Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 24 Sayı: 4

Imidazole and Quinoline-Based Promising Agent for Cancer Treatment; Synthesis, Characterization, and Computational Calculations

İmidazol ve Kinolin Bazlı Kanser Tedavisi için Ümit Verici Ajan; Sentezi, Karakterizasyonu ve Bilgisayarsal Hesaplamaları

Tolga Acar Yeşil(SİNOP ÜNİVERSİTESİ, TÜRKELİ MESLEK YÜKSEKOKULU)0000-0001-5983-8447
Ömer Dilek(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0003-1409-782X
Tahir Tilki(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0002-1040-2375
Pages
798–810
Publication Date
2024-08-20
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

From ADMET and docking studies, it was concluded that the MITPDQ has the potential to be a drug candidate after further investigations were done related with this field.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a novel imidazole and quinoline-based azo compound (MITPDQ) was synthesized, starting from aniline derivative which was used as an intermediate to synthesize nilotinib, which was used in leukemia treatment, characterized, and its structure was elucidated with spectroscopic techniques such as NMR, FTIR, UV, FTIR, and MS.
📊
Key Findings & Data:Theoretical calculations using DFT (B3LYP) method and 6-311G (d,p) basis set were done to obtain optimized geometry and spectral data of MITPDQ.
💡
Conclusion & Impact:From ADMET and docking studies, it was concluded that the MITPDQ has the potential to be a drug candidate after further investigations were done related with this field.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:İmidazol · Kinolin · Kuantum Kimyasal Hesaplamalar

Abstract

In this study, a novel imidazole and quinoline-based azo compound (MITPDQ) was synthesized, starting from aniline derivative which was used as an intermediate to synthesize nilotinib, which was used in leukemia treatment, characterized, and its structure was elucidated with spectroscopic techniques such as NMR, FTIR, UV, FTIR, and MS. Theoretical calculations using DFT (B3LYP) method and 6-311G (d,p) basis set were done to obtain optimized geometry and spectral data of MITPDQ. Experimental results were compared with theoretical ones and it was observed that they were compatible with each other. Using the optimized geometry of MITPDQ, the molecular docking studies were also conducted with cancer-related proteins. From docking results, the highest docking score was found to be -11.0 kcal/mol between MITPDQ and 2XIR protein. Also, the ADMET properties of MITPDQ were calculated. From ADMET and docking studies, it was concluded that the MITPDQ has the potential to be a drug candidate after further investigations were done related with this field.

Özet

Bu çalışmada lösemi tedavisinde kullanılan nilotinibin sentezinde ara madde olarak kullanılan anilin türevinden yola çıkılarak yeni bir imidazol ve kinolin bazlı azo bileşiği (MITPDQ) sentezlendi ve sentezlenen maddenin yapısı NMR, FTIR, UV, FTIR ve MS gibi tekniklerle karakterize edildi. MITPDQ'nun optimize edilmiş geometrisini ve spektral verilerini elde etmek için DFT (B3LYP) yöntemi ve 6-311G (d,p) temel seti kullanılarak teorik hesaplamalar yapıldı. Deneysel sonuçlar teorik sonuçlarla karşılaştırıldı ve birbirleriyle uyumlu oldukları görüldü. MITPDQ'nun optimize edilmiş geometrisi kullanılarak, kanserle ilişkili proteinlerle de moleküler yerleştirme çalışmaları gerçekleştirildi. Yerleştirme sonuçlarından en yüksek yerleştirme puanının MITPDQ ile 2XIR proteini arasında -11,0 kcal/mol olduğu bulundu. Ayrıca MITPDQ'nun ADMET özellikleri de hesaplandı. ADMET ve Moleküler yerleştirme çalışmalarından bu alanla ilgili ileri araştırmalar yapılarak MITPDQ'nun ilaç adayı olma potansiyeline sahip olduğu sonucuna varıldı.

