Posted January 12, 2010
Computational predictive toxicology is based on the implementation of (Q)STR (quantitative structure-toxicity relationship) databases. These databases are set up to store and retrieve information about the structure and related toxic properties of countless chemical compounds. They contain libraries of known molecules, their structural elements, and related binding behaviors. If a new drug candidate has regions that are structurally similar to a known molecule, the two substances are likely to bind in similar ways and have similar functional effects.
At the May 11, 2006, symposium of the Predictive Toxicology Discussion Group, a panel of leading experts from pharmaceutical firms, academic institutions, and government agencies gathered to trade information about (Q)STR databases.The databases—in both public and private sectors—are being used to predict the potential carcinogenic and mutagenic properties of new drug candidates by carefully assessing their structural and physicochemical properties.
Use the tabs above to find a meeting report and multimedia from this event.
Developed by the LHASA group at Harvard University, this software application enables toxicology prediction over the Internet. An online demo is available.
Iconix Pharmaceuticals' toxicogenomics research tool.
Informatics and Computational Safety Analysis Staff (ICSAS)
Home of the U.S. Food and Drug Administration's effort to create a database of adverse effects.
A chemoinformatics platform for drug discovery.
A new online drug safety product published by Elsevier that gives users access to FDA preclinical, clinical, and post-market safety data.
A supplement to Christoph Helma's book Predictive Toxicology that covers in silico techniques for the prediction of toxic properties. These pages provide information about predictive toxicology programs, links to toxicity data, and access to the prediction system LAZAR. Also visit Helma's consulting firm, In Silico.
The National Library of Medicine's databases on toxicology, hazardous chemicals, environmental health, and toxic diseases.
A database that can recognize and search for similarities in chemical structures used in (Quantitative) Structure-Activity Relationship (QSAR) modelling.
Ekins, S. & B. Wang, Eds. 2006. Computer Applications in Pharmaceutical Research and Development. Wiley, Hoboken, NJ.
Helma, C., Ed. 2005. Predictive Toxicology. CRC Press, Boca Raton, FL.
Aptula, A. O. & M. T. Cronin. 2004. Prediction of hERG K+ blocking potency: application of structural knowledge. SAR QSAR Environ. Res. 15: 399-411.
Aronov, A. M. & B. B. Goldman. 2004. A model for identifying HERG K+ channel blockers. Bioorg. Med. Chem. 12: 2307-2315.
Bains, W., A. Basman & C. White. 2004. HERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study. Prog. Biophys. Mol. Biol. 86: 205-233.
Balakin, K. V., Y. A. Ivanenkov, N. P. Savchuk, et al. 2005. Comprehensive computational assessment of ADME properties using mapping techniques. Curr. Drug Disov. Technol. 2: 99-113.
Crumb, W. J., S. Ekins, R. D. Sarazan, et al. 2006. Effects of antipsychotic drugs on Ito, INa, Isus, IKi and hERG: QT prolongation, structure activity relationship and network analysis. Pharm. Res. 23: 1133-1143.
Dobo, K. L., N. Greene, M. O. Cyr, et al. The application of structure-based assessment to support safety and chemistry diligence to manage genotoxic impurities in active pharmaceutical ingredients during drug development. Regul. Toxicol. Pharmacol. 44: 382-393.
Ekins, S. 2006. Systems ADME/Tox: resources and approaches. J. Pharm. Tox. Methods 53: 38-66.
Ekins, S., E. Kirillov, E. A. Rakhmatulin & T. Nikolskaya. 2005. A novel method for visualizing nuclear hormone receptor networks relevant to drug metabolism. Drug Metab. Dispos. 33: 474-481. FULL TEXT
Ekins, S., S. Andreyev, A. Ryabov, et al. 2006. A combined approach to drug metabolism and toxicity assessment. Drug Metab. Dispos. 34: 495-503.
Ekins, S., S. Andreyev, A. Ryabov, et al. 2005. Computational prediction of human drug metabolism. Expert Opin. Drug Metab. Toxicol. 1: 303-324.
Ekins, S., Y. Nikolsky, A. Bugrim, et al. 2006. Pathway mapping tools for analysis of high content data. In D. L. Taylor, J. A. Haskins, & K. A. Giuliano, High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery. The Humana Press, Totowa, NJ.
Ekins, S., Y. Nikolsky & T. Nikolskaya. 2005. Techniques: application of systems biology to absorption, distribution, metabolism, excretion, and toxicity. Trends Pharmacol. Sci. 26: 202-209.
Keseru, G. M. 2003. Prediction of hERG potassium channel affinity by traditional and hologram qSAR methods. Bioorg. Med. Chem. Lett. 13: 2773-2775.
Mitcheson, J. S., J. Chen, M. Lin, et al. 2000. A structural basis for drug-induced long QT syndrome. Proc. Natl. Acad. Sci. USA 97: 12329-12333. FULL TEXT
Natsoulis, G., L. El Ghaoui, G. R. Lanckriet, et al. 2005. Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. Genome Res. 15: 724-736. FULL TEXT
Nikolsky, Y., S. Ekins, T. Nikolskaya & A. Bugrim. 2005. A novel method for generation of signature networks as biomarkers from complex high throughput data. Tox. Lett. 158: 20-29.
