Academic journal article Environmental Health Perspectives

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

Academic journal article Environmental Health Perspectives

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

Article excerpt

Introduction

Integrative computational approaches that combine systems biology and toxicology can increase our understanding of the links between environmental chemical exposure and human health. Systems biology and advanced bioinformatics tools generate new hypotheses. They furnish new insights into and predictions of biological mechanisms induced by chemical substances, including drugs and environmental pollutants. Compelling evidence indicates that a number of chemical substances may play a causative role in diseases (Heindel and Blumberg 2018). Computational sciences, including systems toxicology, can speed up the identification of linkage between adverse outcome pathways (AOPs) and a chemical stressor as well as its effects on health (Ankley et al. 2010).

The concept of an AOP was originally proposed by Ankley et al. (2010). AOPs integrate various key events (KEs) to connect biological perturbations, at the molecular or cellular levels, to toxicity events [i.e., adverse outcomes (AOs)] at organismal and population levels. The use of clearly identified AOPs for decision-making is part of a global methodological initiative, which has, among its goals, the reduction of animal use in toxicity testing. AOPs are expected to be used more and more in regulatory frameworks since they provide evidence-based mechanistic insights (Bopp et al. 2018). The AOPs, which have been identified, are stored in the AOP-Wiki online database (SAAOP 2016). The database is part of a collaborative program that involves the Organisation for Economic Cooperation and Development (OECD) and the European Commission. The AOP knowledge database (AOP-KB) is another tool from the OECD program for AOP development, to support and share information to the scientific community and harmonize the format of generated novel AOP (OECD). All the terms defined in the AOPs are standardized according to structured ontologies (Ives et al. 2017).

Although the development of AOPs has a great potential to address existing knowledge gaps, AOP development and assembly is laborious and time-consuming, since extensive toxicity data need to be gathered. Much of the information that is accumulating derives from omics technologies, high-throughput testing with robots [ToxCast (U.S. EPAa) (Judson et al. 2010)], and novel databases derived from the compilation of heterogeneous information such as the Comparative Toxicogenomics database (CTD) (Davis et al. 2018). Therefore, the development of innovative computing methodologies that allow the prioritization of chemicals according to their inferred threats is highly relevant both for the research community and for health agencies (Richard et al. 2016; Thomas et al. 2013). Such in silico methods that use available data sources also can accelerate the description of new AOPs and provide integrated data to increase the information content of existing AOPs (Berggren et al. 2015; OECD 2014).

The breadth of the currently available scientific literature and diversity of synonyms for chemicals complicates meaningful integration of the information. Thus, it can be difficult to completely and accurately acquire the information on a selected topic, even if specific databases related to a given field have been compiled and information stored [e.g., the Developmental and Reproductive Toxicology database, DART (NIH and TOXNETb)]. In addition to enriching toxicological databases, there is a need for tools allowing better exploration of available databases (including available published literature), and to improve text mining (TM) is such a way to facilitate the establishment of links between a chemical and relevant AOP components. Such tools should be able to explore a wider range of data and have the potential to prioritize the chemical-health outcome connections. We describe here a strategy that integrates a new tool called AOP-helpFinder version 1.0, downloadable on github (https://github.com/jecarvaill/aop-helpFinder). …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.