🔖

Cite This Article

Yeşil, T. A., Dilek, Ö., Tilki, T. (2024). Imidazole and Quinoline-Based Promising Agent for Cancer Treatment; Synthesis, Characterization, and Computational Calculations. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 24 Sayı: 4, 798–810. https://doi.org/10.35414/akufemubid.1432554

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References (49)

  1. Ahadi, H., & Emami, S., 2020. Modification of 7-piperazinylquinolone antibacterials to promising anticancer lead compounds: Synthesis and in vitro studies. European Journal of Medicinal Chemistry, 187, 111970. https://doi.org/10.1016/j.ejmech.2019.111970
  2. Akkachairin, B., Rodphon, W., Reamtong, O., Mungthin, M., Tummatorn, J., Thongsornkleeb, C., & Ruchirawat, S., 2020. Synthesis of neocryptolepines and carbocycle-fused quinolines and evaluation of their anticancer and antiplasmodial activities. Bioorganic Chemistry, 98, 103732. https://doi.org/10.1016/j.bioorg.2020.103732
  3. Ames, B. N., Lee, F. D., & Durston, W. E., 1973. An improved bacterial test system for the detection and classification of mutagens and carcinogens. Proceedings of the National Academy of Sciences of the United States of America, 70(3), 782–786. https://doi.org/10.1073/pnas.70.3.782
  4. Bauernschmitt, R., & Ahlrichs, R., 1996. Treatment of electronic excitations within the adiabatic approximation of time dependent density functional theory. Chemical Physics Letters, 256(4–5), 454–464. https://doi.org/10.1016/0009-2614(96)00440-X
  5. Becke, A. D., 1988. Density-functional exchange-energy approximation with correct asymptotic behavior. Physical Review A, 38(6), 3098–3100. https://doi.org/10.1103/PhysRevA.38.3098
  6. Berman, H. M., 2000. The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
  7. BIOVIA, 2021 Discovery Studio Visualizer, version 21.1.0.20298. Dassault Systèmes, San Diego, CA.
  8. Bueno, J., 2015. Antimicrobial Models in Nanotechnology. In Nanotechnology in Diagnosis, Treatment and Prophylaxis of Infectious Diseases 19–38. Elsevier. https://doi.org/10.1016/B978-0-12-801317-5.00002-5
  9. Carugo, A., & Draetta, G. F., 2019. Academic Discovery of Anticancer Drugs: Historic and Future Perspectives. Annual Review of Cancer Biology, 3(1), 385–408. https://doi.org/10.1146/annurev-cancerbio-030518-055645
  10. Casida, M. E., Jamorski, C., Casida, K. C., & Salahub, D. R., 1998. Molecular excitation energies to high-lying bound states from time-dependent density-functional response theory: Characterization and correction of the timedependent local density approximation ionization threshold. The Journal of Chemical Physics, 108(11), 4439–4449. https://doi.org/10.1063/1.475855
  11. Choi, H. G., Ren, P., Adrian, F., Sun, F., Lee, H. S., Wang, X., Ding, Q., Zhang, G., Xie, Y., Zhang, J., Liu, Y., Tuntland, T., Warmuth, M., Manley, P. W., Mestan, J., Gray, N. S., & Sim, T., 2010. A Type-II Kinase Inhibitor Capable of Inhibiting the T315I “Gatekeeper” Mutant of Bcr-Abl. Journal of Medicinal Chemistry, 53(15), 5439–5448. https://doi.org/10.1021/jm901808w
  12. Debela, D. T., Muzazu, S. G., Heraro, K. D., Ndalama, M. T., Mesele, B. W., Haile, D. C., Kitui, S. K., & Manyazewal, T., 2021. New approaches and procedures for cancer treatment: Current perspectives. SAGE Open Medicine, 9, 205031212110343. https://doi.org/10.1177/20503121211034366
  13. Ditchfield, R., 1972. Molecular Orbital Theory of Magnetic Shielding and Magnetic Susceptibility. The Journal of Chemical Physics, 56(11), 5688–5691. https://doi.org/10.1063/1.1677088
  14. Faudone, G., Zhubi, R., Celik, F., Knapp, S., Chaikuad, A., Heering, J., & Merk, D., 2022. Design of a Potent TLX Agonist by Rational Fragment Fusion. Journal of Medicinal Chemistry, 65(3), 2288–2296. https://doi.org/10.1021/acs.jmedchem.1c01757
  15. Ferreira, L. L. G., & Andricopulo, A. D., 2019. ADMET modeling approaches in drug discovery. Drug Discovery Today, 24(5), 1157–1165. https://doi.org/10.1016/j.drudis.2019.03.015
