Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

Author(s): Mahmoud A. Al-Sha'er* , Rua'a A. Al-Aqtash , Mutasem O. Taha* .

Journal Name: Medicinal Chemistry

Volume 15 , Issue 6 , 2019

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Graphical Abstract:


Background: PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding.

Methods: Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors).

Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values.

Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

Keywords: Co-crystallized structure, PI3Kδ, anticancer, pharmacophore modeling, docking, roc analysis.

Janku, F. Phosphoinositide 3-kinase (PI3K) pathway inhibitors in solid tumors: From laboratory to patients. Cancer Treat. Rev., 2017, 59, 93-101.
Ito, K.; Caramori, G.; Adcock, I.M. Therapeutic potential of phosphatidylinositol 3-kinase inhibitors in inflammatory respiratory disease. J. Pharmacol. Exp. Ther., 2007, 321, 1-8.
Study results provide rationale for use of PI3K inhibitors in therapeutic settings, Retrieved on 2010-11-0
Crabbe, T. Exploring the potential of PI3K inhibitors for inflammation and cancer. Biochem. Soc. Trans., 2007, 35(Pt 2), 253-256.
Stein, R. Prospects for phosphoinositide 3-kinase inhibition as a cancer treatment”. Endocr. Relat. Cancer. Bioscientifica, 2001, 8(3), 237-348.
Chantry, D.; Vojtek, A.; Kashishian, A.; Holtzman, D.A.; Wood, C.; Gray, P.W.; Cooper, J.A.; Hoekstra, M.F. p110δ, a novel phosphatidylinositol 3-kinase catalytic subunit that associates with p85 and is expressed predominantly in leukocytes. J. Biol. Chem., 1997, 272(31), 19236-19241.
Okkenhaug, K.; Vanhaesebroeck, B. PI3K-signalling in B- and T cells: Insights from gene-targeted mice. Biochem. Soc. Trans., 2003, 31, 270-274.
Okkenhaug, K.; Vanhaesebroeck, B. PI3K in lymphocyte development, differentiation and activation. Nat. Rev. Immunol., 2003, 3, 317-330.
Rommel, C.; Camps, M.; Ji, H. PI3K delta and PI3K gamma: Partners in crime in inflammation in rheumatoid arthritis and beyond? Nat. Rev. Immunol., 2007, 7, 191-201.
Thomas, M.; Owen, C. Inhibition of PI-3 kinase for treating respiratory disease: Good idea or bad idea? Curr. Opin. Pharmacol., 2008, 8, 267-274.
Williams, O.; Hoouseman, B.T.; Kunkel, E.J.; Aizenstein, B.; Hoffman, R.; Knight, Z.A.; Shokat, K.M. Discovery of dual inhibitors of the immune cell PI3Ks p110delta and p110gamma: A prototype for new anti-inflammatory drugs. Chem. Biol., 2010, 17, 123-134.
Bernal, A.; Pastore, R.D.; Asgary, Z.; Keller, S.A.; Cesarman, E.; Liou, H.C.; Schattner, E.J. Survival of leukemic B cells promoted by engagement of the antigen receptor. Blood, 2001, 98(10), 3050-3057.
Kurtz, J.E.; Ray-Coquard, I. PI3kinase inhibitors in the clinic: An update. Anticancer Res., 2012, 32(7), 2463-2470.
"PI3K inhibitors: Targeting multiple tumor progression pathways". 2003. Archived from the original on February 28 2009.
Neri, L.M.; Borgatti, P.; Tazzari, P.L.; Bortul, R.; Cappellini, A.; Tabellini, G.; Bellacosa, A.; Capitani, S.; Martelli, A.M. The phosphoinositide 3-kinase/AKT1 pathway involvement in drug and all-trans-retinoic acid resistance of leukemia cells. Mol. Cancer Res., 2003, 1(3), 234-246.
Fruman, D.A.; Rommel, C. PI3Kδ Inhibitors in Cancer: Rationale and serendipity merge in the clinic. Cancer Discov., 2011, 1(7), 562-572.
Patel, L.; Chandrasekhar, J.; Evarts, J.; Haran, A.C.; Ip, C.; Kaplan, J.A.; Kim, M.; Koditek, D.; Lad, L.; Lepist, E-I.; McGrath, M.E.; Novikov, N.; Perreault, S.; Puri, K.D.; Somoza, J.R.; Steiner, B.H.; Stevens, K.L.; Therrien, J.; Treiberg, J.; Villaseñor, A.G.; Yeung, A.; Phillips, G. 2,4,6-triaminopyrimidine as a novel hinge binder in a series of PI3Kδ selective inhibitors. J. Med. Chem., 2016, 59, 3532-3548.
