Quantifying the benefits of using read-across and in silico techniques to fulfill hazard data requirements for chemical categories.
Substantial benefits are realized through the use of read-across and in silico techniques to fill data gaps for structurally similar substances. Considerable experience in applying these techniques was gained under two voluntary high production volume (HPV) chemical programs - the International Council of Chemical Associations' (ICCA) Cooperative Chemicals Assessment Programme (with the cooperation of the Organization of Economic Cooperation and Development) and the U.S. Environmental Protection Agency's HPV Challenge Program. These programs led to the compilation and public availability of baseline sets of health and environmental effects data for thousands of chemicals. The American Cleaning Institute's (ACI) contribution to these national and global efforts included the compilation of these datasets for 261 substances. Chemicals that have structural similarities are likely to have similar environmental fate, physical-chemical and toxicological properties, which was confirmed by examining available data from across the range of substances within categories of structurally similar HPV chemicals. These similarities allowed the utilization of read-across, trend analysis techniques and qualitative structure activity relationship ((Q)SAR) tools to fill data gaps. This paper presents the first quantification of actual benefits resulting from avoided testing through the use of read-across and in silico tools. Specifically, in the evaluation of these 261 noted substances, the use of 100,000-150,000 test animals and the expenditures of $50,000,000 to $70,000,000 (US) were avoided.
Technical Department, American Cleaning Institute, 1331 L Street, NW, Suite 650, Washington, DC, USA. Electronic address: kstanton@cleaninginstitute.org.
Kathleen
K
Stanton
Technical Department, American Cleaning Institute, 1331 L Street, NW, Suite 650, Washington, DC, USA. Electronic address: fkruszewski@cleaninginstitute.org.
Francis H
FH
Kruszewski
D000818:Animals; D003198:Computer Simulation; D016208:Databases, Factual; D015386:Hazardous Substances / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D014481:United States
D015386:Hazardous Substances
(Q)SAR; Animal alternatives; Category approach; HPV; In silico; Read-across; Trend analysis