3D-QSAR Design of New Bcr-Abl Inhibitors Based on Purine Scaffold and Cytotoxicity Studies on CML Cell Lines Sensitive and Resistant to Imatinib
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
1231199
Fondo Nacional de Desarrollo Científico y Tecnológico
23-05474S
Czech Science Foundation
IGA_PrF_20242025_005011
Palacký University Olomouc
ID No. LX22NPO5102
European Union - Next Generation EU
PubMed
40573320
PubMed Central
PMC12195648
DOI
10.3390/ph18060925
PII: ph18060925
Knihovny.cz E-zdroje
- Klíčová slova
- 3D-QSAR, Bcr-Abl inhibitors, chronic myeloid leukaemia, docking studies, molecular dynamics, purine derivatives,
- Publikační typ
- časopisecké články MeSH
Background/Objectives: Bcr-Abl inhibitors such as imatinib have been used to treat chronic myeloid leukemia (CML). However, the efficacy of these drugs has diminished due to mutations in the kinase domain, notably the T315I mutation. Therefore, in this study, new purine derivatives were designed as Bcr-Abl inhibitors based on 3D-QSAR studies. Methods: A database of 58 purines that inhibit Bcr-Abl was used to construct 3D-QSAR models. Using chemical information from these models, a small group of new purines was designed, synthesized, and evaluated in Bcr-Abl. Viability assays were conducted on imatinib-sensitive CML cells (K562 and KCL22) and imatinib-resistant cells (KCL22-B8). In silico analyses were performed to confirm the results. Results: Seven purines were easily synthesized (7a-g). Compounds 7a and 7c demonstrated the highest inhibition activity on Bcr-Abl (IC50 = 0.13 and 0.19 μM), surpassing the potency of imatinib (IC50 = 0.33 μM). 7c exhibited the highest potency, with GI50 = 0.30 μM on K562 cells and 1.54 μM on KCL22 cells. The GI50 values obtained for non-neoplastic HEK293T cells indicated that 7c was less toxic than imatinib. Interestingly, KCL22-B8 cells (expressing Bcr-AblT315I) showed greater sensitivity to 7e and 7f than to imatinib (GI50 = 13.80 and 15.43 vs. >20 μM, respectively). In silico analyses, including docking and molecular dynamics studies of Bcr-AblT315I, were conducted to elucidate the enhanced potency of 7e and 7f. Thus, this study provides in silico models to identify novel inhibitors that target a kinase of significance in CML.
Department of Experimental Biology Palacký University Slechtitelu 27 77900 Olomouc Czech Republic
Instituto de Química Facultad de Ciencias Universidad de Valparaíso Valparaíso 2360102 Chile
Zobrazit více v PubMed
Arber D.A., Orazi A., Hasserjian R., Thiele J., Borowitz M.J., Le Beau M.M., Bloomfield C.D., Cazzola M., Vardiman J.W. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127:2391–2405. doi: 10.1182/blood-2016-03-643544. PubMed DOI
Westerweel P.E., Te Boekhorst P.A.W., Levin M.-D., Cornelissen J.J. New Approaches and Treatment Combinations for the Management of Chronic Myeloid Leukemia. Front. Oncol. 2019;9:665. doi: 10.3389/fonc.2019.00665. PubMed DOI PMC
Komorowski L., Fidyt K., Patkowska E., Firczuk M. Philadelphia Chromosome-Positive Leukemia in the Lymphoid Lineage-Similarities and Differences with the Myeloid Lineage and Specific Vulnerabilities. Int. J. Mol. Sci. 2020;21:5776. doi: 10.3390/ijms21165776. PubMed DOI PMC
An X., Tiwari A.K., Sun Y., Ding P.-R., Ashby C.R., Chen Z.-S. BCR-ABL tyrosine kinase inhibitors in the treatment of Philadelphia chromosome positive chronic myeloid leukemia: A review. Leuk. Res. 2010;34:1255–1268. doi: 10.1016/j.leukres.2010.04.016. PubMed DOI
