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| About Alex Tropsha (University of North Carolina) |
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Alexander Tropsha was born in Moscow in 1960. He received his MS in Chemistry from Moscow State University in 1982 and PhD in Biochemistry and Pharmacology in 1986 from the same university. He immigrated to the US in 1989. In 1991, after two years of postdoctoral research at the University of North Carolina at Chapel Hill, he joined the UNC School of Pharmacy as an Assistant Professor and Director of the Laboratory for Molecular Modeling. Dr. Tropsha has since been promoted to the position of full Professor; he also holds position of the Associate Director of the Carolina Center for Genome Sciences.
The major area of Tropsha’s research is Biomolecular Informatics, which implies understanding relationships between structures (organic or macromolecular) and their properties (activity or function). In recent years, his group has developed several important methodologies and software tools for Computer Assisted Drug Design. Concurrently, they have developed a new approach to protein 3D structure analysis and prediction based on the principles of statistical geometry (Delaunay tessellation). This approach affords determination of key structural and sequence motifs responsible for protein function.
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Predictive Chemical Toxicity Models Using in vitro - in vivo Correlations Enriched by Cheminformatics
Hao Zhu (1,2), Ling Ye (2), Ivan Rusyn (1,3), Ann Richard (4), Alexander Golbraikh (2) and Alexander Tropsha (1,2)
(1) Carolina Environmental Bioinformatics Center; (2) Division of Medicinal Chemistry and Natural Products; (3) Department of Environmental Sciences and Engineering, UNC-Chapel Hill, Chapel Hill, NC 27599; and (4) National Center for Computational Toxicology, EPA, RTP, NC 27711
Establishing robust correlations between in vitro and in vivo toxicity of environmental chemicals or drug candidates is critical to increase the efficiency of toxicity testing. In most studied cases, the correlation has been poor or non-existent. We have developed a novel two-step modeling approach to address this challenge. The approach is illustrated with the data from the German Center for the Documentation and Validation of Alternative Methods (ZEBET), that compiled a database including 347 chemicals with experimental in vitro cytotoxicity IC50 and rodent in vivo LD50 values. We found that this dataset can be subdivided into two subsets: first includes compounds with high correlation in LD50 and IC50 values (R2>0.9), and second is comprised of compounds with poor IC50/LD50 correlation (outlier set). The classification QSAR modeling was successful in discriminating the two classes of compounds using MolConnZ chemical descriptors with the classification accuracy as high as 72% for the external validation set. In addition, k nearest neighbor (kNN) QSAR modeling method was applied to the outlier set only and 16 models with R2/Q2>0.5/0.5 cutoff were produced. Overall, the two-step QSAR approach achieved the prediction accuracy of 72% for the external validation set (25 compounds). We conclude that considerable improvements can be achieved in correlating in vitro and in vivo toxicity data when the activity-correlation based pre-clustering is applied prior to development of predictive toxicity models.
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