HOME- Bryn Mawr Conference
- Workshops & Training
- 2010 Oxford (Discovery)
- 2010 Oxford (ADMET)
- Hardy, B
- Hardy, L
- Jeliazkova, N
- Judson, R
- Leahy, D
- Li, J
- Myatt, G
- Rydberg, P
- Tsaioun, K
- Wiseman, J
- Bursary Award
- Location
- 2009 Oxford (Discovery)
- 2009 Oxford (ADMET)
- 2008 Oxford
- Program
- Exhibition
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| About Jeffrey S. Wiseman, Ph.D. (CEO and Co-Founder, Pharmatrope, Ltd.) |
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Dr. Jeffrey Wiseman has 30 years of experience in Discovery Research in the pharmaceutical industry. Along with Dr. Matthew Clark, he co-founded Pharmatrope in 2009 as a Cheminformatics software company addressing core unmet needs in small molecule ligand design. Before this, he led efforts at Locus Pharmaceuticals for five years to develop computational drug design software for the accurate and rapid prediction of ligand binding affinities. This followed service as Global Vice President of Cheminformatics at GlaxoSmithKline where his division created the informatics capability for the industrialization of high-throughput screening and chemistry. Prior to building the Cheminformatics division, Dr. Wiseman led multiple positions at Glaxo Wellcome, including service on the global Research Executive council and leadership of Biochemistry, Structural Biology, and Molecular Biology at the RTP site. Dr. Wiseman holds a degree in Chemistry from Harvard University and trained as a Postdoctoral Research Fellow in Enzymology at Stanford and Brandeis Universities.
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Human Adverse Event Data Mining
Jeffrey S. Wiseman (Pharmatrope)
The FDA has made a concerted effort over the last decade to systematize the recording of adverse event data and enhance its utility for data mining and drug design. Pharmatrope’s new Titanium™ database condenses and filters the FDA data to report the 100,000 drug-adverse event associations that have been identified as statistically significant from a total of 8,000,000 drug-adverse event relationships reported for the 1800 marketed drugs. The database has been applied to construct fragment-based QSAR models for 650 adverse events across the full spectrum of toxicity classes.
These models are tuned for maximum utility in drug discovery by minimizing the prediction of false positives. This is accomplished by removal of random noise from the underlying data. This tuning is also applied to identify significant adverse event-adverse event links based on their common interactions with drugs. This data-driven clustering of event-event relations maximizes the signal that relates chemical substructure to adverse events and allows us to begin distinguishing adverse events that are linked to off-target activities as opposed to the primary pharmacological activity of the drugs.
For the Oxford workshop we will describe the rationale underlying the construction of the Titanium system and provide examples of its application. We will then provide a walk-through of representative applications to structure-based prediction of toxicity and to investigation of underlying mechanisms of toxicity. The walk-through will include demonstrations of the integration of Titanium with commonly available data mining tools. With this background Titanium will be made generally available for application to the predictive toxicology case study work of the workshop.
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