Baran Y., Saydam G. Cumulative clinical experience from a decade of use: Imatinib as first-line treatment of chronic myeloid leukemia. J. Blood Med. 2012;3:139–150. doi: 10.2147/JBM.S29132. PubMed DOI PMC
Laganà A., Scalzulli E., Bisegna M.L., Ielo C., Martelli M., Breccia M. Understanding and overcoming resistance to tyrosine kinase inhibitors (TKIs) in Chronic myeloid leukemia (CML) Expert. Rev. Hematol. 2025;18:65–79. doi: 10.1080/17474086.2024.2440776. PubMed DOI
Carofiglio F., Trisciuzzi D., Gambacorta N., Leonetti F., Stefanachi A., Nicolotti O. Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to. Molecules. 2020;25:4210. doi: 10.3390/molecules25184210. PubMed DOI PMC
Rossari F., Minutolo F., Orciuolo E. Past, present, and future of Bcr-Abl inhibitors: From chemical development to clinical efficacy. J. Hematol. Oncol. 2018;11:84. doi: 10.1186/s13045-018-0624-2. PubMed DOI PMC
Nascimento M., Moura S., Parra L., Vasconcellos V., Costa G., Leite D., Dias M., Fernandes T.V.A., Hoelz L., Pimentel L., et al. Ponatinib: A Review of the History of Medicinal Chemistry behind Its Development. Pharmaceuticals. 2024;17:1361. doi: 10.3390/ph17101361. PubMed DOI PMC
Azam M., Nardi V., Shakespeare W.C., Metcalf C.A., 3rd, Bohacek R.S., Wang Y., Sundaramoorthi R., Sliz P., Veach D.R., Bornmann W.G., et al. Activity of dual SRC-ABL inhibitors highlights the role of BCR/ABL kinase dynamics in drug resistance. Proc. Natl. Acad. Sci. USA. 2006;103:9244–9249. doi: 10.1073/pnas.0600001103. PubMed DOI PMC
Delgado T., Veselá D., Dostálová H., Kryštof V., Vojáčková V., Jorda R., Castro A., Bertrand J., Rivera G., Faúndez M., et al. New Inhibitors of Bcr-Abl Based on 2,6,9-Trisubstituted Purine Scaffold Elicit Cytotoxicity in Chronic Myeloid Leukemia-Derived Cell Lines Sensitive and Resistant to TKIs. Pharmaceutics. 2024;16:649. doi: 10.3390/pharmaceutics16050649. PubMed DOI PMC
Bertrand J., Dostálová H., Kryštof V., Jorda R., Delgado T., Castro-Alvarez A., Mella J., Cabezas D., Faúndez M., Espinosa-Bustos C., et al. Design, Synthesis, In Silico Studies and Inhibitory Activity towards Bcr-Abl, BTK and FLT3-ITD of New 2,6,9-Trisubstituted Purine Derivatives as Potential Agents for the Treatment of Leukaemia. Pharmaceutics. 2022;14:1294. doi: 10.3390/pharmaceutics14061294. PubMed DOI PMC
Bertrand J., Dostálová H., Krystof V., Jorda R., Castro A., Mella J., Espinosa-Bustos C., María Zarate A., Salas C.O. New 2,6,9-trisubstituted purine derivatives as Bcr-Abl and Btk inhibitors and as promising agents against leukemia. Bioorg. Chem. 2020;94:103361. doi: 10.1016/j.bioorg.2019.103361. PubMed DOI
Gagic Z., Ruzic D., Djokovic N., Djikic T., Nikolic K. In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs. Front. Chem. 2019;7:873. doi: 10.3389/fchem.2019.00873. PubMed DOI PMC
Urich R., Wishart G., Kiczun M., Richters A., Tidten-Luksch N., Rauh D., Sherborne B., Wyatt P.G., Brenk R. De Novo Design of Protein Kinase Inhibitors by in Silico Identification of Hinge Region-Binding Fragments. ACS Chem. Biol. 2013;8:1044–1052. doi: 10.1021/cb300729y. PubMed DOI PMC
Golestanifar F., Garkani-Nejad Z. In silico design and ADMET evaluation of new inhibitors for PIM1 kinase using QSAR studies, molecular docking, and molecular dynamic simulation. Heliyon. 2024;10:e38309. doi: 10.1016/j.heliyon.2024.e38309. PubMed DOI PMC
Azimi S., Bagher M., Shahram B., Vessally E. In Silico study and design of some new potent threonine tyrosine kinase inhibitors using molecular docking simulation. Mol. Simul. 2023;49:517–524. doi: 10.1080/08927022.2023.2172192. DOI
