Cost-effective experimental design to support modeling of concentration-response functions

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State: Public
Version: author
Serval ID
serval:BIB_A1BE21C63107
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Cost-effective experimental design to support modeling of concentration-response functions
Journal
Chemosphere
Author(s)
Chèvre N., Brazzale A.R.
ISSN
0045-6535 (Print)
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
72
Pages
803-810
Language
english
Notes
DA - 20080530 LA - eng RN - 0 (Environmental Pollutants) RN - 0 (Pesticides) RN - 51-28-5 (2,4-Dinitrophenol) SB - IM
Abstract
Modeling concentration-response function became extremely popular in ecotoxicology during the last decade. Indeed, modeling allows determining the total response pattern of a given substance. However, reliable modeling is consuming in term of data, which is in contradiction with the current trend in ecotoxicology, which aims to reduce, for cost and ethical reasons, the number of data produced during an experiment. It is therefore crucial to determine experimental design in a cost-effective manner. In this paper, we propose to use the theory of locally D-optimal designs to determine the set of concentrations to be tested so that the parameters of the concentration-response function can be estimated with high precision. We illustrated this approach by determining the locally D-optimal designs to estimate the toxicity of the herbicide dinoseb on daphnids and algae. The results show that the number of concentrations to be tested is often equal to the number of parameters and often related to the their meaning, i.e. they are located close to the parameters. Furthermore, the results show that the locally D-optimal design often has the minimal number of support points and is not much sensitive to small changes in nominal values of the parameters. In order to reduce the experimental cost and the use of test organisms, especially in case of long-term studies, reliable nominal values may therefore be fixed based on prior knowledge and literature research instead of on preliminary experiments
Keywords
2,4-Dinitrophenol , Algae , Algorithms , analysis , Animals , Cost-Benefit Analysis , Daphnia , drug effects , Environmental Pollutants , Growth , Likelihood Functions , Logistic Models , Models,Statistical , Nonlinear Dynamics , Pesticides , Regression Analysis , Reproduction , Research Design , statistics & numerical data , Survival , Switzerland , toxicity
Create date
29/01/2009 23:13
Last modification date
20/08/2019 16:07
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