ANKİLOZAN SPONDİLİT TEDAVİSİNDE İLAÇ YENİDEN YERLEŞTİRME YAKLAŞIMI

Yıl 2022, , 896 - 908, 30.09.2022

Gozde Yalcin

https://doi.org/10.33483/jfpau.1150706

Öz

Amaç: Bu çalışmada, AS'de inflamasyonun önlenmesinde önemli bir hedef olan IL-17 reseptörünü inhibe eden FDA onaylı bir molekülün ilaç yeniden konumlandırma yaklaşımı kullanılarak belirlenmesi amaçlanmıştır.
Gereç ve Yöntem: “Drug-Gene Interaction” veritabanı kullanılarak AS'de etkin HLA-B genine özgü 18 molekül belirlenmiştir. Ardından IL-17'nin 3D yapısına RSCB veri tabanından ulaşılmıştır. Bağlanma paketinin belirlenmesi için I) Kör kenetlenme II) “Computed Atlas of Surface Topography of Proteins” web aracı kullanılmıştır. Belirlenen bağlanma paketleri çevresindeki grid kutuları kullanılarak IL-17'nin bilinen inhibitörü rhodomyrtone ile IL-17 arasındaki etkileşim moleküler doking ile belirlenmiştir. Buna göre seçilen grid kutusu özellikleri ile konfigürasyon dosyaları hazırlanarak 18 molekül için de AutoDock Vina programı ile doking gerçekleştirilmiştir.
Sonuç ve Tartışma: Karbamazepin molekülü, IL-17 ile en iyi bağlanma afinitesini ve bağlanma profilini göstermiştir. Ayrıca minosiklin, sülfasalazin ve talidomidin moleküllerinin de aktif bölgede sıkıca paketlendiği ortaya çıkmıştır. Bu moleküllerin AS hastalığının tedavisi için bir öncü molekül olabileceği gösterilmiştir.

Anahtar Kelimeler

Ankilozan Spondilit, IL-17, ilaç yeniden konumlandırma, kör kenetlenme, moleküler kenetlenme

DRUG REPOSITIONING APPROACH FOR THE TREATMENT OF ANKYLOSING SPONDYLITIS

Öz

Objective: In this study, it was aimed to determine an FDA-approved molecule that inhibits the IL-17 receptor, which is an important target for the prevention of inflammation in Ankylosing Spondylitis (AS), using the drug repositioning approach.
Material and Method: Using the Drug-Gene Interaction database, 18 molecules specific to the active HLA-B gene were identified in AS. Then, the 3D structure of IL-17 was obtained from the RSCB database. I) Blind docking II) Computed Atlas of Surface Topography of Proteins web tool was used to determine the binding package. The interaction between the known inhibitor of IL-17, rhodomyrtone, and IL-17, was determined by molecular docking using grid boxes around the determined binding packages. Accordingly, configuration files were prepared with the selected grid box features, and docking was performed for 18 molecules with the AutoDock Vina program.
Result and Discussion: The carbamazepine molecule shows the best binding affinity and binding profile with IL-17. It was also revealed that minocycline, sulfasalazine, and thalidomide are tightly packed in the active site. It has been demonstrated that these molecules may be lead molecules for the treatment of AS disease.

Anahtar Kelimeler

Ankylosing Spondylitis, blind docking, drug repositioning, IL-17, molecular docking

