Refine
Keywords
- Predictive Model (1) (remove)
Studies on the toxicity of chemical mixtures find that components at levels below no-observed-effect concentrations (NOECs) may cause toxicity resulting from the combined effects of mixed chemicals. However, chemical risk assessment frequently focuses on individual chemical substances, although most living organisms are substantially exposed to chemical mixtures rather than single substances. The concepts of additive toxicity, concentration addition (CA), and independent action (IA) models are often applied to predict the mixture toxicity of similarly and dissimilarly acting chemicals, respectively. However, living organisms and the environment may be exposed to both types of chemicals at the same time and location. In addition, experimental acquisition of toxicity data for every conceivable mixture is unfeasible since the number of chemical combinations is extremely large. Therefore, an integrated model to predict mixture toxicity on the basis of single mixture components having various modes of toxic action (MoAs) needs to be developed. The objectives of the present study were to analyze the challenges in predicting mixture toxicity in the environment, and to develop integrated models that overcome the limitations of the existing prediction models for estimating the toxicity of non-interactive mixtures through computational models. For these goals, four sub-topics were generated in this study. Firstly, applicable domains and limitations of existing integrated models were analyzed and grouped into three kinds of categories in this study. There are current approaches used to assess mixture toxicity; however, there is a need for a new research concept to overcome challenges associated with such approaches, which recent studies have addressed. These approaches are discussed with particular emphasis on those studies involved in computational approaches to predict the toxicity of chemical mixtures based on the toxicological data of individual chemicals. Secondly, through a case study and a computational simulation, it was found that the Key Critical Component (KCC) and Composite Reciprocal (CR) methods (as described in the European Union (EU) draft technical guidance notes for calculating the Predicted No Effect Concentration (PNEC) and Derived No Effect Level (DNEL) of mixtures) could derive significantly different results. As the third and fourth sub-topics of this study, the following two integrated addition models were developed and successfully applied to overcome the inherent limitations of the CA and IA models, which could be theoretically used for either similarly or dissimilarly acting chemicals: i) a Partial Least Squares-Based Integrated Addition Model (PLS-IAM), and, ii) a Quantitative Structure-Activity Relationship-Based Two-Stage Prediction (QSAR-TSP) model. In this study, it was shown that the PLS-IAM might be useful to estimate mixture toxicity when the toxicity data of similar mixtures having the same compositions were available. In the case of the QSAR-TSP model, it showed the potential to overcome the critical limitation of the conventional TSP model, which requires knowledge of the MoAs for all chemicals. Therefore, this study presented good potential for the advanced integrated models (e.g., PLS-IAM and QSAR-TSP), while considering various non-interactive constituents that have different MoAs in order to increase the reliance of conventional models and simplify the procedure for risk assessment of mixtures.