Interactions of Oleanolic Acid, Apigenin, Rutin, Resveratrol and Ferulic Acid with Phosphatidylcholine Lipid Membranes—A Spectroscopic and Machine Learning Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Chemicals and Reagents
2.2. Preparation of Liposomes
2.3. Steady-State Fluorescence and Anisotropy Fluorescence Measurements
2.4. Dynamic Light Scattering (DLS) and Zeta Potential Measurements
2.5. Antioxidant Activity Assay
2.6. Statistical Analysis
2.7. Machine Learning
3. Results and Discussion
3.1. Interactions of Oleanolic Acid and Polyphenols with Lipid Membranes—Steady-State Fluorescence Measurements
3.2. Influence of Oleanolic Acid and Polyphenols on Lipid Membranes Structure—Fluorescence Anisotropy Measurements
3.3. Influence of Oleanolic Acid and Polyphenols on Phosphatidylcholine Liposomes Hydrodynamic Diameter—Dynamic Light Scattering (DLS) Measurements
3.4. Influence of Oleanolic Acid and Polyphenols on Phosphatidylcholine Liposomes Zeta Potential
3.5. Antioxidant Activity of Phenolic Compounds on Phosphatidylcholine Membranes
3.6. Machine Learning
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ayeleso, T.B.; Matumba, M.G.; Mukwevho, E. Oleanolic Acid and Its Derivatives: Biological Activities and Therapeutic Potential in Chronic Diseases. Molecules 2017, 22, 1915. [Google Scholar] [CrossRef] [PubMed]
- Huang, J.; Lin, S.; Zhu, F.; Xu, L. Exploring the Underlying Mechanism of Oleanolic Acid Treating Glioma by Transcriptome and Molecular Docking. Biomed. Pharmacother. 2022, 154, 113586. [Google Scholar] [CrossRef] [PubMed]
- Verstraeten, S.; Catteau, L.; Boukricha, L.; Quetin-Leclercq, J.; Mingeot-Leclercq, M.-P. Effect of Ursolic and Oleanolic Acids on Lipid Membranes: Studies on MRSA and Models of Membranes. Antibiotics 2021, 10, 1381. [Google Scholar] [CrossRef]
- Castellano, J.M.; Ramos-Romero, S.; Perona, J.S. Oleanolic Acid: Extraction, Characterization and Biological Activity. Nutrients 2022, 14, 623. [Google Scholar] [CrossRef] [PubMed]
- Han, S.K.; Ko, Y.I.; Park, S.J.; Jin, I.J.; Kim, Y.M. Oleanolic Acid and Ursolic Acid Stabilize Liposomal Membranes. Lipids 1997, 32, 769–773. [Google Scholar] [CrossRef]
- Naparlo, K.; Bartosz, G.; Stefaniuk, I.; Cieniek, B.; Soszynski, M.; Sadowska-Bartosz, I. Interaction of Catechins with Human Erythrocytes. Molecules 2020, 25, 1456. [Google Scholar] [CrossRef] [PubMed]
- Naftalin, R.J.; Afzal, I.; Cunningham, P.; Halai, M.; Ross, C.; Salleh, N.; Milligan, S.R. Interactions of Androgens, Green Tea Catechins and the Antiandrogen Flutamide with the External Glucose-Binding Site of the Human Erythrocyte Glucose Transporter GLUT1. Br. J. Pharmacol. 2003, 140, 487–499. [Google Scholar] [CrossRef]
- Karonen, M. Insights into Polyphenol–Lipid Interactions: Chemical Methods, Molecular Aspects and Their Effects on Membrane Structures. Plants 2022, 11, 1809. [Google Scholar] [CrossRef]
- Phan, H.T.T.; Yoda, T.; Chahal, B.; Morita, M.; Takagi, M.; Vestergaard, M.C. Structure-Dependent Interactions of Polyphenols with a Biomimetic Membrane System. Biochim. Biophys. Acta Biomembr. 2014, 1838, 2670–2677. [Google Scholar] [CrossRef]
- Andrade, S.; Ramalho, M.J.; Loureiro, J.A.; Pereira, M.C. The Biophysical Interaction of Ferulic Acid with Liposomes as Biological Membrane Model: The Effect of the Lipid Bilayer Composition. J. Mol. Liq. 2021, 324, 114689. [Google Scholar] [CrossRef]
