Using in vitro prediction models instead of the rabbit eye irritation test to classify and label new chemicals: a post hoc data analysis of the international EC/HO validation study

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Item Type:	Article
Title:	Using in vitro prediction models instead of the rabbit eye irritation test to classify and label new chemicals: a post hoc data analysis of the international EC/HO validation study
Creators Name:	Moldenhauer, F.
Abstract:	The international validation study on alternative methods to replace the Draize rabbit eye irritation test, funded by the European Commission (EC) and the British Home Office (HO), took place during 1992-1994, and the results were published in 1995. The results of this EC/HO study are analysed by employing discriminant analysis, taking into account the classification of the in vivo data into eye irritation classes A (risk of serious damage to eyes), B (irritating to eyes) and NI (non-irritant). A data set for 59 test items was analysed, together with three subsets: surfactants, water-soluble chemicals, and water-insoluble chemicals. The new statistical methods of feature selection and estimation of the discriminant function's classification error were used. Normal distributed random numbers were added to the mean values of each in vitro endpoint, depending on the observed standard deviations. Thereafter, the reclassification error of the random observations was estimated by applying the fixed function of the mean values. Moreover, the leaving-one-out cross-classification method was applied to this random data set. Subsequently, random data were generated r times (for example, r = 1000) for a feature combination. Eighteen features were investigated in nine in vitro test systems to predict the effects of a chemical in the rabbit eye. 72.5% of the chemicals in the undivided sample were correctly classified when applying the in vitro endpoints IgNRU of the neutral red uptake test and IgBCOPo5 of the bovine opacity and permeability test. The accuracy increased to 80.9% when six in vitro features were used, and the sample was subdivided. The subset of surfactants was correctly classified in more than 90% of cases, which is an excellent performance.
Keywords:	All Possible Feature Combinations, Classification Error Estimation, Draize Eye Test, In vitro Prediction Models, Linear discriminant Analysis, New Chemicals, Normal Distributed Random Numbers, OECD Rabbit Eye Irritation Guideline, Animals, Rabbits
Source:	Alternatives to Laboratory Animals
ISSN:	0261-1929
Volume:	31
Number:	1
Page Range:	31-46
Date:	January 2003
Official Publication:	https://doi.org/10.1177/026119290303100105
PubMed:	View item in PubMed

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