Gond C., Kumar N., Mishra A., Daksh S., Datta A., Tiwari A.K. Rational computational Design of new-Generation EGFR tyrosine kinase (EGFR-TK) inhibitors. Results Chem. 2025;15:102239. doi: 10.1016/j.rechem.2025.102239. DOI
Martins D.M., Fernandes P.O., Vieira L.A., Maltarollo V.G., Moraes A.H. Structure-Guided Drug Design Targeting Abl Kinase: How Structure and Regulation Can Assist in Designing New Drugs. ChemBioChem. 2024;25:e202400296. doi: 10.1002/cbic.202400296. PubMed DOI
Koroleva E.V., Ermolinskaya A.L., Ignatovich Z.V., Kornoushenko Y.V., Panibrat A.V., Potkin V.I., Andrianov A.M. Design, in silico Evaluation, and Determination of Antitumor Activity of Potential Inhibitors Against Protein Kinases: Application to BCR-ABL Tyrosine Kinase. Biochemistry. 2024;89:1094–1108. doi: 10.1134/S0006297924060099. PubMed DOI
Harrington E.A., Bebbington D., Moore J., Rasmussen R.K., Ajose-Adeogun A.O., Nakayama T., Graham J.A., Demur C., Hercend T., Diu-Hercend A., et al. VX-680, a potent and selective small-molecule inhibitor of the Aurora kinases, suppresses tumor growth in vivo. Nat. Med. 2004;10:262–267. doi: 10.1038/nm1003. PubMed DOI
Kumar V., Parate S., Danishuddin, Zeb A., Singh P., Lee G., Jung T.S., Lee K.W., Ha M.W. 3D-QSAR-Based Pharmacophore Modeling, Virtual Screening, and Molecular Dynamics Simulations for the Identification of Spleen Tyrosine Kinase Inhibitors. Front. Cell. Infect. Microbiol. 2022;12:909111. doi: 10.3389/fcimb.2022.909111. PubMed DOI PMC
Balasubramanian P.K., Balupuri A., Cho S.J. 3D-QSAR studies on disubstituted dibenzosuberone derivatives as p38α MAP kinase inhibitors using CoMFA and COMSIA. Med. Chem. Res. 2016;25:2349–2359. doi: 10.1007/s00044-016-1642-7. DOI
Golbraikh A., Tropsha A. Beware of q2! J. Mol. Graph. Model. 2002;20:269–276. doi: 10.1016/S1093-3263(01)00123-1. PubMed DOI
Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Inform. 2010;29:476–488. doi: 10.1002/minf.201000061. PubMed DOI
Tropsha A., Gramatica P., Gombar V.K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci. 2003;22:69–77. doi: 10.1002/qsar.200390007. DOI
Roy P.P., Roy K. On Some Aspects of Variable Selection for Partial Least Squares Regression Models. QSAR Comb. Sci. 2008;27:302–313. doi: 10.1002/qsar.200710043. DOI
Espinosa-Bustos C., Zárate A.M., Castro-Álvarez A., Guerrero S., Kogan M.J., Salas C.O. Synthesis, antitumoral activity, and in silico studies on Smoothened receptor of new 2,6,9-trisubstituted purine derivatives. J. Mol. Struct. 2025;1328:141228. doi: 10.1016/j.molstruc.2024.141228. DOI
Espinosa-Bustos C., Bertrand J., Villegas-Menares A., Guerrero S., Di Marcotullio L., Navacci S., Schulte G., Kozielewicz P., Bloch N., Villela V., et al. New Smoothened ligands based on the purine scaffold as potential agents for treating pancreatic cancer. Bioorg. Chem. 2024;151:107681. doi: 10.1016/j.bioorg.2024.107681. PubMed DOI
Villegas A., Satheeshkumar R., Ballesteros-Casallas A., Paulino M., Castro A., Espinosa-Bustos C., Salas C.O. Convergent synthesis, drug target prediction, and docking studies of new 2,6,9-trisubstituted purine derivatives. J. Heterocycl. Chem. 2022;59:97–111. doi: 10.1002/jhet.4368. DOI
Meng Y., Gao C., Clawson D.K., Atwell S., Russell M., Vieth M., Roux B. Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models. J. Chem. Theory Comput. 2018;14:2721–2732. doi: 10.1021/acs.jctc.7b01170. PubMed DOI PMC