- Rabbani, M.; Pezeshki, A.; Ahmadi, R.; Mohammadi, M.; Tabibiazar, M.; Ahmadzadeh Nobari Azar, F.; Ghorbani, M. Phytosomal Nanocarriers for Encapsulation and Delivery of Resveratrol-Preparation, Characterization, and Application in Mayonnaise. LWT 2021, 151, 112093. [Google Scholar] [CrossRef]
- Xiao, Z.; Wang, J.; Han, L.; Guo, S.; Cui, Q. Application of Machine Vision System in Food Detection. Front. Nutr. 2022, 9, 888245. [Google Scholar] [CrossRef] [PubMed]
- Przybył, K.; Wawrzyniak, J.; Koszela, K.; Adamski, F.; Gawrysiak-Witulska, M. Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed. Sensors 2020, 20, 7305. [Google Scholar] [CrossRef]
- Przybył, K.; Koszela, K.; Adamski, F.; Samborska, K.; Walkowiak, K.; Polarczyk, M. Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders. Sensors 2021, 21, 5823. [Google Scholar] [CrossRef]
- Przybył, K.; Samborska, K.; Koszela, K.; Masewicz, L.; Pawlak, T. Artificial Neural Networks in the Evaluation of the Influence of the Type and Content of Carrier on Selected Quality Parameters of Spray Dried Raspberry Powders. Measurement 2021, 186, 110014. [Google Scholar] [CrossRef]
- Przybył, K.; Duda, A.; Koszela, K.; Stangierski, J.; Polarczyk, M.; Gierz, Ł. Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks. Sensors 2020, 20, 499. [Google Scholar] [CrossRef] [PubMed]
- Camacho, D.M.; Collins, K.M.; Powers, R.K.; Costello, J.C.; Collins, J.J. Next-Generation Machine Learning for Biological Networks. Cell 2018, 173, 1581–1592. [Google Scholar] [CrossRef]
- Carracedo-Reboredo, P.; Liñares-Blanco, J.; Rodríguez-Fernández, N.; Cedrón, F.; Novoa, F.J.; Carballal, A.; Maojo, V.; Pazos, A.; Fernandez-Lozano, C. A Review on Machine Learning Approaches and Trends in Drug Discovery. Comput. Struct. Biotechnol. J. 2021, 19, 4538–4558. [Google Scholar] [CrossRef]
- Dara, S.; Dhamercherla, S.; Jadav, S.S.; Babu, C.M.; Ahsan, M.J. Machine Learning in Drug Discovery: A Review. Artif. Intell. Rev. 2022, 55, 1947–1999. [Google Scholar] [CrossRef]
- Weissler, E.H.; Naumann, T.; Andersson, T.; Ranganath, R.; Elemento, O.; Luo, Y.; Freitag, D.F.; Benoit, J.; Hughes, M.C.; Khan, F.; et al. The Role of Machine Learning in Clinical Research: Transforming the Future of Evidence Generation. Trials 2021, 22, 537. [Google Scholar] [CrossRef]
- Ocampo, I.; López, R.R.; Camacho-León, S.; Nerguizian, V.; Stiharu, I. Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer. Micromachines 2021, 12, 1164. [Google Scholar] [CrossRef] [PubMed]
- Shabanzadeh, P.; Senu, N.; Shameli, K.; Ismail, F.; Zamanian, A.; Mohagheghtabar, M. Prediction of Silver Nanoparticles’ Diameter in Montmorillonite/Chitosan Bionanocomposites by Using Artificial Neural Networks. Res. Chem. Intermed. 2015, 41, 3275–3287. [Google Scholar] [CrossRef]
- Balducci, V.; Incerpi, S.; Stano, P.; Tofani, D. Antioxidant Activity of Hydroxytyrosyl Esters Studied in Liposome Models. Biochim. Biophys. Acta Biomembr. 2018, 1860, 600–610. [Google Scholar] [CrossRef] [PubMed]
- Kingma, D.P.; Ba, J.L. Adam: A Method for Stochastic Optimization. 3rd International Conference on Learning Representations, ICLR 2015—Conference Track Proceedings. arXiv 2014, arXiv:1412.6980. [Google Scholar] [CrossRef]
- Patterson, J.; Gibson, A. Deep Learning A Practitioner’s Approach; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2017. [Google Scholar]
- Kittipongpittaya, K.; Panya, A.; Cui, L.; McClements, D.J.; Decker, E.A. Association Colloids Formed by Multiple Surface Active Minor Components and Their Effect on Lipid Oxidation in Bulk Oil. JAOCS J. Am. Oil Chem. Soc. 2014, 91, 1955–1965. [Google Scholar] [CrossRef]
