Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained
| Autor: | Hans-Peter Spirkl, Barber Christopher Gordon, Amanda Giddings, Thierry Hanser, Crina Heghes, Alexis Parenty, Alex Harding, Jonathan D. Vessey, Susanne Glowienke, Alessandro Brigo, Alex Cayley, Alexander Amberg, Sandy Weiner, Ray Kemper, Joerg Wichard, Stephane Werner, Nigel Greene |
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| Rok vydání: | 2015 |
| Předmět: |
0301 basic medicine
DNA Bacterial Quantitative structure–activity relationship Relative wealth Databases Factual Computer science Quantitative Structure-Activity Relationship 010501 environmental sciences Prediction system Toxicology computer.software_genre 01 natural sciences Risk Assessment Interpretation (model theory) Decision Support Techniques 03 medical and health sciences Software Statistics Animals Humans 0105 earth and related environmental sciences Models Statistical business.industry Mutagenicity Tests Reproducibility of Results General Medicine Chemical space Test (assessment) 030104 developmental biology Mutagenesis Mutation Data mining business computer Algorithms Test data |
| Zdroj: | Regulatory toxicology and pharmacology : RTP. 76 |
| ISSN: | 1096-0295 |
| Popis: | The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound. |
| Databáze: | OpenAIRE |
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