Xie, C.; He, Y.; Zhen, M.; Wang, Y.; Xu, Y.; Lou, L. Puquitinib, a novel orally available PI3Kd inhibitor, exhibits potent antitumor efficacy against acute myeloid leukemia. Cancer Sci., 2017, 108(7), 1476-1484.
Murray, J.M.; Sweeney, Z.K.; Chan, B.K.; Balazs, M.E.; Bradley, G.; Castanedo, C.; Chabot, D.; Chantry, M.; Flagella, D.M.; Goldstein, R.; Kondru, J.; Lesnick, J.; Li, M.C.; Lucas, J.; Nonomiya, J.; Pang, S.; Price, L.; Salphati, B.; Safina, P.P.; Savy, E.M.; Seward, U.M.; Sutherlin, D.P. Potent and highly selective benzimidazole inhibitors of PI3-kinase delta. J. Med. Chem., 2012, 55, 7686-7695.
Poulsen, A.; Nagaraj, H.; Lee, A.; Blanchard, S.; Soh, C.K.; Chen, D.; Wang, H.; Hart, S.; Goh, K.C.; Dymock, B.; Williams, M. Structure and ligand-based design of mTOR and PI3-kinase inhibitors leading to the clinical candidates VS-5584 (SB2343) and SB2602. J. Chem. Inf. Model., 2014, 54(11), 3238-3250.
Al-Sha’er, M.A.; Mansi, I.; Khanfar, M.; Abudayyh, A. Discovery of new heat shock protein 90 inhibitors using virtual co-crystallized pharmacophore generation. J. Enzyme Inhib. Med. Chem., 2016, 31, 64-77.
Al-Sha’er, M.A.; Mansi, I.; Almazari, I.; Hakooz, N. Evaluation of novel Akt1 inhibitors as anticancer agents using virtual co- crystallized pharmacophore generation. J. Mol. Graph. Model., 2015, 62, 213-225.
Ma, H.; Deacon, S.; Horiuchi, K. The challenge of selecting protein kinase assays for lead discovery optimization. Expert Opin. Drug Discov., 2008, 3, 607-621.
Levit, A.; Yarnitzky, T.; Wiener, A.; Meidan, R.; Niv, M.Y. Modeling of human prokineticin receptors: Interactions with novel small-molecule binders and potential off-target drugs. PLoS One, 2011, 6e27990
Protein Data Bank (PDB:. 2015
Kumar, B.V.; Kotla, R.; Buddiga, R.; Roy, J.; Singh, S.S.; Gundla, R.; Ravikumar, M.; Sarma, J.A. Ligand-based and structure-based approaches in identifying ideal pharmacophore against C-Jun N-terminal kinase-3. J. Mol. Model., 2010, 17, 151-163.
Kurogi, Y.; Guner, O. Pharmacophore modeling and threedimensional database searching for drug design using catalyst. C.M.C., 2001, 8, 1035-1055.
Abuhamdah, S.; Habash, M.; Taha, M.O. Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors. J. Comput. Aided Mol. Des., 2013, 27, 1075-1092.
Al-Nadaf, A.H.; Taha, M. Discovery of new renin inhibitory leads via sequential pharmacophore modeling, QSAR analysis, in silico screening and in vitro evaluation. J. Mol. Graph. Model., 2011, 29, 843-864.
Al-Sha’er, M.A.; VanPatten, S.; Al-Abed, Y.; Taha, M.O. Elaborate ligand-based modeling reveal new migration inhibitory factor inhibitors. J. Mol. Graph. Model., 2013, 42, 104-114.
Al-Sha’er, M.A.; Khanfar, M.A.; Taha, M.O. Discovery of novel Urokinase Plasminogen Activator (UPA) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis. J. Mol. Model., 2014, 20, 2080-2095.
Habash, M.A.; Abdelazeem, A.H.; Taha, M.O. Elaborate ligand-based modeling reveals new human neutrophil elastase inhibitors. Med. Chem. Res., 2014, 23, 3876-3896.
Khanfar, M.A.; AbuKhader, M.M.; Alqtaishat, S.; Taha, M.O. Pharmacophore modeling, homology modeling, and in silico screening reveal mammalian target of Rapamycin inhibitory activities for Sotalol, Glyburide, Metipranolol, Sulfamethizole, Glipizide, and Pioglitazone. J. Mol. Graph. Model., 2013, 42, 39-49.
