THE INTEGRATED USE OF PHYSICOCHEMICALAND IN VITRO DATA FOR PREDICTINGCHEMICAL TOXICITY

Abstract

The general aim of this PhD project was to investigate the integrated use of physicochemical and in vitro data for predicting the toxicological hazard of chemicals in animals. This was achieved in two stages: firstly, by developing two types of model for acute dermal and ocular toxicity - structure-activity relationships (SARs) based on easily calculated physicochemical properties, and prediction models (PMs) based on experimentally derived physicochemical or in vitro data; and secondly, by evaluating the tiered testing approach to hazard classification, in which different classification models (CMs) are applied sequentially before animal testing is conducted. The thesis therefore reports the development and assessment of CMs for skin irritation, skin corrosion and eye irritation, as well as the outcome of simulations in which these models were incorporated into tiered testing strategies for these toxicological endpoints. The results show that the tiered testing approach to hazard classification provides a reliable means of reducing and refining the use of animals, without compromising the ability to classify chemicals. In addition to developing the above-mentioned CMs, regression models for corneal permeability were developed, and the relationship between corneal permeability and eye irritation was investigated. The thesis also describes the development and assessment of a novel statistical method called embedded cluster modelling (ECM), which generates elliptic models of biological activity from embedded data sets. The combined use of this method with the existing method of cluster significance analysis (CSA) is illustrated through the development of SARs for eye irritation potential. Finally, novel applications of the bootstrap resampling method were investigated. In particular, algorithms based on this method are shown to provide a means of assessing: a) the minimal variability associated with the Draize rabbit tests for skin and eye irritation; and b) the variability in Cooper statistics (commonly used to summarise the performance of two-group CMs) that arises from chemical variation.