Literature DB >> 21487786

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.

Julio Caballero1, Miguel Quiliano, Jans H Alzate-Morales, Mirko Zimic, Eric Deharo.   

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 IC(50) 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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Year:  2011        PMID: 21487786     DOI: 10.1007/s10822-011-9425-1

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  48 in total

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Authors:  Michael Fernández; Julio Caballero
Journal:  Bioorg Med Chem       Date:  2007-06-10       Impact factor: 3.641

2.  Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches.

Authors:  Michal Vieth; Jon Erickson; Jibo Wang; Yue Webster; Mary Mader; Richard Higgs; Ian Watson
Journal:  J Med Chem       Date:  2009-10-22       Impact factor: 7.446

3.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity.

Authors:  G Klebe; U Abraham; T Mietzner
Journal:  J Med Chem       Date:  1994-11-25       Impact factor: 7.446

4.  Analysis of c-Met kinase domain complexes: a new specific catalytic site receptor model for defining binding modes of ATP-competitive ligands.

Authors:  Yasmine Asses; Vincent Leroux; Safia Tairi-Kellou; Rosanna Dono; Flavio Maina; Bernard Maigret
Journal:  Chem Biol Drug Des       Date:  2009-12       Impact factor: 2.817

5.  Hepatocyte growth factor/c-met signaling pathway is required for efficient liver regeneration and repair.

Authors:  Chang-Goo Huh; Valentina M Factor; Aránzazu Sánchez; Koichi Uchida; Elizabeth A Conner; Snorri S Thorgeirsson
Journal:  Proc Natl Acad Sci U S A       Date:  2004-03-30       Impact factor: 11.205

6.  Discovery and optimization of triazolopyridazines as potent and selective inhibitors of the c-Met kinase.

Authors:  Brian K Albrecht; Jean-Christophe Harmange; David Bauer; Loren Berry; Christiane Bode; Alessandro A Boezio; April Chen; Deborah Choquette; Isabelle Dussault; Cary Fridrich; Satoko Hirai; Doug Hoffman; Jay F Larrow; Paula Kaplan-Lefko; Jasmine Lin; Julia Lohman; Alexander M Long; Jodi Moriguchi; Anne O'Connor; Michele H Potashman; Monica Reese; Karen Rex; Aaron Siegmund; Kavita Shah; Roman Shimanovich; Stephanie K Springer; Yohannes Teffera; Yajing Yang; Yihong Zhang; Steven F Bellon
Journal:  J Med Chem       Date:  2008-04-22       Impact factor: 7.446

Review 7.  Novel therapeutic inhibitors of the c-Met signaling pathway in cancer.

Authors:  Joseph Paul Eder; George F Vande Woude; Scott A Boerner; Patricia M LoRusso
Journal:  Clin Cancer Res       Date:  2009-03-24       Impact factor: 12.531

Review 8.  Drug development of MET inhibitors: targeting oncogene addiction and expedience.

Authors:  Paolo M Comoglio; Silvia Giordano; Livio Trusolino
Journal:  Nat Rev Drug Discov       Date:  2008-06       Impact factor: 84.694

9.  Docking and quantitative structure-activity relationship studies for the bisphenylbenzimidazole family of non-nucleoside inhibitors of HIV-1 reverse transcriptase.

Authors:  Carlos F Lagos; Julio Caballero; Fernando D Gonzalez-Nilo; Carlos David Pessoa-Mahana; Tomas Perez-Acle
Journal:  Chem Biol Drug Des       Date:  2008-11       Impact factor: 2.817

10.  Computational study of the interactions between guanine derivatives and cyclin-dependent kinase 2 (CDK2) by CoMFA and QM/MM.

Authors:  Jans Alzate-Morales; Julio Caballero
Journal:  J Chem Inf Model       Date:  2010-01       Impact factor: 4.956

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  8 in total

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Authors:  Yating Mao; Yan Li; Ming Hao; Shuwei Zhang; Chunzhi Ai
Journal:  J Mol Model       Date:  2011-09-27       Impact factor: 1.810

2.  (1S,2E,4S,7E,11E)-2,7,11-Cembratriene-4,6-diol semisynthetic analogs as novel c-Met inhibitors for the control of c-Met-dependent breast malignancies.

Authors:  Hassan Y Ebrahim; Mohamed M Mohyeldin; Mohammad M Hailat; Khalid A El Sayed
Journal:  Bioorg Med Chem       Date:  2016-09-13       Impact factor: 3.641

3.  Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3β.

Authors:  Luisa Quesada-Romero; Julio Caballero
Journal:  Mol Divers       Date:  2013-10-01       Impact factor: 2.943

4.  Drug design for ever, from hype to hope.

Authors:  G Seddon; V Lounnas; R McGuire; T van den Bergh; R P Bywater; L Oliveira; G Vriend
Journal:  J Comput Aided Mol Des       Date:  2012-01-18       Impact factor: 3.686

Review 5.  Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery.

Authors:  Valère Lounnas; Tina Ritschel; Jan Kelder; Ross McGuire; Robert P Bywater; Nicolas Foloppe
Journal:  Comput Struct Biotechnol J       Date:  2013-04-02       Impact factor: 7.271

6.  Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR.

Authors:  Luisa Quesada-Romero; Karel Mena-Ulecia; William Tiznado; Julio Caballero
Journal:  PLoS One       Date:  2014-07-10       Impact factor: 3.240

7.  Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA.

Authors:  Karel Mena-Ulecia; William Tiznado; Julio Caballero
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

8.  Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?

Authors:  David Ramírez; Julio Caballero
Journal:  Molecules       Date:  2018-04-28       Impact factor: 4.411

  8 in total

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