Taha, M.O.; Qandil, A.M.; Al-Haraznah, T.; Abu-Khalaf, R.; Zalloum, H.; Al-Bakri, A.G. Discovery of new antifungal leads via pharmacophore modeling and QSAR analysis of fungal N-Myristoyl transferase inhibitors followed by in silico screening. Chem. Biol. Drug Des., 2011, 78, 391-407.
Taha, M.O.; Habash, M.; Hatmal, M.M.; Abdelazeem, A.H.; Qandil, A. Ligand-based modeling followed by in vitro bioassay yielded new potent Glucokinase activators. J. Mol. Graph. Model., 2015, 56, 91-102.
Langer, T.; Hoffmann, R.D. Pharmacophore modelling: Applications in drug discovery. Expert Opin. Drug Discov., 2006, 1, 261-267.
Al-Sha’er, M.A.; Taha, M.O. Application of docking-based comparative intermolecular contacts analysis for validating Hsp90α docking studies and subsequent in silico screening for inhibitors. J. Mol. Model., 2012, 18, 4843-4863.
Taha, M.O.; Habash, M.; Al-Hadidi, Z.; Al-Bakri, A.; Younis, K.; Sisan, S. Docking-based comparative intermolecular contacts analysis as new 3D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies. J. Chem. Inf. Model., 2011, 51, 647-669.
Taha, M.O.; Habash, M.; Khanfar, M. The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activator. J. Comput. Aided Mol. Des., 2014, 28, 509-547.
Abuhammad, A.; Taha, M. Innovative computer-aided methods for the discovery of new kinase ligands. Future Med. Chem., 2016, 8, 509-526.
Jaradat, N.J.; Khanfar, M.A.; Habash, M.; Taha, M.O. Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors. J. Comput. Aided Mol. Des., 2015, 29, 561-581.
Merz, K.; Ringe, D.; Reynolds, C. Drug Design; Cambridge University Press: Cambridge [U.K.] , 2010.
Discovery Studio 4.5 User Manual 2015.
Lin, H.; Schulz, M.J.; Xie, R.; Zeng, J.; Luengo, J.I.; Squire, M.D.; Tedesco, R.; Qu, J.; Erhard, K.; Mack, J.F.; Raha, K.; Plant, R.; Rominger, C.M.; Ariazi, J.L.; Sherk, C.S.; Schaber, M.D.; McSurdy-Freed, J. Spengler, M.D.; Davis, C.B.; Hardwicke, M.A.; Rivero, R.A. Rational design, synthesis, and SAR of a novel thiazolopyrimidinone series of selective PI3K-beta inhibitors. Med. Chem. Lett., 2012, 3, 524-529.
Barlaam, B.; Cosulich, S.; Degorce, S.; Fitzek, M.; Green, S.; Hancox, U. Lambert-van, der Brempt, C.; Lohmann, J-J.; Maudet, M.; Morgentin, R.; Pasquet, M-J.; Péru, A.; Plé, P.; Saleh, T.; Vautier, M.; Walker, M.; Ward, L.; Warin, N. Discovery of (R) 8-(1-(3,5-Difluorophenylamino) ethyl)-N, N-dimethyl-2-morpholino-4-oxo-4H-chromene-6-carboxamide (AZD8186): A potent and selective inhibitor of PI3Kβ and PI3Kδ for the treatment of PTEN-deficient cancers. J. Med. Chem., 2015, 8, 943-962.
Bui, M.; Hao, X.; Shin, Y.; Cardozo, M.; He, X.; Henne, K.; Suchomel, J.; McCarter, J.; McGee, L.R.; San, M.T.; Medina, J.C.; Mohn, D.; Tran, T.; Wannberg, S.; Wong, J.; Wong, S.; Zalameda, L.; Metz, D.; Cushing, T.D. Synthesis and SAR study of potent and selective PI3Kdelta inhibitors. Bioorg. Med. Chem. Lett., 2015, 25(5), 1104-1109.
CERIUS2, QSAR Users’ Manual, version 4.10Accelrys ; Inc.: San Diego, CA, 2005, p. 43-88, 221-235, 237-250.
Bento, A.P.; Gaulton, A.; Hersey, A.; Bellis, L.J.; Chambers, J.; Davies, M.; Krüger, F.A.; Light, Y.; Mak, L.; McGlinchey, S.; Nowotka, M.; Papadatos, G.; Santos, R.; Overington, J.P. The ChEMBL bioactivity database: An update. Nucleic Acids Res., 2014, 42, 1083-1090.
CERIUS2 4.10 LigandFit User Manual; Accelrys Inc.: San Diego, CA, 2000.
Sutter, J.; Güner, O.; Hoffmann, R.; Li, H.; Waldman, M. In: Pharmacophore Perception, Development, and Use in Drug Design; Güner, O.F., Ed.; International University Line: La Jolla, CA, 2000; pp. 501-511.
