New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test
| Autor: | Els Van Hoeck, Giuseppa Raitano, Tamara Vanhaecke, Melissa Van Bossuyt, Masamitsu Honma, Emilio Benfenati, Vera Rogiers, Birgit Mertens |
|---|---|
| Přispěvatelé: | Pharmaceutical and Pharmacological Sciences, Connexin Signalling Research Group, Experimental in vitro toxicology and dermato-cosmetology, Vriendenkring VUB, Experimental Pharmacology |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Quantitative structure–activity relationship In vivo micronucleus Computer science In silico Quantitative Structure-Activity Relationship Toxicology computer.software_genre Models Biological Sensitivity and Specificity Chromosomes Set (abstract data type) 03 medical and health sciences Pharmacology Toxicology and Pharmaceutics(all) 0302 clinical medicine Software Chromosome (genetic algorithm) Chromosome damage In silico model Animals Computer Simulation Micronucleus Tests business.industry QSAR Pharmacology. Therapy General Medicine 030104 developmental biology Test set Micronucleus test Data mining Genotoxicity business Databases Nucleic Acid Model building computer 030217 neurology & neurosurgery |
| Zdroj: | Toxicology letters |
| ISSN: | 0378-4274 |
| Popis: | A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect