Construction of a High-precision Chemical Prediction System Using Human ESCs
| Autor: | Tsuyoshi Kato, Seiichiroh Ohsako, Wataru Fujibuchi, Satoshi Imanishi, Hiromi Akanuma, Hideko Sone, Reiko Nagano, Junko Yamane, Sachiyo Aburatani |
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| Rok vydání: | 2018 |
| Předmět: |
Pharmacology
Quantitative structure–activity relationship Support Vector Machine business.industry Neurotoxins Gene regulatory network Quantitative Structure-Activity Relationship Pharmaceutical Science Computational biology Prediction system Biology Embryonic stem cell Phenols Toxicity Tests Toxicity Carcinogens Humans Benzhydryl Compounds Stem cell business Embryonic Stem Cells Permethrin Biomedicine Carcinogen |
| Zdroj: | YAKUGAKU ZASSHI. 138:815-822 |
| ISSN: | 1347-5231 0031-6903 |
| DOI: | 10.1248/yakushi.17-00213-2 |
| Popis: | Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells. The data are intended to improve toxicity prediction, per category, of various compounds through the use of support vector machines, and by applying gene networks. The accuracy of our system was 97.5-100% in three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs), and non-genotoxic carcinogens (NGCs). We predicted that two uncategorized compounds (bisphenol-A and permethrin) should be classified as follows: bisphenol-A as a non-genotoxic carcinogen, and permethrin as a neurotoxin. These predictions are supported by recent reports, and as such constitute a good outcome. Our results include two important features: 1) The accuracy of prediction was higher when machine learning was carried out using gene networks and activity, rather than the normal quantitative structure-activity relationship (QSAR); and 2) By using undifferentiated ES cells, the late effect of chemical substances was predicted. From these results, we succeeded in constructing a highly effective and highly accurate system to predict the toxicity of compounds using stem cells. |
| Databáze: | OpenAIRE |
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