Dergiler / Türk Doğa ve Fen Dergisi / 2025 / Cilt: 14 Sayı: 4

Cotinus Coggygria Bitkisinden Elde Edilen Moleküllerin Yara İyileştirme Aktivitesi, Toksisitesi ve Farmakokinetik Profilleri Üzerine in silico Araştırmalar

in silico Investigations on Wound Healing Activity, Toxicity and Pharmacokinetic Profiles of Molecules Obtained from Cotinus Coggygria Plant

Çiğdem Bilkan(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ)0000-0002-3347-673X
Burhan Ertekin(Tokat Gaziosmanpaşa Üniversitesi Tıp Fakültesi)0000-0003-2804-047X
Elçin Özgür Büyükatalay(GAZİ ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0001-6428-918X
Mustafa Tuğfan Bilkan(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0002-0306-1509
Sayfa
99–111
Yayın Tarihi
2025-12-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Çalışma sonuçları, bitkinin yara iyileştirici etkilerinin çoğunlukla, -6,12 kcal/mol, -5,58 kcal/mol ve -5,50 kcal/mol etkileşim enerji değerlerine sahip butin, sülfüretin ve fisetin moleküllerinin, ilgili yolak TGF-β proteini ile etkileşimlerinden kaynaklandığını ortaya koymaktadır.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Halk arasında "duman ağacı" olarak da bilinen Cotinus coggygria bitkisinin, yara iyileşme mekanizmaları üzerine olumlu etkileri bulunmaktadır.
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Temel Bulgular & Veri:Ayrıca bu moleküllerden hangisinin yara iyileşme mekanizmaları üzerinde daha etkili olduğunun belirlenmesi de hedeflenmiştir.
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Sonuç & Katkı:Çalışma sonuçları, bitkinin yara iyileştirici etkilerinin çoğunlukla, -6,12 kcal/mol, -5,58 kcal/mol ve -5,50 kcal/mol etkileşim enerji değerlerine sahip butin, sülfüretin ve fisetin moleküllerinin, ilgili yolak TGF-β proteini ile etkileşimlerinden kaynaklandığını ortaya koymaktadır.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2000 Verileri
⚙️Değişkenler:Cotinus coggygria · Yara iyileşmesi · in silico araştırmalar

Özet

Halk arasında "duman ağacı" olarak da bilinen Cotinus coggygria bitkisinin, yara iyileşme mekanizmaları üzerine olumlu etkileri bulunmaktadır. Bu çalışmada, C. coggygria bitkisinin yapraklarından elde edilen ekstrakt içerisinde bulunan Gallik asit, Mirisetin, Kuersetin, Fisetin, Sülfüretin, Butin ve Taksifolin bileşiklerinin toksisite ve farmakokinetik profillerinin in silico yöntemlerle belirlenmesi amaçlanmıştır. Ayrıca bu moleküllerden hangisinin yara iyileşme mekanizmaları üzerinde daha etkili olduğunun belirlenmesi de hedeflenmiştir. Bileşiklerin toksisite tahminleri, hayvan deneylerine hızlı ve ucuz bir alternatif olan in silico modelleme teknikleri ile gerçekleştirilmiştir. Farmakokinetik özellikler de yapı-aktivite ilişkilerinden yararlanılarak elde edilmiştir. Ayrıca, her bileşiğin yara iyileştirme potansiyelleri, moleküler yerleştirme analizleri ile belirlenmiştir. Çalışma sonuçları, gallik asit, butin ve taksifolin moleküllerinin incelenen bileşikler arasında yaklaşık 2000 mg/kg ile en yüksek LD50 değerlerine (en düşük toksisite) sahip olduğunu göstermiştir. Çalışma sonuçları, bitkinin yara iyileştirici etkilerinin çoğunlukla, -6,12 kcal/mol, -5,58 kcal/mol ve -5,50 kcal/mol etkileşim enerji değerlerine sahip butin, sülfüretin ve fisetin moleküllerinin, ilgili yolak TGF-β proteini ile etkileşimlerinden kaynaklandığını ortaya koymaktadır.

Abstract

Cotinus coggygria, also known as the "smoke tree" among the public, positively affects wound healing mechanisms. In this study, it was aimed to determine the toxicity and pharmacokinetic profiles of Gallic acid, Myricetin, Quercetin, Fisetin, Sulfuretin, Butin, and Taxifolin compounds found in the extract obtained from the leaves of the Cotinus coggygria plant by in silico methods. It also aimed to determine which of these molecules are more effective in wound healing. Toxicity predictions of the compounds were performed using in silico modeling techniques. Pharmacokinetic properties were also obtained by utilizing structure-activity relationships. In addition, the wound-healing potentials of each compound were determined by molecular docking analyses. The study results showed that gallic acid, butine, and taxifolin molecules had the highest LD50 values (lowest toxicity) among the studied compounds, with approximately 2000 mg/kg. The study results also revealed that the wound-healing effects of the plant were primarily due to butine, sulfuretin, and fisetin molecules with energy values of -6.12 kcal/mol, -5.58 kcal/mol, and -5.50 kcal/mol, respectively, from their interactions with the relevant pathway, TGF-β protein. The toxicity and wound-healing effects of molecules found in the extract obtained from the leaves of the Cotinus coggygria plant were determined, thus providing useful preliminary information for future studies and medical research with these molecules.

