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Abstract
We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.
Highlights
The 2007 report by the National Research Council (NRC) of the U.S National Academies of Science, titled “Toxicity testing in the 21st Century: A Vision and A Strategy” (NAS/NRC, 2007), laid out a new path forward for the field of toxicology, envisioning an approach where most toxicity testing will be carried out in vitro, with a gradual reduction of reliance on high-dose animal studies
A key element of the proposed approach is the use of computational systems biology models as a tool to generate hypotheses about cellular level dose-response based on existing data sets, and to identify data and knowledge gaps that can help guide the design of in vitro assays, focused animal studies, and improved in vitro – in vivo extrapolation (IVIVE) methods
We lay out the steps toward developing a multi-scale model, where the lobule Agent-based modeling (ABM) is coupled with an intra-hepatocyte ordinary differential equations (ODEs)-based kinetic model of aryl hydrocarbon receptor (AhR) pathway activation for dose-response prediction with tetrachloro dibenzo-p-dioxin (TCDD) or other chemicals (Figures 5A–C)
Summary
The 2007 report by the National Research Council (NRC) of the U.S National Academies of Science, titled “Toxicity testing in the 21st Century: A Vision and A Strategy” (NAS/NRC, 2007), laid out a new path forward for the field of toxicology, envisioning an approach where most toxicity testing will be carried out in vitro, with a gradual reduction of reliance on high-dose animal studies. Distinct from biological variability, can cause uncertainties in prediction from a computational model (Vanlier et al, 2012) This ODE-based approach does not take into account either spatial diffusion or noise in gene expression, it is a valuable computational tool that has provided many insights into the design and function of molecular circuits underlying a number of biological processes like cell cycle regulation, signal transduction, cell differentiation, stress response, and biological rhythms (Carrier et al, 1995; Bhalla et al, 2002; Forger and Peskin, 2003; Novak and Tyson, 2003; El-Samad and Khammash, 2006; Bhattacharya et al, 2010). For chemicals that exhibit developmental toxicity, a Boolean network model can be used to predict low-dose effects based on high-throughput screening, allowing comparison of gene expression profiles between the undisturbed and disrupted states of the transcriptional regulatory network (Jack et al, 2011) These various modeling techniques are based on a topological representation of cellular signaling networks, and ignore the spatial relationship among intracellular molecular species and the spatial heterogeneity inside a cell. As such they represent an extension of traditional compartmental models to the scale of individual cells in a tissue (Shah and Wambaugh, 2010)
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