DEREK database

A number of approaches are available or under development to predict metabolism, including expert systems such as MetabolExpert (Compudrug), Meteor (Lhasa), MetaFore [42] and the databases Metabolite (MDL) and Metabolism (Synopsys) [43]. Ultimately such programs may be linked to computer-aided toxicity prediction based on quantitative structure-toxicity relationships and expert systems for toxicity evaluation such as DEREK (Lhasa) (see also Chapter 8) [44]. 

Zinke, S., Gerner, I., and Schlede, E., Evaluation of a rule base for identifying contact allergens by using a regulatory database comparison of data on chemicals notified in the European union with "structural alerts" used in the DEREK expert system, Alt. Lab. Anim. (ATLA), 30, 285-298, 2002. 

Compared to the evaluation of these data with DEREK, the sensitivity of MultiCASE is in the same range as DEREK but the specificity is lower in MultiCASE compared to DEREK. The reason for this may be that in the MultiCASE evaluation, the prediction was performed against 12 independent modules and the compound was defined as inactive only if no activity was predicted in all modules, while the prediction with DEREK was only against one database. The same is true for the concordance, which was also lower in the MultiCASE evaluation compared to the DEREK... 

In silico tools make a significant contribution to the SAR-based early identification of potential toxicity. An increasing volume of published preclinical and clinical toxicity data are collected and used to build structure-related searchable databases. These expert knowledge databases can analyze chemical structures and match them with potential mechanisms of toxicity. DEREK for Windows (Lhasa Ltd.)39 is one of such broadly used knowledge-based expert systems to provide toxicology alerts for new compounds. Although certainly not comprehensive, numerous efforts have been made to predict hepatotoxicity. Recently,... 

MCASE and DEREK are still in their infancy regarding hERG queries. For both systems their own rules can be developed to create modules that, upon entering substructures, will generate alerts [55], Since most of the larger pharmaceutical companies possess huge internal databases, it is expected that internal data-sharing initiatives will evolve with more searchable tools to complement commercial databases. 

Computational approaches to predicting toxicity are based on expert systems containing empirical or computationally derived rules, typically encoded via stmctural fragments that could potentially cause toxicity. Several databases are available commercially rule-based DEREK and HazardExpert, as well as systems based on automated QSAR—TOPKAT and CASETOX. Computational methods for predicting drug toxicity are reviewed in reference 57. 

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