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MEIC evaluation of acute systemic toxicity: Part VIII. Multivariate partial least squares evaluation, including the selection of a battery of cell line tests with a good prediction of human acute lethal peak blood concentrations for 50 chemicals
Umeå University, Faculty of Science and Technology, Department of Chemistry.
2000 (English)In: Atla-Alternatives to Laboratory Animals, ISSN 0261-1929, Vol. 28, 201-34 p.Article in journal (Refereed) Published
Abstract [en]

The Multicenter Evaluation of In vitro Cytotoxicity (MEIC) programme was set up to evaluate the relevance for human acute toxicity of in vitro cytotoxicity tests. A total of 61 assays were used to test all 50 reference chemicals. The results of all the tests and the human database were presented in the first five papers of this series. An evaluation of the relevance for human acute toxicity of all submitted test results with use of hard linear regression modelling was presented in the next two papers, and demonstrated a high relevance of in vitro tests, notably tests involving human cell lines. In the present study, multivariate partial least square (PLS) modelling with latent variables analysis has been used to reach two objectives. The first objective was to study the prediction of human acute toxicity by the 61 assays. The second objective was to select a practical battery from the 61 assays, with an optimal prediction of lethal blood concentrations from human acute poisonings of the chemicals. A two-component PLS model of all 61 assays predicted three sets of lethal blood concentrations (clinical, forensic and peak concentrations) very well (R-2 = 0.77, 0.76 and 0.83, Q(2) = 0.74, 0.72 and 0.81, respectively), providing correlative evidence for a high relevance for human acute toxicity of most of the assays. The assays with human cells were highly predictive, whereas assays with Very short incubation times and non-fish ecotoxicological assays were least predictive. These findings confirm the previous results from linear regression analysis. To select an optimal battery, 24 successive PLS models of in vitro data were compared with lethal peak concentrations. The battery selection was based on 38 chemicals with reliable and relevant lethal peak concentrations. An initial PLS model of all 61 assays was used to select the 15 most predictive and most distinct assays. Subsequent PLS models were used to measure the decrease in prediction when assays were deleted from the 15-test battery, as well as the increase in prediction when some extra-predictive assays (as identified by the deletion process) were added later to an optimal two-test battery. The most predictive three-test battery (R-2 = 0.79 and Q(2) = 0.78 for all 50 chemicals) included two circumstantial assays. The most predictive and most cost-effective battery consisted of three human cell line assays, with four endpoints and two exposure times, i.e. protein content (24 hours), ATP content (24 hours), inhibition of elongation of cells (24 hours), and pH-change (7 days). This 1, 5, 9, 16 battery exclusively measures basal cytotoxicity, and is highly predictive (R-2 = 0.77 and Q(2) = 0.76 for 50 chemicals) of the actual lethal peak blood concentrations from acute poisonings in humans. The battery prediction compares favourably with the prediction of human lethal dose by a PLS model of rat and mouse 50% lethal dose (LD50) values for the 50 chemicals (R-2 = 0.65 and Q(2) = 0.64). The three assays of the battery and other promising MEIC assays should be formally validated as soon as possible. The battery can be used immediately for several non-regulatory purposes, including the high-throughput screening of potential pharmaceuticals.

Place, publisher, year, edition, pages
2000. Vol. 28, 201-34 p.
Keyword [en]
acute poisoning, alternatives, basal cytotoxicity, battery optimisation, cytotoxicity, evaluation, human toxicity, in vitro tests, lethal blood concentrations, modelling, multiple regression analysis, multivariate PLS analysis, test batteries, toxicity screening, toxicology, validation
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-9146OAI: diva2:148817
Available from: 2008-03-17 Created: 2008-03-17 Last updated: 2013-03-19Bibliographically approved

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