Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. (August 2020)

• Record Type: Journal Article
• Authors: Cotterill, J.; Price, N.; Rorije, E.; Peijnenburg, A.
• Is Part Of: Food and chemical toxicology. Volume 142(2020)
• Publication Date: 2020-08
• Subjects: Steatosis -- Non-alcoholic fatty liver disease -- QSAR model; AO Adverse Outcome -- AOP Adverse Outcome Pathway -- CAG Cumulative Assessment Group -- CEBS Chemical Effects in Biological Systems -- EFSA European Food Safety Authority -- KE Key Event -- LEL lowest effect level -- MIE Molecular Initiating Event -- NAFLD non-alcoholic fatty liver disease -- NASH non-alcoholic steatohepatitis -- NR Nuclear Receptor -- NTP National Toxicology Program -- PCB polychlorinated biphenyls -- QSAR Quantitative Structure Activity Relationship -- TAFLD toxicant-associated fatty liver disease -- TASH toxicant-associated steatohepatitis -- ToxRefDB Toxicological Reference Database -- VOC Volatile Organic Compound; Toxicology -- Periodicals; Food poisoning -- Periodicals; Food Poisoning -- Periodicals; Toxicology -- Periodicals; Toxicologie -- Périodiques; Intoxications alimentaires -- Périodiques; Food poisoning; Toxicology; Periodicals; Electronic journals; 615.9
• DOI: 10.1016/j.fct.2020.111494
• Languages: English
• ISSNs: 0278-6915

Abstract

Abstract: There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has the potential to provide a rapid and cost-effective method to identify compounds which may trigger steatosis. Although models exist to predict key molecular initiating events of steatosis such as nuclear receptor binding, we are aware of no models to predict the apical effect steatosis. In this study, we describe the development of a QSAR model to predict steatosis using freely available machine learning tools. It was built using a dataset of 207 pharmaceuticals and pesticides which were identified as steatotic or non-steatotic from existing data from in vivo human and animal studies. The best performing model developed using the linear discriminant analysis module in TANAGRA, based on four chemical descriptors, had an accuracy of 70%, a sensitivity of 66% and a specificity of 74%. The expansion of the steatosis dataset to other chemical types, to enable the development of further models, would be of benefit in the identification of compounds with a range of mechanisms of action contributing to steatosis. Highlights: QSAR model to predict steatosis developed using in vivo data. Complements models for specific Nuclear Receptors associated with steatosis. Model developed using freely available learning tools.