Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009 Aug;29(6):545-50.
doi: 10.1002/jat.1443.

Updating the skin sensitization in vitro data assessment paradigm in 2009

Affiliations

Updating the skin sensitization in vitro data assessment paradigm in 2009

David A Basketter et al. J Appl Toxicol. 2009 Aug.

Abstract

Approaches to the interpretation of guinea pig skin sensitization data for both hazard identification and potency assessment have been understood for many years. More recently, the local lymph node assay has to a large extent replaced the earlier guinea pig assays, not least because it provides a more clearly defined and transparent means of identifying hazard, and the ability to measure relative skin sensitization potency. However, beginning in 2009 there will be considerable pressure replace all in vivo assays for skin sensitization with alternative approaches that do not require the use of animals (in vitro and/or in silico methods). As there is a common view that multiple assays will be needed to achieve complete replacement of the in vivo tests, a strategy for the integration of the available data will be required. There has been at least one previous attempt to develop a framework that would provide for integration of relevant information from different sources to reach informed decisions about skin sensitization potential and potency. It is timely now, in the light of recent developments and initiatives, to revisit this paradigm with a view to developing recommendations for modification and refinement. In addition to this, the need for performance standards and an agreed 'gold standard' dataset against which to validate both alternatives and new prediction models is discussed.

PubMed Disclaimer

Comment in

Similar articles

Cited by

LinkOut - more resources