De Novo Design and Molecular Dynamics Evaluation of AZ67-Derived Allosteric PFKFB3 Modulators for Anticancer Metabolic Drug Discovery

• Aeshah Muhana Mohammed

  Department of Chemistry, College of Education for Pure Science Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq

• Jameelah Kadhim Taher Al-Isawi

  Department of Chemistry, College of Education for Pure Science Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq

• Muqdad Khamis Abd

  Department of Chemistry, College of Education for Pure Science Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq

PFKFB3 is a druggable regulator of fructose-2,6-bisphosphate production, glycolytic flux, angiogenic metabolism, and tumour-cell survival signalling. This study redesigned the AZ67 allosteric scaffold to prioritise computational leads for anticancer metabolic drug discovery. ChemoDOTS enumeration produced 575,760 virtual products and 509,921 standardised unique structures. A reconstructed two-branch workflow applied sequential molecular-weight, lipophilicity, and relaxed drug-like criteria in one branch and PAINS filtering in parallel; intersection with Brenk-alert exclusion retained 10,996 candidates. Five-objective Pareto ranking and diversity filtering selected 50 compounds. A separate six-descriptor AZ67-referenced triage rule identified 27 comparator-qualified candidates but was not interpreted as proof of improved overall drug-likeness. Three nonredundant representatives—MOL_0052111, MOL_0051973, and MOL_0000552—were selected to test distinct substitution hypotheses, followed by docking in the PFKFB3 allosteric pocket (PDB 5AJV), 50-ns molecular dynamics, and MM/GBSA estimation. GNINA outputs were reported according to their distinct conventions: the empirical docking score was interpreted as more favourable when more negative, whereas CNNaffinity was reported on a positive pK scale, with higher values indicating stronger predicted affinity. The selected derivatives had higher molecular weight and topological polar surface area and lower QED than AZ67, although several had lower logP and higher Fsp3. MOL_0000552 gave an empirical docking score of 
-7.55 kcal/mol and CNNaffinity of 8.18 pK, compared with -4.33 kcal/mol and 4.21 pK for AZ67. It showed the least variable ligand trajectory among the derivatives and an MM/GBSA profile near -65 kcal/mol versus approximately -40 kcal/mol for AZ67. MOL_0051973 was a secondary lead, whereas MOL_0052111 underwent a marked ligand-RMSD shift after approximately 25 ns. MOL_0000552 therefore represents a prioritised computational hypothesis, not a validated PFKFB3 inhibitor. Biochemical activity, isoform selectivity, cellular effects, ADMET properties, and reproducibility in longer replicate simulations require confirmation.

[1] Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov 2022; 12(1): 31-46.

https://doi.org/10.1158/2159-8290.CD-21-1059

[2] Liberti MV, Locasale JW. The Warburg effect: how does it be-nefit cancer cells? Trends Biochem Sci 2016; 41(3): 211-218.

https://doi.org/10.1016/j.tibs.2015.12.001

[3] Yalcin A, Telang S, Clem B, Chesney J. Regulation of glu-cose metabolism by 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases in cancer. Exp Mol Pathol 2009; 86(3): 174-179.

https://doi.org/10.1016/j.yexmp.2009.01.003

[4] Okar DA, Manzano A, Navarro-Sabate A, Riera L, Bartrons R, Lange AJ. PFK-2/FBPase-2: maker and breaker of the essential biofactor fructose-2,6-bisphosphate. Trends Biochem Sci 2001; 26(1): 30-35.

https://doi.org/10.1016/S0968-0004(00)01699-6

[5] Yalcin A, Clem BF, Imbert-Fernandez Y, et al. 6-Phosphofructo-2-kinase (PFKFB3) promotes cell-cycle progression and suppresses apoptosis via Cdk1-mediated phosphorylation of p27. Cell Death Dis 2014; 5: e1337.

https://doi.org/10.1038/cddis.2014.292

[6] Chen L, Xu YX, Ren YY, et al. PFKFB3 as a multifaceted driver and therapeutic target in castration-resistant prostate cancer. Cell Death Dis 2025; 16(1): 760.

