Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology

• Published in: Toxicological sciences Vol. 174; no. 1; pp. 38 - 50
• Main Authors: Ruiz, Patricia, Emond, Claude, McLanahan, Eva D, Joshi-Barr, Shivanjali, Mumtaz, Moiz
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01-03-2020
• Subjects: PBPK; VOCs; toxicogenomics; computational systems biology; enrichment analysis
• ISSN: 1096-6080; 1096-0929
• DOI: 10.1093/toxsci/kfz243

Abstract
Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures’ toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.