Application of Molecular Topology for Predicting the Antioxidant Activity of a Group of Phenolic Compounds

Authors

Keywords:

Antioxidant, Phenolic Compounds, Molecular Topology, Topological Indices

Abstract

The study of compounds with antioxidant capabilities is of great interest to the scientific community, as it has implications in several areas, from Agricultural Sciences to Biological Sciences, including Food Engineering, Medicine, and Pharmacy. In applications related to human health, it is known that antioxidant activity can delay or inhibit oxidative damage to cells, reducing damage caused by free radicals, helping in the treatment, or even preventing or postponing the onset of various diseases. Among the compounds that have antioxidant properties, there are several classes of phenolic compounds, which include several compounds with different chemical structures. Despite their importance, identifying and predicting the antioxidant potential of phenolic compunds remains a significant challenge due to their structural diversity and the complexity of their mechanisms of action. In this work, based on the molecular branching of compounds and their intramolecular charge distributions, and using Molecular Topology, we propose a significant topological mathematical model to evaluate the potential of candidate compounds to have an antioxidant function. The advantage of the model is that it allows for efficient predictive analysis, assisting in the identification of promising compounds more quickly and accurately, which can accelerate the development of new antioxidants with therapeutic applications.

Author Biographies

Fernando de Souza Bastos, Federal University of Viçosa

Department of Statistics, Viçosa, Brazil

Diogo da Silva Machado, Federal University of Viçosa

Department of Mathematics, Viçosa, Brazil

Jaime Barros Silva Filho, University of California, Riverside

Department of Microbiology and Plant Pathology

Maria Luiza Ferreira Delfim, Federal University of Viçosa

Department of Chemistry, Viçosa, Brazil

References

Amigó, José Manuel et al. (2007). “Topología molecular”. In: Boletín de la Sociedad Española de Matemática Aplicada. N. 39 (2007).

Ayoub, Lahmadi et al. (2018a). “A specific QSAR model for proteasome inhibitors from Olea europaea and Ficus carica”. In: Bioinformation 14.7, p. 384.

Ayoub, Lahmadi et al. (July 2018b). “A specific QSAR model for proteasome inhibitors from Olea europaea and Ficus carica”. In: Bioinformation 14, pp. 384–392.

Chen, Yu-Zhen et al. (2015). “Structure-Thermodynamics-Antioxidant Activity Relationships of Selected Natural Phenolic Acids and Derivatives: An Experimental and Theoretical Evaluation”. In: PLoS ONE 10. doi: 10.1371/journal.pone.0121276.

Cheng, Zhiyong et al. (2002). “Study on the multiple mechanisms underlying the reaction between hydroxyl radical and phenolic compounds by qualitative structure and activity relationship.” In: Bioorganic & medicinal chemistry 10 12, pp. 4067–73. doi: 10.1016/S0968-0896(02)00267-5.

Contrera, Joseph F et al. (2005). “QSAR modeling of carcinogenic risk using discriminant analysis and topological molecular descriptors”. In: Current Drug Discovery Technologies 2.2, pp. 55–67.

Cramer, Richard D (2012). “The inevitable QSAR renaissance”. In: Journal of computer-aided molecular design 26.1, pp. 35–38.

Cronin, MTD and DA Basketter (1994). “Multivariate QSAR analysis of a skin sensitization database”. In: SAR and QSAR in Environmental Research 2.3, pp. 159–179.

Fisher, Ronald A (1936). “The use of multiple measurements in taxonomic problems”. In: Annals of eugenics 7.2, pp. 179–188.

Galvez, Jorge et al. (1994a). “Charge indexes. New topological descriptors”. In: Journal of Chemical Information and Computer Sciences 34.3, pp. 520–525.

Galvez, Jorge et al. (1994b). “Topological approach to analgesia”. In: Journal of Chemical Information and Computer Sciences 34.5, pp. 1198–1203.

Gutman, Ivan, Boris Furtula, and Vladimir Katanicc (2018). “Randic index and information”. In: AKCE International Journal of Graphs and Combinatorics 15.3, pp. 307–312. issn: 0972-8600.

Hall, Lowell H and Lemont B Kier (1978). “Molecular connectivity and substructure analysis”. In: Journal of pharmaceutical sciences 67.12, pp. 1743–1747.

Hansch, Corwin and Toshio Fujita (1964). “p − σ − π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure”. In: Journal of the American Chemical Society 86.8, pp. 1616–1626.

Johnson, Richard A, Dean W Wichern, et al. (1992). “Applied multivariate statistical analysis”. In: New Jersey 405.

Karunakaran, Thiruventhan et al. (2018). “Nitric oxide inhibitory and anti-Bacillus activity of phenolic compounds and plant extracts from Mesua species”. In: Revista Brasileira de Farmacognosia 28, pp. 231–234.