https://doi.org/10.1038/s41419-025-08089-8

[7] De Bock K, Georgiadou M, Schoors S, et al. Role of PFKFB3-driven glycolysis in vessel sprouting. Cell 2013; 154(3): 651-663.

https://doi.org/10.1016/j.cell.2013.06.037

[8] Schoors S, De Bock K, Cantelmo AR, et al. Partial and tran-sient reduction of glycolysis by PFKFB3 blockade reduces pathological angiogenesis. Cell Metab 2014; 19(1): 37-48.

https://doi.org/10.1016/j.cmet.2013.11.008

[9] Boyd S, Brookfield JL, Critchlow SE, et al. Structure-based design of potent and selective inhibitors of the metabolic kinase PFKFB3. J Med Chem 2015; 58(8): 3611-3625.

https://doi.org/10.1021/acs.jmedchem.5b00352

[10] Emini Veseli B, Van Wielendaele P, Delibegovic M, Martinet W, De Meyer GRY. The PFKFB3 inhibitor AZ67 inhibits angiogenesis independently of glycolysis inhibition. Int J Mol Sci 2021; 22(11): 5970.

https://doi.org/10.3390/ijms22115970

[11] Macut H, Hu X, Tarantino D, et al. Tuning PFKFB3 bisphosphatase activity through allosteric interference. Sci Rep 2019; 9(1): 20333.

https://doi.org/10.1038/s41598-019-56708-0

[12] Cruz E, Bessières B, Magistretti P, Alberini CM. Differential role of neuronal glucose and PFKFB3 in memory formation during development. Glia 2022; 70(11): 2207-2231.

https://doi.org/10.1002/glia.24248

[13] Clem BF, O'Neal J, Tapolsky G, et al. Targeting 6-phosphofructo-2-kinase (PFKFB3) as a therapeutic strategy against cancer. Mol Cancer Ther 2013; 12(8): 1461-1470.

https://doi.org/10.1158/1535-7163.MCT-13-0097

[14] Gustafsson NMS, Farnegardh K, Bonagas N, et al. Targeting PFKFB3 radiosensitizes cancer cells and suppresses homologous recombination. Nat Commun 2018; 9(1): 3872.

https://doi.org/10.1038/s41467-018-06287-x

[15] Redman R, Pohlmann P, Kurman MR, et al. PFK-158, first-in-man and first-in-class inhibitor of PFKFB3/glycolysis: a phase I, dose-escalation, multi-centre study in patients with advanced solid malignancies. Cancer Res 2015; 75(15 Suppl): CT206.

https://doi.org/10.1158/1538-7445.AM2015-CT206

[16] Fiore A, Scarano A, Antonini G, et al. First-in-class covalent inhibitors of PFKFB3: discovery and characterization in PDAC models. J Med Chem 2026; 69(11): 13248-13271.

https://doi.org/10.1021/acs.jmedchem.6c00235

[17] Fedichev PO, Greenman K, Chang CT, et al. PFKFB3 inhibitors and their uses. International patent WO2020080979A1. Published April 23, 2020.

[18] Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 2009; 52(21): 6752-6756.

https://doi.org/10.1021/jm901241e

[19] Mouchlis VD, Afantitis A, Serra A, et al. Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci 2021; 22(4): 1676.

https://doi.org/10.3390/ijms22041676

[20] Hoffer L, Charifi-Hoareau G, Barelier S, Betzi S, Miller T, Morelli X, Roche P. ChemoDOTS: a web server to design chemistry-driven focused libraries. Nucleic Acids Res 2024; 52(W1): W461-W468.

https://doi.org/10.1093/nar/gkae326

[21] Hartenfeller M, Eberle M, Meier P, et al. A collection of robust organic synthesis reactions for in silico molecule design. J Chem Inf Model 2011; 51(12): 3093-3098.

https://doi.org/10.1021/ci200379p

[22] Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002; 45(12): 2615-2623.

https://doi.org/10.1021/jm020017n

[23] Baell JB, Holloway GA. New substructure filters for removal of pan-assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 2010; 53(7): 2719-2740.

