Abstract
Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams (“big data”), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.
Highlights
Toxicity Pathways and NetworksRequirements for Test Systems to Derive Pathway InformationRequirements for Omics Data to Derive PathwayRequirements for High-Throughput and High-Content Imaging Data to Derive Pathway InformationChallenge of Linking Network Perturbations to PhenotypesChallenge of Addressing Uncertainty in Computational Models for Systems ToxicologyChallenge of Pathway-Based Testing StrategiesSpecial Issue: Systems Toxicology IIReceived: January 5, 2017 Published: March 31, 2017
As a subset of systems biology, systems toxicology aims to describe the resilience of biological systems to perturbation by toxicants, i.e., the ability to return to normal function
An understanding of biology from a systems perspective involves[3] (1) collection of large sets of experimental data by high-content technologies and/or by mining molecular biology and biochemistry literature and databases; (2) proposal of mathematical models that might account for at least some significant aspects of this data set; (3) accurate computer simulation of the mathematical models to obtain numerical predictions; and (4) assessment of the quality of the models by comparing numerical simulations with the experimental data
Summary
As a subset of systems biology, systems toxicology aims to describe the resilience of biological systems to perturbation by toxicants, i.e., the ability (or lack thereof) to return to normal function. Garcia-Serna et al computationally combined chemical, structural, and biological hazard data of bioactive small molecules to gain a better understanding of the mechanisms leading to adverse effects.[54] Angrish et al introduced a gas phase probe molecule into an in vitro system, observed normal steady state, added chemicals of interest, and quantitatively measured (from headspace gas) effects on metabolism that could be linked back to a well-defined corresponding in vivo effect.[55] Gonzalez-Suarez et al selected three well-known harmful and potentially harmful constituents in tobacco smoke, established a high-content screening in normal human bronchial epithelial cells using 13 indicators of cellular toxicity complemented with a microarraybased whole-transcriptome analysis followed by a computational approach leveraging mechanistic network models, to identify and quantify perturbed molecular pathways.[56]. This type of in vitro to in vivo correlation increases the confidence in the relevance of specific biomarkers and their pathways.[138]
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