  16. Frisch, M. J., G. W., Trucks, H. B., Schlegel, G. E., Scuseria, M. A., Robb, J. R., Cheeseman, G., Scalmani, V., Barone, G. A., Petersson, H., Nakatsuji, X., Li, M., Caricato, A., Marenich, J., Bloino, B. G., Janesko, R., Gomperts, B., Mennucci, H. P., Hratchian, J. V., Ortiz, A. F I., D. J. F. 2016. Gaussian 09, Revision E.01. Gaussian, Inc.
  17. Güney, Y., Dilek, Ö., Sezgin, B., & Tilki, T., 2023. Exploring the Potential of Azo Compounds in Leukemia Treatment: Synthesis and Characterization of New Derivatives with Dimedone and Meldrum’s Acid End Groups. ChemistrySelect, 8(35), e20230264. https://doi.org/10.1002/slct.202302642
  18. Hallek, M., 2019. Chronic lymphocytic leukemia: 2020 update on diagnosis, risk stratification and treatment. American Journal of Hematology, 94(11), 1266–1287. https://doi.org/10.1002/ajh.25595
  19. Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R., 2012. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17
  20. Heravi, M. M., & Zadsirjan, V., 2020. Prescribed drugs containing nitrogen heterocycles: an overview. RSC Advances, 10(72), 44247–44311. https://doi.org/10.1039/D0RA09198G
  21. Hnatiuk, A. P., Bruyneel, A. A. N., Tailor, D., Pandrala, M., Dheeraj, A., Li, W., Serrano, R., Feyen, D. A. M., Vu, M. M., Amatya, P., Gupta, S., Nakauchi, Y., Morgado, I., Wiebking, V., Liao, R., Porteus, M. H., Majeti, R., Malhotra, S. V., & Mercola, M., 2022. Reengineering Ponatinib to Minimize Cardiovascular Toxicity. Cancer Research, 82(15), 2777–2791. https://doi.org/10.1158/0008-5472.CAN-21-3652
  22. Hou, T., Wang, J., & Li, Y., 2007. ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine. Journal of Chemical Information and Modeling, 47(6), 2408–2415. https://doi.org/10.1021/ci7002076
  23. Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., & De Fabritiis, G., 2017. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 33(19), 3036–3042. https://doi.org/10.1093/bioinformatics/btx350
  24. Jung, H., Kim, J., Im, D., Moon, H., & Hah, J.-M., 2019. Design, synthesis, and in vitro evaluation of N-(3-(3-alkyl-1H-pyrazol-5-yl) phenyl)-aryl amide for selective RAF inhibition. Bioorganic & Medicinal Chemistry Letters, 29(4), 534–538. https://doi.org/10.1016/j.bmcl.2019.01.003
  25. Kalinichenko, E., Faryna, A., Bozhok, T., Golyakovich, A., & Panibrat, A., 2023. Novel Phthalic-Based Anticancer Tyrosine Kinase Inhibitors: Design, Synthesis and Biological Activity. Current Issues in Molecular Biology, 45(3), 1820–1842. https://doi.org/10.3390/cimb45030117
  26. Kalinichenko, E., Faryna, A., Bozhok, T., & Panibrat, A., 2021. Synthesis, In Vitro and In Silico Anticancer Activity of New 4-Methylbenzamide Derivatives Containing 2,6-Substituted Purines as Potential Protein Kinases Inhibitors. International Journal of Molecular Sciences, 22(23), 12738. https://doi.org/10.3390/ijms222312738
  27. Kalinichenko, E., Faryna, A., Kondrateva, V., Vlasova, A., Shevchenko, V., Melnik, A., Avdoshko, O., & Belko, A., 2019. Synthesis, Biological Activities and Docking Studies of Novel 4-(Arylaminomethyl)benzamide Derivatives as Potential Tyrosine Kinase Inhibitors. Molecules, 24(19), 3543. https://doi.org/10.3390/molecules24193543
  28. Karabacak Atay, Ç., Dilek, Ö., Tilki, T., & Dede, B., 2023. A novel imidazole-based azo molecule: synthesis, characterization, quantum chemical calculations, molecular docking, molecular dynamics simulations and ADMET properties. Journal of Molecular Modeling, 29(8), 226. https://doi.org/10.1007/s00894-023-05625-1
  29. Kumar, A., Singh, A. K., Singh, H., Vijayan, V., Kumar, D., Naik, J., Thareja, S., Yadav, J. P., Pathak, P., Grishina, M., Verma, A., Khalilullah, H., Jaremko, M., Emwas, A.-H., & Kumar, P., 2023. Nitrogen Containing Heterocycles as Anticancer Agents: A Medicinal Chemistry Perspective. Pharmaceuticals, 16(2), 299. https://doi.org/10.3390/ph16020299
  30. Lee, C., Yang, W., & Parr, R. G., 1988. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B, 37(2), 785–789. https://doi.org/10.1103/PhysRevB.37.785
