Systems toxicology: from basic research to risk assessment

Abstract

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.

Figure 1

What is Systems Toxicology? Systems Toxicology is aimed at decoding the toxicological blueprint of active substances that interact with living systems. It resides at the intersection of Systems Biology with Toxicology and Chemistry. It integrates classic toxicology approaches with network models and quantitative measurements of molecular and functional changes occurring across multiple levels of biological organization. The multidisciplinary Systems Toxicology approach combines principles of chemistry, computer science, engineering, mathematics, and physics with high-content experimental data obtained at the molecular, cellular, organ, organism, and population levels to characterize and evaluate interactions between potential hazards and the components of a biological system. It is aimed at developing a detailed mechanistic as well as quantitative and dynamic understanding of toxicological processes, permitting prediction and accurate simulation of complex (emergent) adverse outcomes. Thereby, the approach provides a basis for translation between model systems (in vivo and in vitro) and study systems (e.g., human, ecosystem). Systems Toxicology, therefore, has an ultimate potential for extrapolating from early and highly sensitive quantifiable molecular and cellular events to medium- and long-term outcomes at the organism level, and its application could be part of a new paradigm for risk assessment. Artwork by Samantha J. Elmhurst (www.livingart.org.uk).

Figure 2

Steps that define the Systems Toxicology paradigm, from biological network models to dynamic adverse outcome pathway (AOP) models. The development of dynamic AOP models enabling the simulation of the population-level effects of an exposure is the ultimate goal of Systems Toxicology. This development follows three broad steps of maturity from top to bottom. The first level consists of the development of causal computable biological network models that link the system’s interaction of a toxicant with the organ-level responses. Such models can be used to quantify the biological impact of an exposure in the context of quantifiable end points such as histology or physiological measurements. In a second step, as more mechanistic knowledge derived from quantitative measurements accumulates, dynamic models linking the exposure with the organ-level responses can be developed. Ultimately, the third level of maturity is reached when the link between the exposure and the population outcome can be represented by mathematical models that enable the simulation of population-level effects of an exposure. Blue arrows denote causal links, which are mainly derived from correlative studies. Artwork by Samantha J. Elmhurst (www.livingart.org.uk).

Figure 3

Biological network model-development process. Initial static BN models can be constructed using biological facts derived from the literature. This process mainly involves manual curation. These initial models can serve as the basis to guide the development of computable BN models. These models rely on biological facts (key events) derived from both the literature and new experimental data and are expressed in a computable format such as the biological expression language (BEL). Computational methods such as reverse causal reasoning and reverse engineering are used to support the model-building process. Such networks can then serve as the foundation to build executable BN models in which edges are expressed with equations such as ordinary differential equations. The main applications of these broad classes of BN models are shown in light blue boxes. Artwork by Samantha J. Elmhurst (www.livingart.org.uk).