Computational expert systems provide an inexpensive and fast alternative to short term genotoxicity assays such as the Ames test. Validation studies show the predictive capability of the MCASE system is about 85 percent. That is, 85 percent concordance is expected between experiment and computational genotoxicity predictions for new chemicals. The strong correlation between chemical structure and genotoxicity is particularly useful for 'in silico' prescreening of new drugs in the pharmaceutical industry. The new Salmonella database modules being developed in this work will be made available online to the public through the InfoTox web site (www.l-tox.com). Additionally, NIH grantees will be allowed unlimited access to the Salmonella modules through InfoTox at no cost. Collaboration will be sought with large drug companies, with mutual exchange of data. Thus the databases will evolve and improve over time as new data are submitted to form a centralized pool of mutagenicity data, that will provide a resource for avoiding unneeded testing of chemicals structurally similarly to those that are already thoroughly understood. Our collaborators at the FDA/CDER will lead the effort to build this industrial consortium.
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| Klopman, Gilles; Chakravarti, Suman K; Zhu, Hao et al. (2004) ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases. J Chem Inf Comput Sci 44:704-15 |
| Klopman, G; Zhu, H; Fuller, M A et al. (2004) Searching for an enhanced predictive tool for mutagenicity. SAR QSAR Environ Res 15:251-63 |
| Klopman, G; Chakravarti, S K; Harris, N et al. (2003) In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system. SAR QSAR Environ Res 14:165-80 |
