Databases Facilitating Systems Biology Approaches in Toxicology

DALE E. JOHNSON*a,b AND ANN M. H. HESLINb

aUniversity of Michigan, School of Public Health, Department of Environmental Health sciences, Апп arbor, Mi 48109-2029, usA; bUniversity of California, Berkeley, Department of Nutritional sciences and toxicology, Morgan Hall, Berkeley, CA 94720-3104, UsA *E-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

Introduction

several authors have published reviews of the application of systems biology approaches to toxicology, all of which require the use of multiple data sets and resources to draw novel inferences about mechanisms and modes of action of chemicals on biological targets, networks, and systems. these approaches include those in therapeutics research, particularly in understanding and predicting adverse drug reactions, and sometimes environmental or ecotoxicology approaches where information on the chemical(s) in question is sparse.1-7 As discussed by sturla and colleagues5, the core objective of systems toxicology is to uncover and hopefully elucidate mechanisms that causally link exposure to active substances with chemically induced adverse events and disease. The process requires the collection of quantifiable experimental

Issues in Toxicology No. 31

Computational Systems Pharmacology and Toxicology Edited by Dale E. Johnson and Rudy J. Richardson © The Royal Society of Chemistry 2017 Published by the Royal Society of Chemistry, www.rsc.org data typically coupled with extensive information gained by processing large sets of data positioned within biological networks and pathways. These data are garnered from accessible databases to allow the reflection of molecular changes in the context of cellular, tissue-level, or physiological changes that are linked to disease phenotypes or adverse events at the organism level.7 several authors have published lists of relevant databases for toxicology data, both for therapeutic and environmental research.18-10 Accordingly, systems toxicology relies heavily on computational approaches to manage, analyze, and interpret these data with the ultimate goal to aid in the development of predictive in silico models that can be used in risk assessment. Computational systems toxicology has the following major areas of focus.6

  • (1) Analyzing the massive amounts of in vitro and in vivo data contained in databases generated by multiple methods and correlating structural features of the compounds with levels of exposure and outcome.
  • (2) Representing the relevant mechanisms leading to an adverse outcome as biological network models that describe the normal state and the causal effect of their perturbations upon exposure to chemical compounds.
  • (3) Quantifying the dose-dependent and time-resolved perturbations of these biological networks their overall biological impact upon exposure and assessing risk.
  • (4) Building and validating adequate computational models with predictive power that can be applied to risk assessment.

In the adverse outcome pathway model, the sequence of events that lead to an adverse outcome span multiple levels of biological organization,8 but always contain a molecular initiating event, which is defined as the initial interaction between a chemical molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway.8 In the therapeutics toxicology field, systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand adverse drug reactions and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict adverse drug reactions in individual patients and global populations.11

As mentioned in Chapter 1, several factors have been involved in the rapid changes seen in the toxicology field, including the understanding of genetic changes, which have enhanced the ability to understand diseases, and chemically-induced toxicities at the molecular level. “Omics” technologies and approaches such as transcriptomics, metabolomics, proteomics, and epig- enomics have changed the way mechanisms of toxicity and the perturbation of biological systems that lead to adverse outcomes are viewed.5 These advances have been coupled with the public availability of large data sets of information and new modeling approaches that have enhanced the ability to understand toxicological events and effects at multiple biological levels.6 since scientific approaches, inquiries, and visions aimed at understanding toxicological events and outcomes have been broadened tremendously, this reinforces the need for new and better ways to assess toxicity and risk.

 
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