James Firman is a postdoctoral researcher (predictive toxicology) within the Chemoinformatics Research Group, Liverpool John Moores University, UK. In this capacity, he has published upon topics relevant both to human and environmental chemical safety assessment – with a focus concerning the development and evaluation of structure- and mechanism-based models and profiling tools. In addition to serving upon ILSI Expert Groups, contributions have been made towards European projects including IpiE, eTRANSAFE and (presently) RISK-HUNT3R. Holding a background in medicinal chemistry and pharmacology, his PhD was received in 2015 from the MRC Centre for Drug Safety Science (University of Liverpool).
Adverse Outcome Pathway-Driven Analysis of Liver Steatosis
James W. Firman1, Samuel J. Belfield1, Daniel Burgwinkel2, Swapnil Chavan3, Gerhard F. Ecker4, Barry Hardy2, Palle S. Helmke4, Jeffrey S. Wiseman2, Mark T.D. Cronin1
1Liverpool John Moores University, Liverpool, England, 2Edelweiss Connect, Basel, Switzerland, 3RISE, Sodertalje, Sweden, 4University of Vienna, Vienna, Austria
The concept of the adverse outcome pathway (AOP) has acquired popularity as a means through which the relatedness of key molecular and cellular events preceding toxicity might intuitively be rationalised. A robust AOP allows for data sourced from in vitro and in silico new approach methodologies (NAM) to be placed within context, thus enhancing confidence in the validity of such techniques as predictive tools. Many, seemingly distinct, modes of toxic action are noted to share key events (or event-sequences), ensuring that isolated AOPs may be linked at points of commonality in order to form broader networks. These, in turn, serve to afford a more extensive mechanistic insight.
We report the development of an AOP network intended to support the formulation of predictive and conceptual models (notably quantitative AOP) relevant to hepatic steatosis. Following review and curation, a collection of 15 distinct pathways, recovered from within the AOP-Wiki resource, was condensed into a network consisting of 29 nodes – representing dysregulation of events integral within the synthesis (e.g., activation of fatty acid synthase), breakdown (e.g., suppression of 17β-hydroxysteroid dehydrogenase) and cellular uptake (e.g., heightened expression of the LDL receptor) of lipid components. Each was linked, ultimately, to modulation of well characterised transcriptional regulators – amongst which are LXR, PXR, CAR and PPAR. With insight into interconnectivity established, the basis for the assembly of a knowledge graph (adopting the Neo4j platform), integrating NAM data gathered through efforts of RISK-HUNT3R partners, is provided.
