Computational toxicology: realizing the promise of the toxicity testing in the 21st century.
BACKGROUND
The National Academies' Standing Committee on Use of Emerging Science for Environmental Health Decisions held a meeting (21-22 September 2009 in Washington, DC) titled "Computational Toxicology: From Data to Analyses to Applications." This commentary reflects on the presentations and roundtable discussions from the meeting that were designed to review the state of the art in the field and the practical applications of the new science and to provide focus to the field.


OBJECTIVE
The meeting considered two topics: the emerging data streams amenable to computational modeling and data mining, and the emerging data analysis and modeling tools.


CONCLUSIONS
Computational toxicology is a subdiscipline of toxicology that aims to use the mathematical, statistical, modeling, and computer science tools to better understand the mechanisms through which a given chemical induces harm and, ultimately, to be able to predict adverse effects of the toxicants on human health and/or the environment. The participants stressed the importance of computational toxicology to the future of environmental health sciences and regulatory decisions in public health; however, many challenges remain to be addressed before the findings from high-throughput screens and in silico models may be considered sufficiently robust and informative.


CONCLUSIONS
Many scientists, regulators, and the general public believe that new and better ways to assess human toxicity are now needed, and technological breakthroughs are empowering the field of toxicity assessment. Even though the application of computational toxicology to environmental health decisions requires additional efforts, the merger of the power of computers with biological information is poised to deliver new tools and knowledge.
Rusyn
Ivan
I
Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599-7431, USA. iir@unc.edu
Daston
George P
GP
D019295:Computational Biology / Q000639:trends*; D003198:Computer Simulation; D057225:Data Mining; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D018675:Toxicity Tests / Q000379:methods; D014116:Toxicology / Q000639:trends*