  31. Lipinski, C. A., 2004. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
  32. Lu, X., Zhang, Z., Ren, X., Pan, X., Wang, D., Zhuang, X., Luo, J., Yu, R., & Ding, K., 2015. Hybrid pyrimidine alkynyls inhibit the clinically resistance related Bcr-AblT315I mutant. Bioorganic & Medicinal Chemistry Letters, 25(17), 3458–3463. https://doi.org/10.1016/j.bmcl.2015.07.006
  33. Marella, A., Tanwar, O. P., Saha, R., Ali, M. R., Srivastava, S., Akhter, M., Shaquiquzzaman, M., & Alam, M. M., 2013. Quinoline: A versatile heterocyclic. Saudi Pharmaceutical Journal, 21(1), 1–12. https://doi.org/10.1016/j.jsps.2012.03.002
  34. McCann, J., Choi, E., Yamasaki, E., & Ames, B. N., 1975. Detection of carcinogens as mutagens in the Salmonella/microsome test: assay of 300 chemicals. Proceedings of the National Academy of Sciences, 72(12), 5135–5139. https://doi.org/10.1073/pnas.72.12.5135
  35. Merrick, J. P., Moran, D., & Radom, L., 2007. An Evaluation of Harmonic Vibrational Frequency Scale Factors. The Journal of Physical Chemistry A, 111(45), 11683–11700. https://doi.org/10.1021/jp073974n
  36. Onakpoya, I. J., Heneghan, C. J., & Aronson, J. K., 2016. Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature. BMC Medicine, 14(1), 10. https://doi.org/10.1186/s12916-016-0553-2
  37. Pandrala, M., Bruyneel, A. A. N., Hnatiuk, A. P., Mercola, M., & Malhotra, S. V., 2022. Designing Novel BCR-ABL Inhibitors for Chronic Myeloid Leukemia with Improved Cardiac Safety. Journal of Medicinal Chemistry, 65(16), 10898–10919. https://doi.org/10.1021/acs.jmedchem.1c01853
  38. Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E., 2004. UCSF Chimera - A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
  39. Rashid, H. ur, Xu, Y., Muhammad, Y., Wang, L., & Jiang, J., 2019. Research advances on anticancer activities of matrine and its derivatives: An updated overview. European Journal of Medicinal Chemistry, 161, 205–238. https://doi.org/10.1016/j.ejmech.2018.10.037
  40. Siegel, R. L., Miller, K. D., Wagle, N. S., & Jemal, A., 2023. Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(1), 17–48. https://doi.org/10.3322/caac.21763
  41. Singh, N., Vayer, P., Tanwar, S., Poyet, J.-L., Tsaioun, K., & Villoutreix, B. O., 2023. Drug discovery and development: introduction to the general public and patient groups. Frontiers in Drug Discovery, 3, 1201419. https://doi.org/10.3389/fddsv.2023.1201419
  42. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F., 2021. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249. https://doi.org/10.3322/caac.21660
  43. Trott, O., & Olson, A. J., 2009. 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. https://doi.org/10.1002/jcc.21334
  44. Verma, A., Joshi, S., & Singh, D., 2013. Imidazole: Having Versatile Biological Activities. Journal of Chemistry, 2013, 1–12. https://doi.org/10.1155/2013/329412
  45. Wolinski, K., Hinton, J. F., & Pulay, P., 1990. Efficient implementation of the gauge-independent atomic orbital method for NMR chemical shift calculations. Journal of the American Chemical Society, 112(23), 8251–8260. https://doi.org/10.1021/ja00179a005
  46. Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D., 2021. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 49(W1), W5–W14. https://doi.org/10.1093/nar/gkab255
  47. Yadav, P., & Shah, K., 2021. Quinolines, a perpetual, multipurpose scaffold in medicinal chemistry. Bioorganic Chemistry, 109, 104639. https://doi.org/10.1016/j.bioorg.2021.104639
  48. Zhu, D., Huang, H., Pinkas, D. M., Luo, J., Ganguly, D., Fox, A. E., Arner, E., Xiang, Q., Tu, Z.-C., Bullock, A. N., Brekken, R. A., Ding, K., & Lu, X., 2019. 2-Amino-2,3-dihydro-1H-indene-5-carboxamide-Based Discoidin Domain Receptor 1 (DDR1) Inhibitors: Design, Synthesis, and in Vivo Antipancreatic Cancer Efficacy. Journal of Medicinal Chemistry, 62(16), 7431–7444. https://doi.org/10.1021/acs.jmedchem.9b00365
  49. Zhurko, G. A. and Zhurko, D. A. ChemCraft, Tool for treatment of the chemical data, version 1.8; www.chemcraftprog.com