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Natural modulators of nonalcoholic fatty liver disease: Mode of action analysis and in silico ADME-Tox prediction.

作者信息

Al Sharif Merilin, Alov Petko, Vitcheva Vessela, Diukendjieva Antonia, Mori Mattia, Botta Bruno, Tsakovska Ivanka, Pajeva Ilza

机构信息

Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 105, 1113 Sofia, Bulgaria.

Faculty of Pharmacy, Medical University of Sofia, Dunav 2 Str., 1000 Sofia, Bulgaria.

出版信息

Toxicol Appl Pharmacol. 2017 Dec 15;337:45-66. doi: 10.1016/j.taap.2017.10.013. Epub 2017 Nov 5.

Abstract

Nonalcoholic fatty liver disease (NAFLD) is considered to be the most common chronic liver disease. The discovery of natural product-based NAFLD modulators requires a more comprehensive study of their modes of action (MoAs). In this study we analysed available in the literature data for 26 naturally-derived compounds associated with experimental evidence for NAFLD alleviation and outlined potential biomolecular targets and a network of pharmacological MoAs for 12 compounds with the highest number of experimentally supported MoA key events, modulated by them. Despite the general perception that the therapeutic agents of natural origin are safe, an evaluation of ADME-Tox properties of these compounds has also been performed in order to estimate their suitability as drug candidates. We evaluated how the investigated structures fit to Lipinski's "Rule of five" and predicted their potential Phase I biotransformation pathways and toxicological effects using the ACD/Percepta platform, and the Meteor Nexus and Derek Nexus knowledge-based systems. Our results revealed the potential of the studied compounds as lead structures and outlined those of them that needed further optimisation of their pharmacokinetic profiles. The presented combined MoA/in silico approach could be extrapolated to naturally-derived and pathology-relevant lead structures with other biological activities. It could direct their optimisation by a mechanistically justified in silico evaluation.

摘要

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