non-governmental organizations, and 12 corporations. This paper provides a brief overview of the tiered RISK21 framework called the roadmap and risk visualization matrix, and articulates the core principles derived by RISK21 participants that guided its development. Subsequent papers describe the roadmap and matrix in greater detail. RISK21 principles include focusing on problem formulation, utilizing existing information, starting with exposure assessment (rather than toxicity), and using a tiered process for data development. Bringing estimates of exposure and toxicity together on a two-dimensional matrix provides a clear rendition of human safety and risk. The value of the roadmap is its capacity to chronicle the stepwise acquisition of scientific information and display it in a clear and concise fashion. Furthermore, the tiered approach and transparent display of information will contribute to greater efficiencies by calling for data only as needed (enough precision to make a decision), thus conserving animals and other resources.</p> </abstract>