Poptodorov, T.; Luu, T.; Langer, R.H. In Methods and Principles in Medicinal Chemistry. Pharmacophores and Pharmacophores Searches; Hoffmann, R.D., Ed.; Wiley-VCH: Weinheim, Germany 2, 2006, pp. 17-47.
Triballeau, N.; Acher, F.; Brabet, I.; Pin, J.P.; Bertrand, H.O. Virtual screening workflow development guided by the “Receiver Operating Characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem., 2005, 48, 2534-2547.
Kirchmair, J.M.P.; Distinto, S.; Wolber, G.; Langer, T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection -What can we learn from earlier mistakes? J. Comput. Aided Mol., 2008, 22, 213-228.
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 settings. Adv. Drug Del. Rev., 2001, 46, 3-26.
Rao, S.N.; Head, M.S.; Kulkarni, A.; LaLonde, J.M. Validation studies of the site-directed docking program LibDock. J. Chem. Inf. Model., 2007, 47(6), 2159-2171.
Diller, D.J.; Merz, K.M. High throughput docking for library design and library prioritization. Proteins, 2001, 1(43), 113-124.
Yuan, J.; Mehta, P.P.; Yin, M.J.; Sun, S.; Zou, A.; Chen, J.; Rafidi, K.; Feng, Z.; Nickel, J.; Engebretsen, J.; Hallin, J.; Blasina, A.; Zhang, E.; Nguyen, L.; Sun, M.; Vogt, P.K.; McHarg, A.; Cheng, H.; Christensen, J.G.; Kan, J.L.; Bagrodia, S. PF-04691502, a potent and selective oral inhibitor of PI3K and mTOR kinases with antitumor activity. Mol. Cancer Ther., 2011, 10(11), 2189-2199.
Du, X.; Li, Y.; Xia, Y.; Ai, S.; Liang, J.; Sang, P.; Ji, X.; Liu, S. Insights into protein–ligand interactions: Mechanisms, models, and methods. Int. J. Mol. Sci., 2016, 17, 144-177.
Mortier, J.; Rakers, C.; Bermudez, M.; Murgueitio, M.; Riniker, S.; Wolber, G. The impact of molecular dynamics on drug design: Applications for the characterization of ligand-macromolecule complexes. Drug Discov. Today, 2015, 20, 686-702.
Sanders, M.; McGuire, R.; Roumen, L.; de Esch, I.; de Vlieg, J.; Klomp, J.; de Graaf, C. From the protein’s perspective: The benefits and challenges of protein structure-based pharmacophore modeling. Med. Chem. Commun, 2012, 3, 28-38.
Jacoby, E. Computational chemogenomics. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1, 57-67.
Ermondi, G.; Caron, G. Recognition forces in ligand-protein complexes: Blending information from different sources. Biochem. Pharmacol., 2006, 72, 1633-1645.
Hatmal, M.M.; Taha, M.O. Simulated annealing molecular dynamics and ligand-receptor contacts analysis for pharmacophore modeling. Future Med. Chem., 2017, 9, 1141-1159.
Hatmal, M.M.; Jaber, S.; Taha, M.O. Combining molecular dynamics simulation and ligand-receptor contacts analysis as a new approach for pharmacophore modeling: Beta-secretase 1 and check point kinase 1 as case studies. J. Comput. Aided Mol. Des., 2016, 30, 1149-1163.
Ortuso, F.; Langer, T.; Alcaro, S. GBPM: GRID-based pharmacophore model: Concept and application studies to protein-protein recognition. Bioinformatics, 2006, 22, 1449-1455.
Alcaro, S.; Artese, A.; Ceccherini-Silberstein, F.; Chiarella, V.; Dimonte, S.; Ortuso, F.; Perno, C. Computational analysis of Human Immunodeficiency Virus (HIV) type-1 reverse transcriptase crystallographic models based on significant conserved residues found in Highly Active Antiretroviral Therapy (HAART)-treated patients (Supplementary Material). Curr. Med. Chem., 2010, 17, 290-308.
Taha, M.O.; Habash, M.; Khanfar, M. The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activator. J. Comput. Aided Mol. Des., 2014, 28, 509-547.
Habash, M.; Abuhamdah, S.; Younis, K.; Taha, M.O. Docking-based comparative intermolecular contacts analysis and in silico screening reveal new potent acetylcholinesterase inhibitors. Med. Chem. Res., 2017, 26, 2768-2784.
