Development of a Partial Least Squares-Based Integrated Addition Model for Predicting Mixture Toxicity

Title
Development of a Partial Least Squares-Based Integrated Addition Model for Predicting Mixture Toxicity
Authors
Jongwoon KimSanghun KimGabriele E. Schaumann
Keywords
mixture toxicity; partial least squares; integrated addition model; concentration addition; independent action
Issue Date
2014-01
Publisher
Human and ecological risk assessment : HERA
Citation
VOL 20, NO 1, 174-200
Abstract
Concentration addition (CA) and independent action (IA) models are often applied to estimate the mixture toxicity of similarly and dissimilarly acting chemicals, respectively. An integrated addition model (IAM), called the “integrated CA with IA based on a multiple linear regression (ICIM) model” was recently proposed for predicting additive toxicity of non-interactive mixtures regardless of whether mixture components produce similar, dissimilar, or both similar and dissimilar modes of action. In the ICIM, the effective concentrations of mixtures experimentally tested were regarded as the response variable, and the results estimated by CA and IA were considered as the predictor variable. However, it can be highlighted that the multicollinearity problem (i.e., a linear relationship between predictor variables), which may be caused in the existing ICIM model employing ordinary least squares regression. Therefore, the objectives of this study were to develop and evaluate a Partial Least Squares-based IAM (PLS-IAM) not only to overcome the multicollinearity problem, but also to combine the CA and IA into an IAM using the latent variable that accounts for most of the variation in the response. Through four test datasets, this study showed that the PLS-IAM overall outperformed the other reference models, including the CA, IA, and ICIM models.
URI
http://pubs.kist.re.kr/handle/201004/47188
ISSN
10807039
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KIST Publication > Article
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