Kaynakça

• Ebrahimiadib, N., Berijani, S., Ghahari, M., Pahlaviani, F.G. (2021). Ankylosing spondylitis. Journal of Ophthalmic and Vision Research, 16(3), 462-469. [CrossRef]
• 2. Cornell, T. (2004). Ankylosing spondylitis: an overview. Journal of Professional Nursing, 19(8), 431-432.
• 3. Hwang, M. C., Ridley, L., Reveille, J. D. (2021). Ankylosing spondylitis risk factors: a systematic literature review. Clinical Rheumatology, 40(8), 3079-3093. [CrossRef]
• 4. Jung, J. H., Bang, C. H., Seok, H., Choi, S. J., Song, G. G. (2019). Clinical findings of Ankylosing Spondylitis with and without Human Leukocyte Antigen (HLA)-B27 and HLA-B51. Annals of the Academy of Medicine of Singapore, 48(10), 321-329.
• 5. Asquith, M., Sternes, P.R., Costello, M.E., Karstens, L., Diamond, S., Martin, T. M., Li, Z., Marshall, M. S., Spector, T. D., Cao, K. A., Rosenbaum, J. T., Brown, M. A. (2019). HLA Alleles associated with risk of Ankylosing Spondylitis and Rheumatoid Arthritis influence the Gut Microbiome. Arthritis & Rheumatology, 71(10), 1642-1650. [CrossRef]
• 6. Hu, N., Liu, D., Zhao, N., Wang, X., Bai, Y., Sun, H. (2021). Lack of association between TNF polymorphism and ankylosing spondylitis susceptibility in HLA-B27-positive population: a meta-analysis. European Spine Journal, 30(8), 2401-2408. [CrossRef]
• 7. Simone, D., Al Mossawi, M. H., Bowness, P. (2018). Progress in our understanding of the pathogenesis of ankylosing spondylitis. Rheumatology (Oxford), 57(6), 4-9. [CrossRef]
• 8. Watad, A., Bridgewood, C., Russell, T., Marzo-Ortega, H., Cuthbert, R., McGonagle, D. (2018). The early phases of ankylosing spondylitis: Emerging insights from clinical and basic science. Frontiers in Immunology, 9(NOV), 1-9. [CrossRef]
• 9. Garcia-Montoya, L., Gul, H., Emery, P. (2018). Recent advances in ankylosing spondylitis: Understanding the disease and management [version 1; peer review: 2 approved]. F1000Research, 7(0), 1-11. [CrossRef]
• 10. Pedersen, S.J., Maksymowych, W.P. (2019). The Pathogenesis of Ankylosing Spondylitis: An update. Current Rheumatology Reports, 21(10), 58. [CrossRef]
• 11. Yang, P., Wan, W., Du, L., Zhou, Q., Qi, J., Liang, L., Wang, C., Wu, L., Kijlstra, A. (2018). Clinical features of HLA-B27-positive acute anterior uveitis with or without ankylosing spondylitis in a Chinese cohort. British Journal of Ophthalmology, 102(2), 215-219. [CrossRef]
• 12. Choi, E.Y., Lee, M., Lee, C.S. (2020). Uveitis occurrence in patients with ankylosing spondylitis according to the type of tumour necrosis factor inhibitor: a cohort study of 175 patients. Clinical and Experimental Rheumatology, 38(6), 1132-1137.
• 13. The Standardization of Uveitis Nomenclature (SUN) Working Group. (2021). Classification Criteria for Spondyloarthritis/HLA-B27-Associated Anterior Uveitis. American Journal of Ophthalmology, 228, 117-125. [CrossRef]
• 14. Fan, M., Liu, J., Zhao, B., Wu, X., Li, X., Gu, J. (2020). Indirect comparison of NSAIDs for ankylosing spondylitis: Network meta-analysis of randomized, double-blinded, controlled trials. Experimental and Therapeutic Medicine, 3031-3041. [CrossRef]
• 15. Carbo, M. J. G., Spoorenberg, A., Maas, F., Brouwer, E., Bos, R., Bootsma H., Van der Veer, E., Wink, F., Arends, S. (2018). Ankylosing spondylitis disease activity score is related to NSAID use, especially in patients treated with TNF-α inhibitors. PLoS One, 13(4), 1-12. [CrossRef]
• 16. Yalcin-Ozkat, G., Yildiz, I. (2022). In silico studies to develop new GSK3 Inhibitors effective in the Alzheimer’s Disease. Letters in Drug Design & Discovery, 19(8), 691-705. [CrossRef]
• 17. Huylu, B., Yalcin, G. (2022). MS hastalığının tedavi̇si̇ne yönelik Sfingozin-1-Fosfat Reseptör Modülatörleri̇n geli̇şti̇ri̇lmesi̇. Konya Mühendislik Bilimleri Dergisi, 10(1), 102-114. [CrossRef]
• 18. Yalcin, G., Huylu, B. (2022). Development of new Cyclophilin D Receptor Inhibitors for the treatment of Multiple Sclerosis. Journal of Faculty of Pharmacy of Ankara University, 46(2), 458-473. [CrossRef]
• 19. Osman, M. S., Maksymowych, W. P. (2017). An update on the use of tumor necrosis factor alpha inhibitors in the treatment of ankylosing spondylitis. Expert Review of Clinical Immunology, 13(2), 125-131. [CrossRef]
