S6: Predicting Nanoparticles

How well can the mixture toxicity of engineered nanoparticles be predicted?

S6: Predicting Nanoparticles, OpenTox Asia 2017
PRESENTING AUTHOR: 

Jongwoon Kim 

INSTITUTION / COMPANY : 

KIST Europe Institute 

POSITION: 

Senior Researcher 

ABSTRACT CONTENT / DETAILS: 

Engineered nanoparticles (ENPs) are widely used in different industries and our everyday life due to their notable characteristics. Recent studies have shown that co-exposures to ENPs can provoke the mixture toxicity to living organisms, and the toxicity of ENP mixtures can be higher than that of each ENP.  It might be unfeasible to request toxicity tests for all conceivable mixture products due to the extremely large number of combinatorial complexity. Thus, there is an essential need for developing computational toxicology methods as cost savings, fast and reliable approaches to predict the mixture toxicity. Conventionally, concentration addition (CA) and independent action (IA) models have frequently employed to estimate the additive toxicity of chemical mixtures. The CA and IA models can either assume a target mixture is composed of similarly or dissimilarly acting chemicals, respectively. The CA model is in general recommended as a default method if there is lack of knowledge of modes of toxic action of all mixture components since the model tends to be less data-demanding and provides more conservative results than the IA model. In this study, we investigated the applicability of the CA and IA models for estimating the joint toxicity of ENPs. This study highlights how well the CA and IA models can predict the mixture toxicity of ENPs and future challenges in mixture risk assessment.