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Bu Makaleye Atıf Yap

Bilkan, Ç., Ertekin, B., Büyükatalay, E. Ö., Bilkan, M. T. (2025). Cotinus Coggygria Bitkisinden Elde Edilen Moleküllerin Yara İyileştirme Aktivitesi, Toksisitesi ve Farmakokinetik Profilleri Üzerine in silico Araştırmalar. Türk Doğa ve Fen Dergisi, Cilt: 14 Sayı: 4, 99–111. https://doi.org/10.46810/tdfd.1647612

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Kaynakça (38)

  1. Rahman, S, Jan, G, Jan, FG, Rahim HU (2022) Phytochemical Investigation and Therapeutical Potential of Cotinus coggygria Scop. in Alloxan-Induced Diabetic Mice. Oxid Med Cell Longev. 8802178. https://doi.org/ 10.1155/2022/8802178
  2. Gospodinova Z, Zupko I, Bozsity N, Monava VI, Georgieva MS, Todinova SJ et al (2021) Cotinus coggygria Scop. induces cell cycle arrest, apoptosis, genotoxic effects, thermodynamic and epigenetic events in MCF7 breast cancer cells. Z. Naturforsch 76(3–4)c, 129–140. https://doi.org/ 10.1515/znc-2020-0087
  3. Sen A, Ertas B, Cevik O, Yıldırım A, Gokceoglu Kayalı D, Akakın D, Bitis L, Sener G (2023) Cotinus coggygria Scop. Attenuates Acetic Acid‐Induced Colitis in Rats by Regulation of Inflammatory Mediators. Applied Biochemistry and Biotechnology 195: 7021–7036 https://doi.org/ 10.1007/s12010-023-04474-1
  4. Kremer M, Burkemper N (2023) Aging Skin and Wound Healing. Clin Geriatr Med. 40(1):1-10. https://doi.org/ 10.1016/j.cger.2023.06.001
  5. Knoedler S, Knoedler L, Kauke-Navarro M, Rinkevich Y et al (2023) Regulatory T cells in skin regeneration and wound healing. Military Medical Research 10:49. https://doi.org/10.1186/s40779-023-00484-6
  6. Pena OA, Martin P (2024) Cellular and molecular mechanisms of skin wound healing. Nature Reviews Molecular Cell Biology 25:599-616. https://doi.org/10.1038/s41579-024-01035-z
  7. Liang Z, Lai P, Zhang J, Lai Q, He L (2023) Impact of moist wound dressing on wound healing time: A meta-analysis. Int Wound J. 20(10):4410-4421. https://doi.org/ 10.1111/iwj.14319
  8. Huelsboemer L, Knoedler L, Kochen A, Yu CT et al (2024) Cellular therapeutics and immunotherapies in wound healing – on the pulse of time? Military Medical Research 11:23. https://doi.org/10.1186/s40779-024-00528-5
  9. Cedillo-Cortezano M, Martinez-Cuevas LB, Lopez JAM et al (2024) Use of Medicinal Plants in the Process of Wound Healing: A Literature Review. Pharmaceuticals 17:303. https://doi.org/10.3390/ph17030303
  10. Pathak D, Mazumder A (2024) A critical overview of challenging roles of medicinal plants in improvement of wound healing technology. DARU Journal of Pharmaceutical Sciences 32:379–419. https://doi.org/10.1007/s40199-023-00502-x
  11. Philipp, K., Riedel, F., Sauerbier, M., Hörmann, K., & Germann, G. (2004). Targeting TGF-beta in human keratinocytes and its potential role in wound healing. International journal of molecular medicine, 14(4), 589-682.
  12. Frisch, M., et al. (2009) Gaussian 09. Revision E.01, Gaussian Inc., Wallingford CT.
  13. Banerjee, P., Kemmler, E., Dunkel, M., & Preissner, R. (2024). ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, gkae303.
  14. Lee, S.K., G.S.Chang, I.H.Lee, J.E.Chung, K.Y.Sung, K.T.No, “The PreADME: Pc-Based Program For Batch Predıctıon Of Adme Propertıes“, EuroQSAR 2004, 2004, 9.5-10, Istanbul, Turkey.
  15. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 23(1-3), 3-25.
  16. Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of combinatorial chemistry, 1(1), 55-68.
  17. Walters, W. P., & Murcko, M. A. (2002). Prediction of ‘drug-likeness’. Advanced drug delivery reviews, 54(3), 255-271.
  18. Walter M, Borghardt JM, Humbecj L, Skalic M (2024) Multi-Task ADME/PK prediction at industrial scale:leveraging large and diverse experimentaldatasets. Molecular informatics 43:e202400079. https://doi.org/10.1002/minf.202400079
  19. Ruswanto R, Nofianti T, Lestari T, Septian AD, Firmansyah AP, Mardianingeum R (2024) Potential Active Compounds of Propolis as Breast Anticancer Candidates: In Silico Study. JJBS 17:153-161. https://doi.org/10.54319/jjbs/170115