• 20. Ma, Z., Liu, X., Xu, X., Jiang, J., Zhou, J., Wang, J., Chen, D., Luo S. (2017). Safety of tumor necrosis factor-alpha inhibitors for treatment of ankylosing spondylitis. Medicine (Baltimore), 96(25), e7145. [CrossRef]
• 21. Nigil Haroon, N., Sriganthan, J., Al Ghanim, N., Inman, R. D., Cheung, A. M. (2014). Effect of TNF-alpha inhibitor treatment on bone mineral density in patients with ankylosing spondylitis: A systematic review and meta-analysis. Semin Arthritis Rheumatology, 44(2), 155-161. [CrossRef]
• 22. Perrotta, F. M., Scriffignano, S., Ciccia, F., Lubrano, E. (2022). Therapeutic targets for Ankylosing Spondylitis – Recent insights and future prospects. Open Access Rheumatology: Research and Reviews, 14(April), 57-66. [CrossRef]
• 23. Armstrong, A., Fahrbach, K., Leonardi. C, Agustin, M., Neupane, B., Kazmierska, P., Betts, M., Freitag, A., Kiri, S., Taieb, V., Slim, M., Gomez, N. N., Warren, R. B. (2022). Efficacy of Bimekizumab and other biologics in moderate to Severe Plaque Psoriasis : A systematic literature review and a network meta-analysis. Dermatology and Therapy, 12(8), 1777-1792. [CrossRef]
• 24. Reich, K., Warren, R. B., Lebwohl, M., Gooderham, M., Strober, B., Langley, R. G., Paul, C., Cuyper, D. D., Vanvoorden, V., Madden, C., Cioffi, C., Peterson, L., Blauvelt, A. (2021). Bimekizumab versus Secukinumab in Plaque Psoriasis. The New England Journal of Medicine, 385(2), 142-152. [CrossRef]
• 25. Low, Z. Y., Farouk, I. A., Lal, S. K. (2020). Drug repositioning: New approaches and future prospects for life-debilitating diseases and the COVID-19 pandemic outbreak. Viruses, 12(9), 1058. [CrossRef]
• 26. Ngidi, N. T. P., Machaba, K. E., Mhlongo, N. N. (2022). In Silico drug repurposing approach: Investigation of Mycobacterium tuberculosis FadD32 targeted by FDA-Approved Drugs. Molecules, 27(3), 668. [CrossRef]
• 27. Pushpakom, S., Iorio, F., Eyers, P.A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., Norris, A., Sanseau, P., Cavalla, D., Pirmohammed, M. (2018). Drug repurposing: Progress, challenges and recommendations. Nature Reviews Drug Discovery, 18(1), 41-58. [CrossRef]
• 28. Freshour, S. L., Kiwala, S., Cotto, K.C., Coffman, A. C., McMichael, J. F., Song, J. J., Griffith, (2021). Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Research, 49(D1), D1144-D1151. [CrossRef]
• 29. Kim, S., Thiessen, P. A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., Bryant, S. H. (2016). PubChem Substance and Compound databases. Nucleic Acids Research, 44(D1), D1202-D1213. [CrossRef]
• 30. Trott, O., Olson, A. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461. [CrossRef]
• 31. Steffen, C., Thomas, K., Huniar, U., Hellweg, A., Rubner, O., Schroer, A. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of Computational Chemistry, 31(16), 2967-2970. [CrossRef]
• 32. Liu, S., Song, X., Chrunyk, B.A., Shanker, S., Hoth, L. R., Marr, E. S., Griffor, M. C. (2013). Crystal structures of interleukin 17A and its complex with IL-17 receptor A. Nature Communications, 4(May), 1-9. [CrossRef]
• 33. Berman, H. M., Westbrook, J., Feng, Z., Gilliand, G., Bhat, T. N., Weissig, H., Shindyalov, I., N., Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235-242. [CrossRef]
• 34. Tian, W., Chen, C., Lei, X., Zhao, J., Liang, J. (2018). CASTp 3.0 : Computed atlas of surface topography of proteins. Nucleic Acids Research, 46(June), 363-367. [CrossRef]
• 35. Hassan, N. M., Alhossary, A. A., Mu, Y., Kwoh, C. K. (2017). Protein-Ligand Blind Docking Using QuickVina-W with Inter-Process Spatio-Temporal Integration. Scientific Reports, 7(1), 1-13. [CrossRef]
• 36. Verma, A. K., Hossain, M. S., Ahmed, S. F., Hussain, N., Ashid, M., Upadhyay, S. K., Vıshakarma, N. K., Bhojiya A. A., Srivastava, S. K. (2022). In silico identification of ethoxy phthalimide pyrazole derivatives as IL-17A and IL-18 targeted gouty arthritis agents. Journal of Biomolecular Structure and Dynamics, 1-15. [CrossRef]
• 37. Dassault Systèmes. (2019). Discovery Studio Visualizer (No. 2019). BIOVIA.