Pearlstein, R. A., R. J. Vaz, J. Kang, et al. 2003. Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches. Bioorg. Med. Chem. Lett. 13: 1829-1835.
O'Brien. S. E. & M. J. de Groot. 2005. Greater than the sum of its parts: combining models for useful ADMET prediction. J. Med. Chem. 48: 1287-1291.
Rajamani, R., B. A. Tounge, J. Li & C. H. Reynolds. 2005. A two-state homology model of the hERG K+ channel: application to ligand binding. Bioorg. Med. Chem. Lett. 15: 1737-1741.
Swaan, P. W. & S. Ekins. 2005. Reengineering the pharmaceutical industry by crash-testing molecules. Drug Disc. Today 10: 1191-1200.
Tobita, M., T. Nishikawa & R. Nagashima. 2005. A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. Bioorg. Med. Chem. Lett. 15: 2886-2890.
Philip Bentley, PhD
Philip Bentley received his doctorate in biochemistry from the University of Hull in the United Kingdom. He went on to pursue postdoctoral work on the mechanisms of mutagenicity and metabolic activation at the Universities of Basel and Mainz. Bentley joined the experimental toxicology group at CIBA-Geigy and went on to become head of this group, as well as the head of the drug metabolism and preclinical safety groups. With the formation of Novartis, Bentley assumed leadership of the drug metabolism pharmacokinetics group in Europe. In 1997, he moved to the United States to head the U.S. Novartis toxicology group. Since 2005, Bentley has been the global head of safety profiling and assessment at Novartis.
R. Daniel Benz, PhD
Daniel Benz received his undergraduate degree in biology at the Illinois Institute of Technology and his PhD in biophysics from the University of California, Berkeley. Benz went on to do postdoctoral work in genetic toxicology at York University in Toronto. Benz has held academic positions at the University of California, Irvine, and at Southampton College, has worked for the Brookhaven National Laboratory, and has served as consultant for the New York Health Department and the Brookhaven National Laboratory. He joined the U.S. Food and Drug Administration (FDA) in 1987 as a review toxicologist, and has since served as a team leader at the Center for Food Safety and Applied Nutrition (CFSAN). More recently, he has been database manager for the informatics and computational safety assessment staff at the Center for Drug Evaluation and Research (CDER). Benz has published numerous papers in the field of computational toxicology.
Nigel Greene, PhD
Nigel Greene earned his undergraduate degree in chemistry and computational science from the University of Leeds, from which he was later awarded a doctorate in chemistry. He went on to work at Lhasa, Ltd., where he promoted the development and use of the DEREK software package. He went on to work at Tripos, where he was involved in designing the Sybil molecular modeling software suite. In 2001, Greene joined Pfizer's worldwide safety sciences department, where he now heads the global toxico-informatics center of emphasis, which is responsible for developing in silico systems for toxicology, including structure-based approaches, bioinformatics, and systems-biology applications.
Christoph Helma, PhD
Christoph Helma obtained his undergraduate degree and PhD in chemistry and physics from the Technical University of Vienna. He followed this with postdoctoral work in toxicology at the University of Vienna. From 1995 to 2005, he was director of the Machine Learning Laboratory at the Institute of Computer Sciences at the University of Freiburg, and in 2004 founded his own company, In Silico Toxicology. He is the editor of a textbook, Predictive Toxicology, and author of numerous articles on data mining for predictive toxicology.
May Lee, PhD
May Lee received a bachelor's degree in chemistry from the University of British Columbia and a PhD in organic chemistry from the University of Illinois. She did postdoctoral work at Harvard University before joining a microbial fermentation-based natural products discovery program at Lederle Laboratories. Lee joined Microcide Pharmaceuticals in 1994, where she developed and managed the company's molecular diversity program and the high-throughput screening group. In 2001, she moved to Iconix Pharmaceuticals as the director of drug informatics, and has been instrumental in the development of the company's DrugMatrix software.
Sean Ekins, PhD, DSc
Sean Ekins did his graduate training at the University of Aberdeen, where he received a master's degree and PhD in clinical pharmacology and a DSc in Science. He completed postdoctoral studies in the department of drug disposition at Eli Lilly Research Laboratories. Ekins has subsequently held positions in leading pharmaceutical companies, including Pfizer, Eli Lilly, and Concurrent Pharmaceuticals, where he served as the associate director for computational drug discovery. More recently, he served as vice president of computational biology at GeneGo, where he developed software for drug metabolism and toxicity assessment. Ekins has published more than 80 articles and book chapters on in vitro and computational approaches to ADME/Tox, and has recently edited a book on computational applications to pharmaceutical research and development. He is currently editing a second book, Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals. He serves as Vice President of Computational Biology at ACT, LLC and holds an appointment as adjunct associate professor in the school of pharmacy at the University of Maryland.
Kiryn Haslinger is a science writer and editor with a masters in theoretical chemistry. Since working with James D. Watson on his book DNA: The Secret of Life as a research and editorial assistant, she has written freelance articles on science and scientific history.