- Schoefer, L.; Braune, A.; Blaut, M. A Fluorescence Quenching Test for the Detection of Flavonoid Transformation. FEMS Microbiol. Lett. 2006, 204, 277–280. [Google Scholar] [CrossRef]
- Zhao, H.; Lappalainen, P. A Simple Guide to Biochemical Approaches for Analyzing Protein-Lipid Interactions. Mol. Biol. Cell 2012, 23, 2823–2830. [Google Scholar] [CrossRef]
- Czubinski, J.; Dwiecki, K. Heat-Induced Changes in Lupin Seed γ-Conglutin Structure Promote Its Interaction with Model Phospholipid Membranes. Food Chem. 2022, 374, 131533. [Google Scholar] [CrossRef]
- Tsuchiya, H.; Nagayama, M.; Tanaka, T.; Furusawa, M.; Kashimata, M.; Takeuchi, H. Membrane-Rigidifying Effects of Anti-Cancer Dietary Factors. BioFactors 2002, 16, 45–56. [Google Scholar] [CrossRef]
- Lenne-Gouverneur, A.F.; Lobstein, A.; Haan-Archipoff, G.; Duportail, G.; Anton, R.; Kuhry, J.G. Interactions of the Monomeric and Dimeric Flavones Apigenin and Amentoflavone with the Plasma Membrane of L929 Cells; A Fluorescence Study. Mol. Membr. Biol. 1999, 16, 157–165. [Google Scholar] [CrossRef]
- Song, F.; Chen, J.; Zheng, A.; Tian, S. Effect of Sterols on Liposomes: Membrane Characteristics and Physicochemical Changes during Storage. LWT 2022, 164, 113558. [Google Scholar] [CrossRef]
- Banerjee, K.; Banerjee, S.; Mandal, M. Enhanced Chemotherapeutic Efficacy of Apigenin Liposomes in Colorectal Cancer Based on Flavone-Membrane Interactions. J. Colloid Interface Sci. 2017, 491, 98–110. [Google Scholar] [CrossRef] [PubMed]
- Banerjee, K.; Banerjee, S.; Das, S.; Mandal, M. Probing the Potential of Apigenin Liposomes in Enhancing Bacterial Membrane Perturbation and Integrity Loss. J. Colloid Interface Sci. 2015, 453, 48–59. [Google Scholar] [CrossRef]
- Pawlikowska-Pawlȩga, B.; Misiak, L.E.; Zarzyka, B.; Paduch, R.; Gawron, A.; Gruszecki, W.I. FTIR, 1H NMR and EPR Spectroscopy Studies on the Interaction of Flavone Apigenin with Dipalmitoylphosphatidylcholine Liposomes. Biochim. Biophys. Acta Biomembr. 2013, 1828, 518–527. [Google Scholar] [CrossRef]
- Halevas, E.G.; Avgoulas, D.I.; Katsipis, G.; Pantazaki, A.A. Flavonoid-Liposomes Formulations: Physico-Chemical Characteristics, Biological Activities and Therapeutic Applications. Eur. J. Med. Chem. Rep. 2022, 5, 100059. [Google Scholar] [CrossRef]
- Isailović, B.D.; Kostić, I.T.; Zvonar, A.; Dordević, V.B.; Gašperlin, M.; Nedović, V.A.; Bugarski, B.M. Resveratrol Loaded Liposomes Produced by Different Techniques. Innov. Food Sci. Emerg. Technol. 2013, 19, 181–189. [Google Scholar] [CrossRef]
- Ara, T.; Ono, S.; Hasan, M.; Ozono, M.; Kogure, K. Protective Effects of Liposomes Encapsulating Ferulic Acid against CCl4-Induced Oxidative Liver Damage in Vivo Rat Model. J. Clin. Biochem. Nutr. 2023, 72, 46–53. [Google Scholar] [CrossRef]
- Tai, K.; He, X.; Yuan, X.; Meng, K.; Gao, Y.; Yuan, F. A Comparison of Physicochemical and Functional Properties of Icaritin-Loaded Liposomes Based on Different Surfactants. Colloids Surf. A Physicochem. Eng. Asp. 2017, 518, 218–231. [Google Scholar] [CrossRef]
- Zhang, J.; Stanley, R.A.; Melton, L.D. Lipid Peroxidation Inhibition Capacity Assay for Antioxidants Based on Liposomal Membranes. Mol. Nutr. Food Res. 2006, 50, 714–724. [Google Scholar] [CrossRef]
- Tadolini, B.; Juliano, C.; Piu, L.; Franconi, F.; Cabrini, L. Resveratrol Inhibition of Lipid Peroxidation. Free Radic. Res. 2000, 33, 105–114. [Google Scholar] [CrossRef]