Khattree, Ravindra and Dayanand N Naik (2000). “Multivariate data reduction and discrimination”. In: SAS Institute, Cary, North Carolina.

Khoddami, Ali, Meredith A Wilkes, and Thomas H Roberts (2013). “Techniques for analysis of plant phenolic compounds”. In: Molecules 18.2, pp. 2328–2375.

Khokhar, Santosh and Richard.K. Owusu Apenten (2003). “Iron binding characteristics of phenolic compounds: some tentative structure–activity relations”. In: Food Chemistry 81.1, pp. 133–140. doi: https://doi.org/10.1016/S0308-8146(02)00394-1.

Kier, Lemont (2012). Molecular connectivity in chemistry and drug research. Vol. 14. Elsevier.

Konovalov, Dmitry A et al. (2008). “Robust cross-validation of linear regression QSAR models”. In: Journal of chemical information and modeling 48.10, pp. 2081–2094.

Kumar, Naresh and Nidhi Goel (2019). “Phenolic acids: Natural versatile molecules with promising therapeutic applications”. In: Biotechnology Reports 24, e00370.

Lagana Vinci, Roberto et al. (2024). “Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography”. In: Journal of Chromatography A 1730, p. 465146.

Lu, Ang et al. (2022). “QSAR study of phenolic compounds and their anti-DPPH radical activity by discriminant analysis”. In: Scientific Reports 12.1, pp. 1–9.

Mahmoudi, Nassira et al. (2006). “Identification of new antimalarial drugs by linear discriminant analysis and topological virtual screening”. In: Journal of Antimicrobial Chemotherapy 57.3, pp. 489–497.

Martins, Natalia, Lillian Barros, and Isabel CFR Ferreira (2016). “In vivo antioxidant activity of phenolic compounds: Facts and gaps”. In: Trends in Food Science & Technology 48, pp. 1–12.

Muller, Ashley G et al. (2019). “Delivery of natural phenolic compounds for the potential treatment of lung cancer”. In: DARU Journal of Pharmaceutical Sciences 27.1, pp. 433–449.

Munteanu, Irina Georgiana and Constantin Apetrei (2021). “Analytical methods used in determining antioxidant activity: A review”. In: International Journal of Molecular Sciences 22.7, p. 3380.

Nagarajan, Subhalakshmi et al. (2020). “Antioxidant Activity of Synthetic Polymers of Phenolic Compounds”. In: Polymers 12.8. doi: 10.3390/polym12081646.

Nguyen, Truyen, Philip J. Sherratt, and C. B. Pickett (2003). “Regulatory mechanisms controlling gene expression mediated by the antioxidant response element.” In: Annual review of pharmacology and toxicology 43, pp. 233–60.

R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. url: https://www.R-project.org/.

Randic, Milan (1975). “Characterization of molecular branching”. In: Journal of the American Chemical Society 97.23, pp. 6609–6615.

Soobrattee, Muhammed Asin et al. (2005). “Phenolics as potential antioxidant therapeutic agents: mechanism and actions”. In: Mutation Research/Fundamental and Molecular mechanisms of mutagenesis 579.1-2, pp. 200–213.

Spiegel, Maciej et al. (2020). “Antioxidant activity of selected phenolic acids–ferric reducing antioxidant power assay and QSAR analysis of the structural features”. In: Molecules 25.13, p. 3088.

Todeschini, Roberto and Viviana Consonni (2008). Handbook of molecular descriptors. John Wiley & Sons.

Vasilyev, Alexander and Dragan Stevanovic (2014). “MathChem: a Python package for calculating topological indices”. In: MATCH Commun. Math. Comput. Chem 71, pp. 657–680.

Vuolo, Milena Morandi, Verena Silva Lima, and Maario Roberto Maroostica Junior (2019). “Chapter 2 - Phenolic Compounds: Structure, Classification, and Antioxidant Power”. In: Bioactive Compounds. Ed. by Maira Rubi Segura Campos. Woodhead Publishing, pp. 33–50.

Yasir, Muhammad, Bushra Sultana, and Matthew Amicucci (2016). “Biological activities of phenolic compounds extracted from Amaranthaceae plants and their LC/ESI-MS/MS profiling”. In: Journal of Functional Foods 26, pp. 645–656.

Yoo, Sae-Rom et al. (2017). “Simultaneous determination and anti-inflammatory effects of four phenolic compounds in Dendrobii Herba”. In: Natural Product Research 31.24, pp. 2923–2926.

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Published

28-12-2024

How to Cite

de Souza Bastos, F., da Silva Machado, D., Barros Silva Filho, J., & Luiza Ferreira Delfim, M. (2024). Application of Molecular Topology for Predicting the Antioxidant Activity of a Group of Phenolic Compounds. Sigmae, 13(5), 99–113. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/2544

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Section

Applied Statistics