Taha, M.O.; Al-Sha’er, M.A.; Khanfar, M.A.; Al-Nadaf, A.H. Discovery of nanomolar phosphoinositide 3 kinase gamma (PI3K-γ) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis. Eur. J. Med. Chem., 2014, 84, 454-465.
Hatmal, M.M.; Taha, M.O. Combining Stochastic deformation/relaxation and intermolecular contacts analysis for extracting pharmacophores from ligand-receptor complexes. J. Chem. Inf. Model., 2018, 58(4), 879-893.
Gohlke, H.; Klebe, G. DrugScore meets CoMFA: Adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. J. Med. Chem., 2002, 45(19), 4153-4170.
Sippl, W. Receptor-based 3D QSAR analysis of estrogen receptor ligands-merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods. J. Comp. Aided Mol. Des., 2000, 14(6), 559-572.
Dong, X-L.; Hilliard, S.G.; Zheng, W. Structure-based quantitative structure-activity relationship modeling of estrogen receptor <beta> -ligands. Future Med. Chem., 2011, 3(8), 933-945.
Ortiz, A.R.; Pastor, M.; Palomer, A.; Cruciani, G.; Gago, F.; Wade, R.C. Reliability of comparative molecular field analysis models: Effects of data scaling and variable selection using a set of human synovial fluid phospholipase A2 inhibitors. J. Med. Chem., 1997, 40(7), 1136-1148.
Santos-Filho, O.A.; Hopfinger, A.J.; Cherkasov, A.; De Alencastro, R.B. The receptordependent QSAR paradigm: An overview of the current state of the art. Med. Chem., 2009, 5(4), 359-366.
Meslamani, J.; Li, J.; Sutter, J.; Stevens, A.; Bertrand, H-O.; Rognan, D. Protein-ligand-based pharmacophores: Generation and utility assessment in computational ligand profiling. J. Chem. Inf. Model., 2012, 52(4), 943-955.
Bemis, G.W.; Murcko, M.A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem., 1996, 39, 2887-2893.
Gerlach, C.; Smolinski, M.; Steuber, H.; Sotriffer, C.A.; Heine, A.; Hangauer, D.G.; Klebe, G. Thermodynamic inhibition profile of a cyclopentyl and a cyclohexyl derivative towards thrombin: The same but for different reasons. Angew Chem. Int., 2007, 46, 8511-8514.
Davis, A.M.; St-Gallay, S.A.; Kleywegt, G.J. Limitations and lessons in the use of X-ray structural information in drug design. Drug Discov. Today, 2008, 13, 831-841.
Lai, B.; Nagy, G.; Garate, J.A.; Oostenbrink, C. Entropic and enthalpic contributions to stereospecific ligand binding from enhanced sampling methods. J. Chem. Inf. Model., 2014, 54, 151-158.
Rühmann, E.; Betz, M.; Heine, A.; Klebe, G. Fragment binding can be either more enthalpy-driven or entropy-driven: Crystal structures and residual hydration patterns suggest why. J. Med. Chem., 2014, 58, 6960-6971.
Al-Sha’er, M.A.; VanPatten, S.; Al-Abed, Y.; Taha, M.O. Elaborate ligand-based modeling reveal new migration inhibitory factor inhibitors. J. Mol. Graphics. Modell., 2013, 42, 104-114.
Hann, M.; Hudson, B.; Lewell, X.; Lifely, R.; Miller, L.; Ramsden, N. Strategic pooling of compounds for high-throughput screening. J. Chem. Inf. Comput. Sci., 1999, 39, 897-902.
Walters, W.P.; Murcko, M.A. Prediction of ‘drug-likeness’. Adv. Drug Deliv. Rev., 2002, 54, 255-271.
Shoichet, B.K. Interpreting steep dose-response curves in early inhibitor discovery. J. Med. Chem., 2006, 49, 7274-7277.
Walters, W.P.; Namchuk, M. Designing screens: How to make your hits a hit. Nat. Rev. Drug Discov., 2003, 2, 259-266.

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Year: 2019
Page: [588 - 601]
Pages: 14
DOI: 10.2174/1573406415666190222125333
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