- 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.
