11 - 15 March 2018
2018 - Society of Toxicology 57th Annual Meeting and ToxExpo
Skin sensitisation: What place does in silico have within a defined approach?
Testing strategies incorporating in silico predictions can provide additional value by considering factors such as metabolism, lipophilicity and chemical reactivity. Lhasa Limited will describe a defined approach integrating Derek Nexus with non-animal assays to reliably predict skin sensitisation, and will compare its performance to both human and animal data.
Ames Test Strain Profile: Implications for Mutagenicity Predictions and ICH M7
Recent discussions around the bacterial strains used for the Ames test may impact both the development of in silico models (e.g. training sets) and the interpretation of predictions. These topics will be discussed and new features in Sarah Nexus to assist with expert review will be demonstrated.
A Defined Approach to Skin Sensitization Using Derek Nexus and Non-Animal Assays
In Silico Prediction of DILI: Extraction of Histopathology Data from Preclinical Toxicity Studies of the eTOX Database for New In Silico Models of Hepatotoxicity
Alexander Amberg, Sanofi (Lilia Fisk - Co-Author)
Derek Nexus and the Prediction of Human Skin Sensitization Potential: An Evaluation
Addressing the Challenge of Making Negative Predictions for Skin Sensitisation
Strain Profiles: Moving Beyond Binary Ames Classification
Headline Event (shown first above featured)
- A Defined Approach to Skin Sensitization Using Derek Nexus and Non-Animal Assays
- Addressing the Challenge of Making Negative Predictions for Skin Sensitisation
- Derek Nexus and the Prediction of Human Skin Sensitisation Potential: An Evaluation
- In Silico Prediction of DILI - Extraction of Histopathology Data from Preclinical Toxicity Data Studies of the eTOX Database for new In SIlico Models of Hepatotoxicity
- Leveraging Strain Information In Sarah Nexus Predictions
- Skin sensitisation - What place does in silico have within a defined approach?
- Strain profiles: Moving beyond binary Ames classification