18th European Symposium on Quantitative Structure – Activity by n a

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Comesaña, G and Pastor M. J. Chem Inf Mod. 2008, 48(9), 1813-1823 Rhodes, September 19-24, 2010 57 ORAL PRESENTATION 5 IDENTIFICATION AND APPLICATION OF ANTITARGET ACTIVITY HOTSPOTS TO GUIDE COMPOUND OPTIMIZATION Gerhard Hessler, Clemens Giegerich, Friedemann Schmidt, Li-hsing Wang, Karl-Heinz Baringhaus LGCR Structure, Design and Informatics, Building G838, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, 65926 Frankfurt, Germany The similarity principle has driven Medicinal Chemistry for more than 100 years.

They are 1) the presence of a substructure that will yield a chemically reactive species upon biotransformation 2) the possibility that a metabolic event will actually occur in that particular substructure such that the reactive species will be formed in that molecule and 3) that the metabolic rate of the molecule will be such that enough of the reactive species will be formed to pose a risk. This combination of factors makes the use of a simple QSAR model impractical. Instead we have employed a weight-of-evidence approach to the problem of reactive metabolite risk assessment by the combination of computational tools to address the above factors individually and then combined them to assess, using actual reactive metabolite screening data, the conditions dictated by the individual tools that will indicate high and low risk for reactive metabolite formation.

Assessment of hazardous substructures is by simple SMARTS matching using a database of examples from literature and from in-house data. The probability of biotransformation within that substructure to yield a reactive species is preformed using the site-of-metabolism tool MetaPrint2D. Finally, the rate of metabolism is assessed in a number of ways, including physico-chemical properties, rate-ofmetabolism QSARs or actual biotransformation rate data. The results of the risk assessment validation against glutathione trapping data suggests a set of conditions that if met, predict positive and negative glutathione trapping results with greater than 80% accuracy.

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