Dm. Bagley et al., Assessment of the chorioallantoic membrane vascular assay (CAMVA) in the COLIPA in vitro eye irritation validation study, TOX VITRO, 13(2), 1999, pp. 285-293
The chorioallantoic membrane vascular assay (CAMVA) is an alternative to th
e Draize rabbit eye irritation method. The CAMVA employs the vascularized m
embrane of a fertile hen's egg to assess eye irritation potential. This irr
itation potential is a function of alterations in the vasculature following
the administration of test material. Because of the history of use of the
CAMVA it was selected as one of the methods for a validation study organize
d and sponsored by COLIPA. For this validation study mathematical predictio
n models (PMs) were developed to convert the CAMVA results into predicted D
raize eye irritation scores known as a modified maximum average Draize scor
e (MMAS). These predicted scores were statistically compared with the obser
ved scores to assess the relevance of the CAMVA. The assay was conducted on
the same set of test materials by two independent laboratories. These two
sets of data were compared to assess the interlaboratory reproducibility of
the assay. The results of this validation study of the CAMVA show that for
test materials with MMASs in the 0 to 5 range or the 55 to 110 range, the
CAMVA did not give a good prediction. The predictions were better for sampl
es of mild to moderate irritation (MMAS 5-55). The difficulty in predicting
at the low end of the irritation scale appears to be due to the biological
variability of the test system and the subjective nature of the CAMVA eval
uation. For those samples with an MMAS above 55, the CAMVA appeared to be l
imited in demonstrating the more severe response. This may be due to the fa
ct that the PMs were developed using historical data sets of test materials
with MMASs below this range. Two approaches for improving the CAMVA for ey
e irritation prediction are (1) to decrease the variability at the low end
by reducing the subjectivity in the scoring and (2) to develop better predi
ction models using more data in the range of severe irritants. (C) 1999 Els
evier Science Ltd. All rights reserved.