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Predictive Toxicology |
InterAction Meeting Session, Bryn Mawr, Philadelphia, USA
Friday 17 October 2008
chaired by Artem Cherkasov (University of British Columbia) Bio....
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Current advances in computer-based predictive toxicology offer the potential to create more advanced environments for the screening and prediction of safety issues due to chemical and drug adverse side effects, drug-drug and chemical-system interactions, and chemical and drug toxicologies. Advances in this growing field also offer the potential to replace or reduce the need for animal testing. Acceleration of progress in practical applications requires the creation of interoperable knowledge environments, data sharing and integration, algorithm development, extensive validation and testing, and a significant and structured collaboration approach between organizations, investigators, initiatives and sectors.
In this Predictive Toxicology session recent developments and perspectives will be presented and discussed by a group of leading investigators in this field.
The session will be preceded the evening of the 16 October by a Knowledge Café to discuss Collaboration Opportunities in Predictive ADME & Predictive Toxicology.
Presenters & Discussions Leaders
Curt Breneman (RPI), A Hard Look at Predictive Modeling: How Much Data is Enough? Abstract & Bio....
Artem Cherkasov (University of British Columbia), The Use of Conventional Drug Design Technologies for Identification of Potential Endocrine Disruptors Interacting with Sex-Hormone Binding Globulin in Zebra Fish Abstract & Bio....
Barry Hardy (Douglas Connect), The OpenTox Predictive Toxicology Framework Abstract & Bio....
Andreas Maunz (Freiburg Center for Data Analysis and Modelling), New Lazar Developments and Data Mining Techniques for the Identification of Structural Alerts Abstract & Bio....
Ann Richard (US EPA), EPA DSSTox and ToxCast Project Updates: Generating New Data and Linkages in Support of Public Toxico-Cheminformatics Efforts
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Weida Tong (FDA), The FDA’s Endocrine Disruptor Knowledge Base (EDKB)– Lessons Learned in QSAR Modeling and Applications
Abstract & Bio....
Alex Tropsha (UNC), Predictive Chemical Toxicity Models using in vitro - in vivo Correlations Enriched by Cheminformatics Abstract & Bio....
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