Willis S.G., Lange T., Demehri S., Otto S., Crossman L., Niederwieser D., Stoffregen E.P., McWeeney S., Kovacs I., Park B., et al. High-sensitivity detection of BCR-ABL kinase domain mutations in imatinib-naive patients: Correlation with clonal cytogenetic evolution but not response to therapy. Blood. 2005;106:2128–2137. doi: 10.1182/blood-2005-03-1036. PubMed DOI
Weisberg E., Manley P., Mestan J., Cowan-Jacob S., Ray A., Griffin J.D. AMN107 (nilotinib): A novel and selective inhibitor of BCR-ABL. Br. J. Cancer. 2006;94:1765–1769. doi: 10.1038/sj.bjc.6603170. PubMed DOI PMC
Pemovska T., Johnson E., Kontro M., Repasky G.A., Chen J., Wells P., Cronin C.N., McTigue M., Kallioniemi O., Porkka K., et al. Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature. 2015;519:102–105. doi: 10.1038/nature14119. PubMed DOI
Phillips J.C., Braun R., Wang W., Gumbart J., Tajkhorshid E., Villa E., Chipot C., Skeel R.D., Kalé L., Schulten K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005;26:1781–1802. doi: 10.1002/jcc.20289. PubMed DOI PMC
Bai Q., Tan S., Xu T., Liu H., Huang J., Yao X. MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Brief. Bioinform. 2020;22:bbaa161. doi: 10.1093/bib/bbaa161. PubMed DOI PMC
Meanwell N.A. Improving Drug Candidates by Design: A Focus on Physicochemical Properties as a Means of Improving Compound Disposition and Safety. Chem. Res. Toxicol. 2011;24:1420–1456. doi: 10.1021/tx200211v. PubMed DOI
Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development. Adv. Drug Deliv. Rev. 1997;23:3–25.1. doi: 10.1016/S0169-409X(96)00423-1. Erratum in Adv. Drug Deliv. Rev. 2001, 46, 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0 . PubMed DOI
Veber D.F., Johnson S.R., Cheng H.-Y., Smith B.R., Ward K.W., Kopple K.D. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. J. Med. Chem. 2002;45:2615–2623. doi: 10.1021/jm020017n. PubMed DOI
Powell M.J.D. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 1964;7:155–162. doi: 10.1093/comjnl/7.2.155. DOI
Klebe G., Abraham U., Mietzner T. Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity. J. Med. Chem. 1994;37:4130–4146. doi: 10.1021/jm00050a010. PubMed DOI
Clark M., Cramer III R.D., Van Opdenbosch N. Validation of the general purpose tripos 5.2 force field. J. Comput. Chem. 1989;10:982–1012. doi: 10.1002/jcc.540100804. DOI
Roy K., Kar S., Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst. 2015;145:22–29. doi: 10.1016/j.chemolab.2015.04.013. DOI
Curik N., Polivkova V., Burda P., Koblihova J., Laznicka A., Kalina T., Kanderova V., Brezinova J., Ransdorfova S., Karasova D., et al. Somatic Mutations in Oncogenes Are in Chronic Myeloid Leukemia Acquired De Novo via Deregulated Base-Excision Repair and Alternative Non-Homologous End Joining. Front. Oncol. 2021;11:744373. doi: 10.3389/fonc.2021.744373. PubMed DOI PMC
Jo S., Kim T., Iyer V.G., Im W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 2008;29:1859–1865. doi: 10.1002/jcc.20945. PubMed DOI
Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I., et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 2010;31:671–690. doi: 10.1002/jcc.21367. PubMed DOI PMC
Ryckaert J.-P., Ciccotti G., Berendsen H.J.C. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys. 1977;23:327–341. doi: 10.1016/0021-9991(77)90098-5. DOI
Huang J., MacKerell A.D., Jr. CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 2013;34:2135–2145. doi: 10.1002/jcc.23354. PubMed DOI PMC