Structure-Activity Databases for Predictive Toxicology: Current Progress and Future Directions
Thursday, May 11, 2006
Presented by the Predictive Toxicology Discussion Group
Organizers: Ray Kemper, Boehringer-Ingelheim; and Donald Halbert, Iconix Pharmaceuticals
Safety liabilities represent the single largest cause of drug failure, accounting for 30 to 40 percent of preclinical attrition. One of the primary approaches currently being applied to decrease attrition of new drugs is computational structure-toxicity ((Q)STR) modeling for prediction of potential adverse effects. Availability of high quality structure-toxicity databases and the informatics tools to efficiently mine and visualize these data are two of the most important factors in successful (Q)STR development. Establishment of corporate structure-toxicity databases allows companies to make more effective use of their institutional knowledge to guide development of safer products, while open databases such as DSSTox provide an easily accessible data source for (Q)STR researchers in the public arena.
The objective of this symposium is to discuss the role of structure-toxicity databases and data mining in predictive strategies to decrease drug attrition and support the entry of safer drugs and chemicals into the marketplace. We will present the current state-of-the-art for structure-toxicity databases and draw upon cross-disciplinary expertise to consider the future of STR database development. The symposium will feature a panel of speakers composed of leaders in database development and chemoinformatics from the private and public sectors.
1:00 pm – 5:00 pm
Nigel Greene, Pfizer, "Using Structure-based Approaches for Hazard Identification and Risk Assessment."
Daniel Benz, CDER, FDA, US FDA, "Toxicological and Clinical Databases, and Computational Toxicology."
May Lee, Iconix Pharmaceuticals, "Mining of a Toxicogenomics Reference Database for (Q)STR in Gene Expression Space."
Christoph Helma, University of Freiburg, Germany, "Lazy-Structure-Activity-Relationships (LAZAR): A Data Mining Tool for the In-Silico Prediction of Chemical Toxicity."
Sean Ekins, ACT LLC, "Combining Quantitative Structure Activity Relationships and Systems Biology Approaches for Predicting Toxicity."
Philip Bentley, Novartis, "Topic to be announced."
"Using Structure-based Approaches for Hazard Identification and Risk Assessment"
In today's social and economic climate there is a pressing need to ensure the safety of new medicines whilst still maintaining a steady flow of new and more effective products. Often initial assessments have to be made in the absence of high-quality toxicology data and generating this data would take many years and millions of dollars for each compound under review. Structure-based approaches to hazard identification and risk assessment offer significant advantages for industry because they are very fast and cheap to run once they have been successfully implemented. They also offer a highly attractive public relations solution in view of the increasing demands to refine, reduce or replace animals in laboratory experiments. However, questions still exist about the ability of structure-based approaches to accurately distinguish between toxic and non-toxic molecules and their effectiveness in ensuring public safety. This presentation will highlight recent experiences in the practical application of structure-based hazard identification and risk assessment within a pharmaceutical company environment.
"US FDA Toxicological and Clinical Databases, and Computational Toxicology"
The US Food and Drug Administration, Center for Drug Evaluation and Research, Informatics and Computational Safety Analysis Staff (ICSAS) is an applied regulatory research unit that creates toxicological and clinical databases, develops rules for quantifying toxicological and clinical endpoints, evaluat