Engineering and validation of a novel lipid thin film for biomembrane modeling in lipophilicity determination of drugs and xenobiotics

Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays

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.

Computational Antioxidant Capacity Simulation (CAOCS): A Novel Framework of Antioxidant Capacity Profiling

Inconsistent ranking is a well-known drawback of antioxidant capacity (AOC) profiling methodologies that use free-radical species as oxidant. This problem leads to assay results that are not biorelevant. Linear free energy relationships (LFER) theory predicts proton transfer (PT) kinetics as a surrogate for biorelevant hydrogen atom transfer (HAT) kinetics. Computational antioxidant capacity simulation (CAOCS), based on real-time proton transfer kinetics modeling (PTKM) of polyphenols and phenol-like small molecules, inspired a novel AOC profiling methodology.

Kinetic data acquired by incremental addition of resorcinol to an oxidized probe (phenol red), was fitted to mono-exponential decay equation (MED). Absorbance decay data from strongly antioxidant phenol-like molecules (e.g. ascorbic acid) and a new chromogenic probe (phenolphthalein) was fitted to MED and bi-exponential decay equation. The preferred model and corresponding best-fit rate constant (Kptt) was identified by comparison of fits, using Akaike’s Information Criterion (AICc).

Photometric phenolphthalein assay (PPA)-derived metric was normalized with photometric phenol red assay (PPRA) results by using a function developed from proton concentration differential between phenolphthalein and phenol red, with respect to decay threshold to plateau (assay endpoint) interval. pKa dependence of the CAOCS’ metric is a signature of structure–function relationships, and hence, biorelevance.

It is shown, unambiguously, that a combination of two phenolic probe molecules, an analytical system devoid of free radicals, and statistical identification of preferred exponential decay fit to PT kinetics data, constitutes a novel algorithm for AOC profiling of polyphenols and phenol-like molecules. This methodology holds a promise of utility in quality assurance of dietary supplements.