Target Organ Toxicity Prediction
While a wealth of target organ in vivo toxicity data are available from industry and academia, unfortunately using those data in the generation of accurate predictive models is very challenging even with substantial in vitro data supplementation. One efficient strategy is to use CoRE™’s active learning to rapidly build and evaluate accurate predictive models for many subsets of the training data across many distinct in vivo target organ toxicity endpoints. Then CoRE™ will help to prioritize compounds by both how safe they are predicted to be in the target organ modeled as well as how informative the results are predicted to be for the models.
- Improved predictive accuracy from efficiently identifying predictive in vitro assays
- Use of the most predictive models for in silico prioritization of pre-in vivo safety screening
- For scheduled for in vivo studies, use the predictive models to identify toxicities that should be monitored