Traditional in vitro tests investigate only a few biomarkers and provide limited information to give reliable results. Animal tests provide much more information which, however, are not always human-relevant. By using a genomics-based approach with machine-learning technology, GARD combines the simplicity of in vitro methods and the biological intricacy of in vivo models.
This holistic approach contributes to improved accuracy and human relevance. For example, the predictive accuracy of animal tests for skin sensitization assessment is estimated from 70% to 75%; the traditional in vitro tests are ranging from 75% to 80%, while GARDskin has a predictive accuracy of over 90%.