  20. Ma, X. L., Chen, C., & Yang, J. (2005). Predictive model of blood-brain barrier penetration of organic compounds. Acta Pharmacologica Sinica, 26(4), 500-512.
  21. Yamashita, S., Furubayashi, T., Kataoka, M., Sakane, T., Sezaki, H., & Tokuda, H. (2000). Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells. European journal of pharmaceutical sciences, 10(3), 195-204.
  22. Esaki T, Yonezawa T, Ikeda K (2024) A new workfow for the efective curation of membrane permeability data from open ADME information. Journal of Cheminformatics 16:30. https://doi.org/10.1186/s13321-024-00826-z
  23. Bokulic A, Padovan J, Stupin-Polancec D, Milic A (2022) Isolation of MDCK cells with low expression of mdr1 gene and their use in membrane permeability screening. Acta Pharm. 75: 275-288. https://doi.org/10.2478/acph-2022-0003
  24. Hassan MA, Sadiya AH, Jafaru KE, Sunday HG (2023). In-silico Investigation of Potential Inhibitors of 11-β-Hydroxysteroid Dehydrogenase. AJBGMB 15:10-23 https://doi.org/ 10.9734/AJBGMB/2023/v15i2329
  25. Han Z, Xia Z, Xia J, Tetko IV, Wu S (2024). The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation. bioRxiv https://doi.org/10.1101/2024.07.12.603170
  26. Bergström CAS, Larsson P (2018) Computational prediction of drug solubility in water-based systems: Qualitative and quantitative approaches used in the current drug discovery and development setting. Int J Pharm. 540(1-2):185-193. https://doi.org/ 10.1016/j.ijpharm.2018.01.044
  27. Idakwo G, Luttrell J, Chen M, Hong H, Zhou Zhaoxian et al (2018) A review on machine learning methods for in silicotoxicity prediction. Journal of Envıronmental Scıence And Health 36:169-191. https://doi.org/10.1080/10590501.2018.1537118
  28. Bilkan MT, Bilkan Ç (2024) The Determination of Toxicity and Pharmacokinetic Parameters of Some Important Central Nervous System Depressants by Using In Silico Methods. Healt and Science 2024-I, 29-42.
  29. Rim KT (2020) In silico prediction of toxicity and its applications for chemicals at work. Toxicology and Environmental Health Sciences 12:191–202. https://doi.org/10.1007/s13530-020-00056-4
  30. Noga M, Michalska A, Jurowski K (2024). The prediction of acute toxicity (LD50) for organophosphorus based chemical warfare agents (V series) using toxicology in silico methods Archives of Toxicology 98:267–275. 98:267–275 https://doi.org/10.1007/s00204-023-03632-y
  31. Barrientos, S., Stojadinovic, O., Golinko, M. S., Brem, H., & Tomic-Canic, M. (2008). Transforming growth factor beta and wound healing. Wound Repair and Regeneration, 16(5), 585–601.
  32. Pakyari, M., Farrokhi, A., Maharlooei, M. K., & Ghahary, A. (2013). Critical Role of Transforming Growth Factor Beta in Different Phases of Wound Healing. Advances in Wound Care, 2(5), 215–224.
  33. Finnson, K. W., McLean, S., Di Guglielmo, G. M., & Philip, A. (2013). Dynamics of Transforming Growth Factor Beta Signaling in Wound Healing and Scarring. Advances in Wound Care, 2(5), 195–214.
  34. Kim, M., Choi, Y. S., Song, H. H., & Bae, Y. S. (2016). β-Lapachone regulates the transforming growth factor-β–Smad signaling pathway associated with collagen biosynthesis in human dermal fibroblasts. Biological and Pharmaceutical Bulletin, 39(4), 544–550.
  35. Ashcroft, G. S., Yang, X., Glick, A. B., Weinstein, M., Letterio, J. J., Mizel, D. E.,. & Roberts, A. B. (1999). Mice lacking Smad3 show accelerated wound healing and an impaired local inflammatory response. Nature Cell Biology, 1(5), 260–266.
  36. Shah, M., Foreman, D. M., & Ferguson, M. W. J. (1995). Neutralisation of TGF-β1 and TGF-β2 or exogenous addition of TGF-β3 to cutaneous rat wounds reduces scarring. Journal of Cell Science, 108(3), 985–1002.
  37. Seo, H. H., Lee, S., Lee, C. Y., Park, M. S., & Kim, H. S. (2017). KGF-1 accelerates wound contraction through the TGF-β1/Smad signaling pathway in a double-paracrine manner. Journal of Biological Chemistry, 292(51), 20938–20950/
  38. Ashcroft, G. S., Yang, X., Glick, A. B., Weinstein, M., Letterio, J. J., Mizel, D. E.,. & Roberts, A. B. (1999). Mice lacking Smad3 show accelerated wound healing and an impaired local inflammatory response. Nature Cell Biology, 1(5), 260–266.