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Idowu, SO and Fatokun, AA (2020) Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays. SLAS Technology. ISSN 2472-6303
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Artificial Intelligence (AI) to the Rescue Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. Download (1MB) | Preview |
Abstract
Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently "biorelevant," because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol's antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F2-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.
| Item Type: | Article |
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| Uncontrolled Keywords: | antioxidants; artificial intelligence; bioassays; machine learning; phytochemicals |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RM Therapeutics. Pharmacology R Medicine > RS Pharmacy and materia medica |
| Divisions: | Pharmacy & Biomolecular Sciences |
| Publisher: | Sage |
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| Date Deposited: | 24 Nov 2020 09:27 |
| Last Modified: | 04 Sep 2021 06:20 |
| DOI or ID number: | 10.1177/2472630320962716 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/14067 |
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