The Journal of Toxicological Sciences
Online ISSN : 1880-3989
Print ISSN : 0388-1350
Original Article
Predictive performance of the human Cell Line Activation Test (h-CLAT) for lipophilic chemicals with high octanol-water partition coefficients
Osamu TakenouchiMasaaki MiyazawaKazutoshi SaitoTakao AshikagaHitoshi Sakaguchi
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2013 Volume 38 Issue 4 Pages 599-609


To meet the urgent need for a reliable alternative test for predicting skin sensitizing potential of many chemicals, we have developed a cell-based in vitro test, human Cell Line Activation Test (h-CLAT). However, the predictive performance for lipophilic chemicals in the h-CLAT still remains relatively unknown. Moreover, it’s suggested that low water solubility of chemicals might induce false negative outcomes. Thus, in this study, we tested relatively low water soluble 37 chemicals with log Kow values above and below 3.5 in the h-CLAT. The small-scale assessment resulted in nine false negative outcomes for chemicals with log Kow values greater than 3.5. We then created a dataset of 143 chemicals by combining the existing dataset of 106 chemicals and examined the predictive performance of the h-CLAT for chemicals with a log Kow of less than 3.5; a total of 112 chemicals from the 143 chemicals in the dataset. The sensitivity and overall accuracy for the 143 chemicals were 83% and 80%, respectively. In contrast, sensitivity and overall accuracy for the 112 chemicals with log Kow values below 3.5 improved to 94% and 88%, respectively. These data suggested that the h-CLAT could successfully detect sensitizers with log Kow values up to 3.5. When chemicals with log Kow values greater than 3.5 that were deemed positive by h-CLAT were included with the 112 chemicals, the sensitivity and accuracy in terms of the resulting applicable 128 chemicals out of the 143 chemicals became 95% and 88%, respectively. The use of log Kow values gave the h-CLAT a higher predictive performance. Our results demonstrated that the h-CLAT could predict sensitizing potential of various chemicals, which contain lipophilic chemicals using a large-scale chemical dataset.

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© 2013 The Japanese Society of Toxicology
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