Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems.

Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Research Abstract Details 

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Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Abstract Text:

Andrei I Khlebnikov,Igor A Schepetkin,Nina G Domina,Liliya N Kirpotina,Mark T Quinn,

Quantitative structure-activity relationship (QSAR) models are useful in understanding how chemical structure relates to the biological activity of natural and synthetic chemicals and for design of newer and better therapeutics. In the present study, 46 flavonoids and related polyphenols were evaluated for direct/indirect antioxidant activity in three different assay systems of increasing complexity (chemical, enzymatic, and intact phagocytes). Based on these data, two different QSAR models were developed using (i) physicochemical and structural (PC&S) descriptors to generate multiparameter partial least squares (PLS) regression equations derived from optimized molecular structures of the tested compounds and (ii) a partial 3D comparison of the 46 compounds with local fingerprints obtained from fragments of the molecules by the frontal polygon (FP) method. We obtained much higher QSAR correlation coefficients (r) for flavonoid end-point antioxidant activity in all three assay systems using the FP method (0.966, 0.948, and 0.965 for datasets evaluated in the biochemical, enzymatic, and whole cell assay systems, respectively). Furthermore, high leave-one-out cross-validation coefficients (q2) of 0.907, 0.821, and 0.897 for these datasets, respectively, indicated enhanced predictive ability and robustness of the model. Using the FP method, structural fragments (submolecules) responsible for the end-point antioxidant activity in the three assay systems were also identified. To our knowledge, this is the first QSAR model derived for description of flavonoid direct/indirect antioxidant effects in a cellular system, and this model could form the basis for further drug development of flavonoid-like antioxidant compounds with therapeutic potential.

Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Publishing Authors By Initials

AI Khlebnikov,IA Schepetkin,NG Domina,LN Kirpotina,MT Quinn,

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Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Bioorganic & medicinal chemistry

VOLUME: 15

Page Numbers: 1749-70

Journal Abbreviation: Bioorg. Med. Chem.

ISSN: 0968-0896

DAY: 29

MONTH: 11

YEAR: 2006

Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Information

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LANGUAGE: eng

NlmUniqueID: 9413298

Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Keywords Mesh Terms:

KEYWORDS: Quantitative Structure-Activity Relation

MESH TERMS: pharmacology

Chemical & Substance for Abstract: Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems. Information

Substance Name: polyphenols

Registry Number: 0

Grant and Affiliation Information for Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems.

AFFILIATION: Department of Chemistry, Altai State Technical University, Barnaul 656038, Russia. aikhlebnikov@narod.ru

Country: England

AGENCY: United States NCRR

GRANT: RR020185

ACRONYM: RR

MEDLINETA: Bioorg Med Chem

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