A novel computational solution to the health risk assessment of air pollution via joint toxicity prediction: A case study on selected PAH binary mixtures in particulate matters

Abstract

Regional haze episode has already caused overwhelming public concern. Unraveling the health effects of the representative composition mixtures of atmospheric fine particulate matters (PM_2.5) becomes a top priority. In this study, a novel computational solution integrating chemical-induced genomic residual effect prediction with in vitro-based risk assessment is proposed to obtain the cumulative health risk of typical chemical mixtures of particulate matters (PM). The joint toxicity of binary mixtures is estimated by analyzing both genomic similarity and dose-response curve of relevant pollutants for the chemical-induced genomic residual effect. Specifically, the modified relative potency factor (mRPF) of mixtures is introduced for this purpose, and the ratio of activation (RA) value is defined to assess the corresponding health risks of the mixtures. As a methodology demonstration, the health risk of typical binary polycyclic aromatic hydrocarbon (PAH) mixtures in PM, containing Benzo[a]pyrene (BaP) as a component, is assessed using the proposed solution. Our results indicate that the combined effect of pairwise PAHs of BaP with Benzo[b]fluoranthene (BbF) and Benz[a]anthracene (BaA) is synergistic on p53 pathway, and that the health risk of the such mixtures increases compared to that of the individual ones. Obviously, the cumulative health risk of environmental mixtures will be underestimated when the synergistic effect is wrongly assumed to be additive. To our knowledge, this is the first study ever report on a computational solution to the health risk assessment of environmental pollution via joint toxicity prediction. The novel methodology proposed here makes full use of the open-access in vitro assay data and transcriptomic information in literatures and provides a successful demonstration of the concept of systems biology and translational science.

Keywords: Atmospheric particulate matters; Binary mixture; Computational solution; Health risk; Joint toxicity; Polycyclic aromatic hydrocarbons.