- Li, Z.; Chen, X.; Liu, G.; Li, J.; Zhang, J.; Cao, Y.; Miao, J. Antioxidant Activity and Mechanism of Resveratrol and Polydatin Isolated from Mulberry (Morus alba L.). Molecules 2021, 26, 7574. [Google Scholar] [CrossRef] [PubMed]
- Siddiqui, M.R.; AlOthman, Z.A.; Rahman, N. Analytical Techniques in Pharmaceutical Analysis: A Review. Arab. J. Chem. 2017, 10, S1409–S1421. [Google Scholar] [CrossRef]
- Lodén, M.; Ungerth, L.; Serup, J. Changes in European Legislation Make It Timely to Introduce a Transparent Market Surveillance System for Cosmetics. Acta Derm. Venereol. 2007, 87, 485–492. [Google Scholar] [CrossRef] [PubMed]
- Küster, A.; Adler, N. Pharmaceuticals in the Environment: Scientific Evidence of Risks and Its Regulation. Philos. Trans. R. Soc. B Biol. Sci. 2014, 369, 20130587. [Google Scholar] [CrossRef] [PubMed]
- Bonfilio, R.; de Araújo, M.B.; Salgado, H.R.N. Recent Applications of Analytical Techniques for Quantitative Pharmaceutical Analysis: A Review. WSEAS Trans. Biol. Biomed. 2010, 7, 316–338. [Google Scholar]
- Chen, L. Stock Price Prediction Using Adaptive Time Series Forecasting and Machine Learning Algorithms; University of California: Los Angeles, CA, USA, 2020. [Google Scholar]
- Bashir, D.; Montañez, G.D.; Sehra, S.; Segura, P.S.; Lauw, J. An Information-Theoretic Perspective on Overfitting and Underfitting. In Lecture Notes in Computer Science, Proceedings of the Australasian Joint Conference on Artificial Intelligence, Canberra, ACT, Australia, 29–30 November 2020; Springer: Cham, Switzerland, 2020; Volume 12576. [Google Scholar]
Type | Name MLP | Test Set | Training Set | ||||||
---|---|---|---|---|---|---|---|---|---|
MSE | MAE | R2 | MSE | MAE | R2 | Activation Hidden | Training Algorithm | ||
resveratrol (RS) | 1:1-100-50-50-6:1 | 0.0148 | 0.0634 | 0.8247 | 0.0284 | 0.0981 | 0.7989 | Tanh | adam |
oleanolic acid (OA) | 1:1-100-50-20-6:1 | 0.0358 | 0.0107 | 0.7991 | 0.0380 | 0.1225 | 0.8165 | Tanh | adam |
ferulic acid (FA) | 1:1-100-50-50-6:1 | 0.0305 | 0.0996 | 0.7266 | 0.0359 | 0.1252 | 0.7810 | Tanh | adam |
rutin (RT) | 1:1-200-100-50-6:1 | 0.0237 | 0.0831 | 0.6928 | 0.0267 | 0.0982 | 0.8520 | Tanh | adam |
apigenin (AP) | 1:1-100-50-50-6:1 | 0.0199 | 0.0710 | 0.5563 | 0.0308 | 0.1033 | 0.7462 | Tanh | adam |
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Dwiecki, K.; Przybył, K.; Dezor, D.; Bąkowska, E.; Rocha, S.M. Interactions of Oleanolic Acid, Apigenin, Rutin, Resveratrol and Ferulic Acid with Phosphatidylcholine Lipid Membranes—A Spectroscopic and Machine Learning Study. Appl. Sci. 2023, 13, 9362. https://doi.org/10.3390/app13169362
Dwiecki K, Przybył K, Dezor D, Bąkowska E, Rocha SM. Interactions of Oleanolic Acid, Apigenin, Rutin, Resveratrol and Ferulic Acid with Phosphatidylcholine Lipid Membranes—A Spectroscopic and Machine Learning Study. Applied Sciences. 2023; 13(16):9362. https://doi.org/10.3390/app13169362
Chicago/Turabian StyleDwiecki, Krzysztof, Krzysztof Przybył, Dobrawa Dezor, Ewa Bąkowska, and Silvia M. Rocha. 2023. "Interactions of Oleanolic Acid, Apigenin, Rutin, Resveratrol and Ferulic Acid with Phosphatidylcholine Lipid Membranes—A Spectroscopic and Machine Learning Study" Applied Sciences 13, no. 16: 9362. https://doi.org/10.3390/app13169362
APA StyleDwiecki, K., Przybył, K., Dezor, D., Bąkowska, E., & Rocha, S. M. (2023). Interactions of Oleanolic Acid, Apigenin, Rutin, Resveratrol and Ferulic Acid with Phosphatidylcholine Lipid Membranes—A Spectroscopic and Machine Learning Study. Applied Sciences, 13(16), 9362. https://doi.org/10.3390/app13169362