Abstract
We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC50 values were obtained by using quantitative structure–activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.
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References
Liu X, Yao W, Newton RC, Scherle PA (2008) Targeting the c-MET signaling pathway for cancer therapy. Expert Opin Investig Drugs 17(7):997–1011
Peruzzi B, Bottaro DP (2006) Targeting the c-Met signaling pathway in cancer. Clin Cancer Res 12(12):3657–3660
Huh C-G, Factor VM, Sánchez A, Uchida K, Conner EA, Thorgeirsson SS (2004) Hepatocyte growth factor/c-met signaling pathway is required for efficient liver regeneration and repair. Proc Natl Acad Sci USA 101(13):4477–4482
Lesko E, Majka M (2008) The biological role of HGF-MET axis in tumor growth and development of metastasis. Front Biosci 13(13):1271–1280
Comoglio PM, Giordano S, Trusolino L (2008) Drug development of MET inhibitors: targeting oncogene addiction and expedience. Nat Rev Drug Discov 7(6):504–516
Porter J (2010) Small molecule c-Met kinase inhibitors: a review of recent patents. Expert Opin Ther Pat 20(2):159–177
Eder JP, Vande Woude GF, Boerner SA, LoRusso PM (2009) Novel therapeutic inhibitors of the c-Met signaling pathway in cancer. Clin Cancer Res 15(7):2207–2214
Schroeder GM, Chen X-T, Williams DK, Nirschl DS, Cai Z-W, Wei D, Tokarski JS, An Y, Sack J et al (2008) Identification of pyrrolo[2, 1-f][1, 2, 4]triazine-based inhibitors of Met kinase. Bioorg Med Chem Lett 18(6):1945–1951
Kim KS, Zhang L, Schmidt R, Cai Z-W, Wei D, Williams DK, Lombardo LJ, Trainor GL, Xie D et al (2008) Discovery of pyrrolopyridine–pyridone based inhibitors of met kinase: synthesis, X-ray crystallographic analysis, and biological activities. J Med Chem 51(17):5330–5341
Williams DK, Chen X-T, Tarby C, Kaltenbach R, Cai Z-W, Tokarski JS, An Y, Sack JS, Wautlet B et al (2010) Design, synthesis and structure-activity relationships of novel biarylamine-based Met kinase inhibitors. Bioorg Med Chem Lett 20(9):2998–3002
Cai Z-W, Wei D, Schroeder GM, Cornelius LAM, Kim K, Chen X-T, Schmidt RJ, Williams DK, Tokarski JS et al (2008) Discovery of orally active pyrrolopyridine- and aminopyridine-based Met kinase inhibitors. Bioorg Med Chem Lett 18(11):3224–3229
Schroeder GM, An Y, Cai Z-W, Chen X-T, Clark C, Cornelius LAM, Dai J, Gullo-Brown J, Gupta A et al (2009) Discovery of N-(4-(2-Amino-3-chloropyridin-4-yloxy)-3-fluorophenyl)-4-ethoxy-1-(4-fluorophenyl)-2-oxo-1, 2-dihydropyridine-3-carboxamide (BMS-777607), a selective and orally efficacious inhibitor of the met kinase superfamily. J Med Chem 52(5):1251–1254
Albrecht BK, Harmange J-C, Bauer D, Berry L, Bode C, Boezio AA, Chen A, Choquette D, Dussault I et al (2008) Discovery and optimization of triazolopyridazines as potent and selective inhibitors of the c-Met Kinase†. J Med Chem 51(10):2879–2882
Boezio AA, Berry L, Albrecht BK, Bauer D, Bellon SF, Bode C, Chen A, Choquette D, Dussault I et al (2009) Discovery and optimization of potent and selective triazolopyridazine series of c-Met inhibitors. Bioorg Med Chem Lett 19(22):6307–6312
D’Angelo ND, Bellon SF, Booker SK, Cheng Y, Coxon A, Dominguez C, Fellows I, Hoffman D, Hungate R et al (2008) Design, synthesis, and biological evaluation of potent c-Met inhibitors. J Med Chem 51(18):5766–5779
Porter J, Lumb S, Lecomte F, Reuberson J, Foley A, Calmiano M, le Riche K, Edwards H, Delgado J et al (2009) Discovery of a novel series of quinoxalines as inhibitors of c-Met kinase. Bioorg Med Chem Lett 19(2):397–400
Porter J, Lumb S, Franklin RJ, Gascon-Simorte JM, Calmiano M, Riche KL, Lallemand B, Keyaerts J, Edwards H et al (2009) Discovery of 4-azaindoles as novel inhibitors of c-Met kinase. Bioorg Med Chem Lett 19(10):2780–2784
