QSAR models of human data can enrich or replace LLNA testing for human skin sensitization.

ABSTRACT: Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating con

SUBMITTER: Alves VM 

PROVIDER: S-EPMC5473635 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature