Modelling the chemical reactivity and biological functions of phenolics: a computational approach

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dc.contributor.authorMighri, Hedi
dc.contributor.authorBennour, Naima
dc.contributor.authorJarray, Noureddine
dc.contributor.authorHarboub, Nesrine
dc.contributor.authorMadrid, Pablo Campra
dc.contributor.authorKucukbay, Hasan
dc.contributor.authorAkrout, Ahmed
dc.date.accessioned2026-04-04T13:35:17Z
dc.date.available2026-04-04T13:35:17Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThis study was conducted on wild Ajuga iva species, widely used in Tunisian folk medicine for treating various diseases. It was selected as a model plant to introduce an innovative predictive modelling approach that aims to establish a connection between its phenolic composition and related biological activities. For that, six extracts were prepared using solvents of increasing polarity (Hex: Hexane, DCM: Dichloromethane, EtAc: Ethyl acetate, BuOH: Butanol, EtOH: Ethanol, and water: Aq) and evaluated for a range of biological activities. Kaempferol, cynaroside, and gallic, 3,4-di-O-caffeoylquinic, and salvianolic acids were identified by LC-MS analysis, for the first time in the A. iva's aerial part. Based on chromatographic profiling and response surface methodology (RSM), the established model exhibited robust predictive power (R2 = 0.946) in correlating phenolic content with bioactivity. Variance inflation factor (VIF) analysis allowed selecting salvianolic acid, quercetin, cirsiliol, and cirsilineol as the most impactful contributors to this bioactivity. These findings were further supported by Partial least squares regression (PLSR) to confirm the model's ability in capturing complex multivariate interactions. Despite this, interpretation of the negative regression coefficients in the generated model, raised questions about underlying compound dynamics, potentially influenced by multicollinearity, synergistic or antagonistic effects, or concentration thresholds. These interactions warrant further investigation through in silico approaches to better understand the mechanistic insights into the real effects.
dc.identifier.doi10.1007/s41207-025-01003-w
dc.identifier.issn2365-6433
dc.identifier.issn2365-7448
dc.identifier.issue1
dc.identifier.orcid0000-0003-4298-1890
dc.identifier.scopus2-s2.0-105025434863
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s41207-025-01003-w
dc.identifier.urihttps://hdl.handle.net/11616/109738
dc.identifier.volume11
dc.identifier.wosWOS:001645350200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEuro-Mediterranean Journal For Environmental Integration
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectPhenolic composition
dc.subjectBiological activity
dc.subjectResponse surface methodology
dc.subjectPartial least squares regression
dc.subjectSalvianolic acid
dc.titleModelling the chemical reactivity and biological functions of phenolics: a computational approach
dc.typeArticle

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