Alzate-Morales JH, Caballero J, Vergara-Jaque A, González-Nilo FD (2009) Insights into the structural basis of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors: prediction of the binding modes and potency of the inhibitors by docking and ONIOM calculations. J Chem Inf Model 49(4):886–899
Alzate-Morales JH, Vergara-Jaque A, Caballero J (2010) Computational study on the interaction of N1 substituted pyrazole derivatives with B-Raf kinase: an unusual water wire hydrogen-bond network and novel interactions at the entrance of the active site. J Chem Inf Model 50(6):1101–1112
Larsen CA, Bisson WH, Dashwood RH (2009) Tea catechins inhibit hepatocyte growth factor receptor (MET Kinase) activity in human colon cancer cells: kinetic and molecular docking studies. J Med Chem 52(21):6543–6545
Fernandez M, Tundidor-Camba A, Caballero J (2005) Modeling of cyclin-dependent kinase inhibition by 1H-Pyrazolo[3, 4-d]Pyrimidine derivatives using artificial neural network ensembles. J Chem Inf Model 45(6):1884–1895
González M, Caballero J, Helguera A, Garriga M, González G, Fernández M (2006) 2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors. Bull Math Biol 68(4):735–751
Caballero J, Fernández M, Saavedra M, González-Nilo FD (2008) 2D Autocorrelation, CoMFA, and CoMSIA modeling of protein tyrosine kinases’ inhibition by substituted pyrido[2, 3-d]pyrimidine derivatives. Bioorg Med Chem 16(2):810–821
Caballero J, Fernández M, González-Nilo FD (2008) Structural requirements of pyrido[2, 3-d]pyrimidin-7-one as CDK4/D inhibitors: 2D autocorrelation, CoMFA and CoMSIA analyses. Bioorg Med Chem 16(11):6103–6115
Gueto C, Ruiz JL, Torres JE, Méndez J, Vivas-Reyes R (2008) Three-dimensional quantitative structure-activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA. Bioorg Med Chem 16(5):2439–2447
Alzate-Morales J, Caballero J (2010) Computational study of the interactions between guanine derivatives and cyclin-dependent kinase 2 (CDK2) by CoMFA and QM/MM. J Chem Inf Model 50(1):110–122
Muthas D, Sabnis YA, Lundborg M, Karlén A (2008) Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering. J Mol Graph Model 26(8):1237–1251
Xie H-Z, Li L-L, Ren J-X, Zou J, Yang L, Wei Y-Q, Yang S-Y (2009) Pharmacophore modeling study based on known spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors. Bioorg Med Chem Lett 19(7):1944–1949
Uno M, Ban HS, Nabeyama W, Nakamura H (2008) de novo design and synthesis of N-benzylanilines as new candidates for VEGFR tyrosine kinase inhibitors. Org Biomol Chem 6(6):979–981
Vieth M, Erickson J, Wang J, Webster Y, Mader M, Higgs R, Watson I (2009) Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches. J Med Chem 52(20):6456–6466
Alzate-Morales JH, Contreras R, Soriano A, Tuñon I, Silla E (2007) A computational study of the protein-ligand interactions in CDK2 inhibitors: using quantum mechanics/molecular mechanics interaction energy as a predictor of the biological activity. Biophys J 92(2):430–439
Alzate-Morales JH, Caballero J, Gonzalez-Nilo FD, Contreras R (2009) A computational ONIOM model for the description of the H-bond interactions between NU2058 analogues and CDK2 active site. Chem Phys Lett 479(1–3):149–155
Villacañas O, Pérez JJ, Rubio-Martínez J (2002) Structural analysis of the inhibition of Cdk4 and Cdk6 by p16INK4a through molecular dynamics simulations. J Biomol Struct Dyn 20:347–358
Asses Y, Leroux V, Tairi-Kellou S, Dono R, Maina F, Maigret B (2009) Analysis of c-Met kinase domain complexes: a new specific catalytic site receptor model for defining binding modes of ATP-competitive ligands. Chem Biol Drug Des 74(6):560–570
Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37(24):4130–4146
Wellenzohn B, Liedl KR, Rode BM, Zaheer-ul-haq (2003) Molecular docking studies of natural cholinesterase-inhibiting steroidal alkaloids from sarcococca saligna. J Med Chem 46(23):5087–5090
Rojo LE, Alzate-Morales J, Saavedra IN, Davies P, Maccioni RB (2010) Selective interaction of lansoprazole and astemizole with tau polymers: potential new clinical use in diagnosis of alzheimer’s disease. J Alzheimer Dis 19(2):573–589
Hanessian S, Moitessier N, Therrien E (2001) A comparative docking study and the design of potentially selective MMP inhibitors. J Comput Aided Mol Des 15(10):873–881
Caballero J, Vergara-Jaque A, Fernández M, Coll D (2009) Docking and quantitative structure–activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase. Mol Divers 13(4):493–500
Lagos CF, Caballero J, Gonzalez-Nilo FD, David Pessoa-Mahana C, Perez-Acle T (2008) Docking and quantitative structure-activity relationship studies for the bisphenylbenzimidazole family of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Chem Biol Drug Des 72(5):360–369
Abagyan R, Totrov M, Kuznetsov D (1994) ICM–A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15(5):488–506
Molecular Editor, version 2.5, La Jolla, CA, Molsoft LLC, 2006
ICM, version 3.4-8, La Jolla, CA, Molsoft LLC, 2006
An J, Totrov M, Abagyan R (2005) Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol Cell Proteomics 4(6):752–761
Caballero J, Fernández M (2008) Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr Top Med Chem 8(18):1580–1605
DRAGON, version 3.0, Milano, Italy, Milano Chemometrics, 2003
Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42(3):682–692
Fernández M, Caballero J (2007) QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-[alpha]-phenylsulfonylacetamide derivatives. Bioorg Med Chem 15(18):6298–6310
Viswanadhan VN, Ghose AK, Revankar GR, Robins RK (1989) Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci 29(3):163–172
Ertl P, Rohde B, Selzer P (2000) 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 43(20):3714–3717
Miroguchi I, Hirono S, Liu Q, Nakagome I, Matsushita Y (1992) Simple method of calculating octanol/water partition coefficient. Chem Pharm Bull 40:127–130
de Oliveira DB, Gaudio AC (2000) BuildQSAR: a new computer program for QSAR analysis. Quant Struct Act Relat 19(6):599–601
Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct 622(1–2):39–51
Fernandez M, Carreiras MC, Marco JL, Caballero J (2006) Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized Genetic Neural Networks and ensemble averaging. J Enzyme Inhib Med Chem 21(6):647–661
Fatemi MH, Gharaghani S (2007) A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine. Bioorg Med Chem 15(24):7746–7754
González MP, Terán C, Teijeira M, González-Moa MJ (2005) GETAWAY descriptors to predicting A2A adenosine receptors agonists. Eur J Med Chem 40(11):1080–1086
Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graph Model 20(4):269–276
Acknowledgments
J.C. thanks “Becas Universidad de Talca” for financial support through a doctoral fellowship. M.Q. and E.D. gratefully acknowledge the Institut de Recherche pour le Développement (UMR 152 IRD-UPS) for financial support. J.H.A.M. acknowledges the financial support through project FONDECYT No 11100177.
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Caballero, J., Quiliano, M., Alzate-Morales, J.H. et al. Docking and quantitative structure–activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors. J Comput Aided Mol Des 25, 349–369 (2011). https://doi.org/10.1007/s10822-011-9425-1
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DOI: https://doi.org/10.1007/s10822-011-9425-1