In Silico Evaluation of Salvia rosmarinus Spenn. Metabolites as Potential Modulators of the Keap1/Nrf2 Pathway

Oxidative stress is a common biological mechanism involved in chronic inflammation, metabolic disorders, neurodegeneration, cancer, and cellular stress responses. The Keap1/Nrf2 pathway isone of the central regulatory systems controlling cellular antioxidant and cytoprotective defense. Natural products, particularly plant-derived secondary metabolites, represent an important chemical space for identifying potential modulators of this pathway. In this study, metabolites reported for Salvia rosmarinus Spenn. (syn. Rosmarinus officinalis L.) were evaluated using an in silico workflow. A ligand library consisting of 410 curated metabolites was constructed from LOTUS, KNApSAcK Core System, and literature sources. Molecular docking was performed against two Keap1 structural regions: the Kelch domain represented by PDB ID 2FLU and the BTB domain represented by PDB ID 5DAF. The most promising candidate was further evaluated using Swis-sADME, SwissTargetPrediction, and molecular dynamics simulation. Luteolin-3′-O-glucuronide showed favorable docking scores against both Keap1 structures compared with reference ligands. Its interaction profile suggested that the glucuronide moiety may contribute to additional polar, hydrogen-bonding, and electrostatic contacts with key residues. Although the compound showed some favorable in silico properties, predicted low gastrointestinal absorption and low bioavailability indicate pharmacokinetic limitations. Overall, luteolin-3′-O-glucuronide may be considered a potential candidate for further experimental validation targeting the Keap1/Nrf2 pathway.

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Journal
Anatolian Journal of Pharmaceutical Sciences
Published
2026-10-27
DOI
https://doi.org/10.71133/anatphar.2045419
Primary Topic
Computational Drug Discovery Methods
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article
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article

In Silico Evaluation of Salvia rosmarinus Spenn. Metabolites as Potential Modulators of the Keap1/Nrf2 Pathway

Mehmet Abdullah Alagöz, Şüheda Rumeysa OSMANLIOĞLU DAĞ
2 links
Anatolian Journal of Pharmaceutical Sciences
Computational Drug Discovery Methods
article

In Silico Evaluation of Salvia rosmarinus Spenn. Metabolites as Potential Modulators of the Keap1/Nrf2 Pathway

Mehmet Abdullah Alagöz, Şüheda Rumeysa OSMANLIOĞLU DAĞ
article en

Abstract

Oxidative stress is a common biological mechanism involved in chronic inflammation, metabolic disorders, neurodegeneration, cancer, and cellular stress responses. The Keap1/Nrf2 pathway isone of the central regulatory systems controlling cellular antioxidant and cytoprotective defense. Natural products, particularly plant-derived secondary metabolites, represent an important chemical space for identifying potential modulators of this pathway. In this study, metabolites reported for Salvia rosmarinus Spenn. (syn. Rosmarinus officinalis L.) were evaluated using an in silico workflow. A ligand library consisting of 410 curated metabolites was constructed from LOTUS, KNApSAcK Core System, and literature sources. Molecular docking was performed against two Keap1 structural regions: the Kelch domain represented by PDB ID 2FLU and the BTB domain represented by PDB ID 5DAF. The most promising candidate was further evaluated using Swis-sADME, SwissTargetPrediction, and molecular dynamics simulation. Luteolin-3′-O-glucuronide showed favorable docking scores against both Keap1 structures compared with reference ligands. Its interaction profile suggested that the glucuronide moiety may contribute to additional polar, hydrogen-bonding, and electrostatic contacts with key residues. Although the compound showed some favorable in silico properties, predicted low gastrointestinal absorption and low bioavailability indicate pharmacokinetic limitations. Overall, luteolin-3′-O-glucuronide may be considered a potential candidate for further experimental validation targeting the Keap1/Nrf2 pathway.

Anatolian Journal of Pharmaceutical SciencesVol. 5(3)
Inonu University (TR)
Good health and well-being
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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