https://doi.org/10.1021/jm901137j

[24] Brenk R, Schipani A, James D, et al. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 2008; 3(3): 435-444.

https://doi.org/10.1002/cmdc.200700139

[25] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002; 6(2): 182-197.

https://doi.org/10.1109/4235.996017

[26] Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem 2012; 4(2): 90-98.

https://doi.org/10.1038/nchem.1243

[27] Bajusz D, Racz A, Heberger K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 2015; 7: 20.

https://doi.org/10.1186/s13321-015-0069-3

[28] Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res 2000; 28(1): 235-242.

https://doi.org/10.1093/nar/28.1.235

[29] Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 1996; 17(5-6): 490-519.

https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P

[30] McNutt AT, Francoeur P, Aggarwal R, et al. GNINA 1.0: molecular docking with deep learning. J Cheminform 2021; 13(1): 43.

https://doi.org/10.1186/s13321-021-00522-2

[31] McNutt AT, Li Y, Meli R, Aggarwal R, Koes DR. GNINA 1.3: the next increment in molecular docking with deep learning. J Cheminform 2025; 17(1): 28.

https://doi.org/10.1186/s13321-025-00973-x

[32] Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE. Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (SC '06). New York: Association for Computing Machinery 2006. Article 84.

https://doi.org/10.1145/1188455.1188544

[33] Berendsen HJC, Postma JPM, van Gunsteren WF, Hermans J. Interaction models for water in relation to protein hydration. In: Pullman B, editor. Intermolecular Forces. Dordrecht: Springer 1981. p. 331-342.

https://doi.org/10.1007/978-94-015-7658-1_21

[34] Lu C, Wu C, Ghoreishi D, et al. OPLS4: improving force-field accuracy on challenging regimes of chemical space. J Chem Theory Comput 2021; 17(7): 4291-4300.

https://doi.org/10.1021/acs.jctc.1c00302

[35] Martyna GJ, Klein ML, Tuckerman M. Nose-Hoover chains: the canonical ensemble via continuous dynamics. J Chem Phys 1992; 97(4): 2635-2643.

https://doi.org/10.1063/1.463940

[36] Martyna GJ, Tobias DJ, Klein ML. Constant pressure molecular dynamics algorithms. J Chem Phys 1994; 101(5): 4177-4189.

https://doi.org/10.1063/1.467468

[37] Ertl P, Rohde B, Selzer P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 2000; 43(20): 3714-3717.

https://doi.org/10.1021/jm000942e

[38] Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 2015; 10(5): 449-461.

https://doi.org/10.1517/17460441.2015.1032936

[39] McInnes L, Healy J, Saul N, Grossberger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Softw 2018; 3(29): 861.

https://doi.org/10.21105/joss.00861

[40] Nicolaou CA, Brown N. Multi-objective optimisation methods in drug design. Drug Discov Today Technol 2013; 10(3): e427-e435.

https://doi.org/10.1016/j.ddtec.2013.02.001

[41] Quiroga R, Villarreal MA. Developing generalizable scoring functions for molecular docking: challenges and perspectives. Curr Med Chem 2025; 32(28): 5960-5972.

https://doi.org/10.2174/0109298673334469241017053508

[42] Ballatore C, Huryn DM, Smith AB 3rd. Carboxylic acid (bio)isosteres in drug design. ChemMedChem 2013; 8(3): 385-395.

https://doi.org/10.1002/cmdc.201200585

[43] Kumari S, Carmona AV, Tiwari AK, Trippier PC. Amide bond bioisosteres: strategies, synthesis, and successes. J Med Chem 2020; 63(21): 12290-12358.

https://doi.org/10.1021/acs.jmedchem.0c00530

[44] Alvarez R, Mandal D, Chittiboina P. Canonical and non-canonical roles of PFKFB3 in brain tumors. Cells 2021; 10(11): 2913.

https://doi.org/10.3390/cells10112913

[45] Akella NM, Ciraku L, Reginato MJ. Fueling the fire: emerging role of the hexosamine biosynthetic pathway in cancer. BMC Biol 2019; 17(1): 52.

https://doi.org/10.1186/s12915-019-0671-3

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