In view of the global legislative changes, which aim to increase the speed of evaluation of chemicals, reinforce the use of alternative methods, and at the same time preserve reliable identification of chemicals of potential risk concern, a better understanding of the mechanisms leading to the development of a specific disease is needed.
The Adverse Outcome Pathway (AOP) is a framework for scientific data organisation, comprised of several building blocks starting with a molecular initiating event (MIE), which describes the initial interaction of a stressor, in most cases a chemical, with a biomolecule and continuing with a dependent series of intermediate key events (KEs) at different levels of biological organisation, while finally ending in an adverse outcome (AO) (Vinken, 2013). The primary objective for the introduction of the AOP framework was knowledge assembly, specifically, making information attained through scientific research by subject-matter experts and distributed in the body of scientific literature accessible to regulators during the decision-making process.
The actual process of AOP development is still mainly done manually, and as such is a slow, time-consuming process with associated problems of sometimes biased and even incorrect events’ linkage.
In this project, we present our advances in the area of computational approaches for AOP hypothesis generation, namely the development of a 2-step automated workflow for AOP hypothesis generation. The created workflow has potential for manifold use and applications in this field, as the incorporation of high-content and high-throughput datasets increasingly becomes a standard part of our toxicological and risk assessment practices.
As a proof-of-principle, we showcase the extraction and identification of KEs at different levels of biological organisation which are linked to non-genotoxic-induced hepatocellular carcinoma (HCC) (a type of liver cancer).
The incorporation of computational toxicology into risk assessment practices is an evolving area. The global goal of this project is to support this expansion by offering a hypothesis generation tool which can be freely exploited by the scientific community and will eventually guide the development of an integrated testing strategy completely based on in silico and in vitro data to be used in regulatory settings. The proposed computationally-predicted AOP has the advantage of being a result of a fast and unbiased process of data gathering, organisation, integration, interlinking and analysis.
The overall objectives are:
-Development of computational approach for pre-processing and linking of information coming from diverse, publicly-available data sources;
-Identification of the best methodologies for creating a collection of all relevant, interlinked events for a particular disease;
-Extraction and identification, grouping and prioritization of (de novo) KEs characteristic for non-mutagenic carcinogen-induced HCC;
-Initiation of a proof-of-principle approach and the in-practice validation of a subset of important KEs of the non-mutagenic AOP network by targeted in vitro experimentation;
-Contribution to the development of an Integrated Testing Strategy (ITS) for identification of non-mutagenic carcinogens.
