Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies

Chemokines trigger numerous inflammatory responses and modulate the immune system. The interaction between monocyte chemoattractant protein-1 and chemokine receptor 2 (CCR2) may be the cause of atherosclerosis, obesity, and insulin resistance. However, CCR2 is also implicated in other inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, asthma, and neuropathic pain. Therefore, there is a paramount importance of designing potent and selective CCR2 antagonists despite a number of drug candidates failed in clinical trials. In this article, 83 CCR2 antagonists by Jhonson and Jhonson Pharmaceuticals have been considered for robust validated multi-QSAR modeling studies to get an idea about the structural and pharmacophoric requirements for designing more potent CCR2 antagonists. All these QSAR models were validated and statistically reliable. Observations resulted from different modeling studies correlated and validated results of other ones. Finally, depending on these QSAR observations, some new molecules were proposed that may exhibit higher activity against CCR2.

[1]  A K Halder,et al.  Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches , 2017, SAR and QSAR in environmental research.

[2]  Sugunadevi Sakkiah,et al.  3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. , 2010, European journal of medicinal chemistry.

[3]  Kunal Roy,et al.  Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection , 2011 .

[4]  Johann Gasteiger,et al.  Chemical Information in 3D Space , 1996, J. Chem. Inf. Comput. Sci..

[5]  Md Ataul Islam,et al.  Exploration of the structural requirements of HIV-protease inhibitors using pharmacophore, virtual screening and molecular docking approaches for lead identification. , 2015, Journal of molecular graphics & modelling.

[6]  N. Maeda,et al.  Absence of CC chemokine receptor-2 reduces atherosclerosis in apolipoprotein E-deficient mice. , 1999, Atherosclerosis.

[7]  S. Gayen,et al.  An integrated ligand-based modelling approach to explore the structure-property relationships of influenza endonuclease inhibitors , 2017, Structural Chemistry.

[8]  S. Gayen,et al.  Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches , 2016 .

[9]  Sk Abdul Amin,et al.  Combating breast cancer with non-steroidal aromatase inhibitors (NSAIs): Understanding the chemico-biological interactions through comparative SAR/QSAR study. , 2017, European journal of medicinal chemistry.

[10]  Oleg Devinyak,et al.  3D-MoRSE descriptors explained. , 2014, Journal of molecular graphics & modelling.

[11]  J. Mudgett,et al.  Impaired neuropathic pain responses in mice lacking the chemokine receptor CCR2 , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  A. A. Lagunin,et al.  SAR and QSAR in Environmental Research , 2006 .

[13]  E. Opas,et al.  A novel series of N-(azetidin-3-yl)-2-(heteroarylamino)acetamide CCR2 antagonists. , 2013, Bioorganic & medicinal chemistry letters.

[14]  Eslam Pourbasheer,et al.  2D and 3D Quantitative Structure-Activity Relationship Study of Hepatitis C Virus NS5B Polymerase Inhibitors by Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis Methods , 2014, J. Chem. Inf. Model..

[15]  W. Murray,et al.  Synthesis and structure-activity relationship of 7-azaindole piperidine derivatives as CCR2 antagonists. , 2008, Bioorganic & medicinal chemistry letters.

[16]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[17]  R. Todeschini,et al.  Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References , 2009 .

[18]  E. Opas,et al.  Discovery and SAR of 5-aminooctahydrocyclopentapyrrole-3a-carboxamides as potent CCR2 antagonists. , 2014, Bioorganic & medicinal chemistry letters.

[19]  T. Yao,et al.  Sequential Evaluation of Serum Monocyte Chemotactic Protein 1 Among Asymptomatic State and Acute Exacerbation and Remission of Asthma in Children , 2009, The Journal of asthma : official journal of the Association for the Care of Asthma.

[20]  S. Gayen,et al.  First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington's disease: A proposal to chemists! , 2016, Bioorganic & medicinal chemistry letters.

[21]  M. Singer,et al.  Substituted dipiperidine alcohols as potent CCR2 antagonists. , 2008, Bioorganic & medicinal chemistry letters.

[22]  Supratik Kar,et al.  On a simple approach for determining applicability domain of QSAR models , 2015 .

[23]  K. Roy,et al.  Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants , 2010 .

[24]  Bruce L. Bush,et al.  Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA , 1993, J. Comput. Aided Mol. Des..

[25]  A. IJzerman,et al.  Discovery and Mapping of an Intracellular Antagonist Binding Site at the Chemokine Receptor CCR2 , 2014, Molecular Pharmacology.

[26]  Margaret A. Phillips,et al.  Identification of New Human Malaria Parasite Plasmodium falciparum Dihydroorotate Dehydrogenase Inhibitors by Pharmacophore and Structure-Based Virtual Screening , 2016, J. Chem. Inf. Model..

[27]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[28]  Nareshkumar Jain,et al.  Synthesis, structure-activity relationship and in vivo antiinflammatory efficacy of substituted dipiperidines as CCR2 antagonists. , 2007, Journal of medicinal chemistry.

[29]  W. Kuziel,et al.  Cc Chemokine Receptor 2 Is Critical for Induction of Experimental Autoimmune Encephalomyelitis , 2000, The Journal of experimental medicine.

[30]  Kai-xian Chen,et al.  Novel Bayesian classification models for predicting compounds blocking hERG potassium channels , 2014, Acta Pharmacologica Sinica.

[31]  Asim Kumar Debnath,et al.  Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase. , 2002, Journal of medicinal chemistry.

[32]  S. Gayen,et al.  Designing Potential Antitrypanosomal Thiazol-2-ethylamines through Predictive Regression Based and Classification Based QSAR Analyses. , 2017, Current drug discovery technologies.

[33]  Anthony E. Klon,et al.  Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction , 2006, J. Chem. Inf. Model..

[34]  A. IJzerman,et al.  When structure-affinity relationships meet structure-kinetics relationships: 3-((Inden-1-yl)amino)-1-isopropyl-cyclopentane-1-carboxamides as CCR2 antagonists. , 2015, European journal of medicinal chemistry.

[35]  Nilanjan Adhikari,et al.  First report on the structural exploration and prediction of new BPTES analogs as glutaminase inhibitors , 2017 .

[36]  R. R. Hocking The analysis and selection of variables in linear regression , 1976 .

[37]  G. Wolf,et al.  The role of chemokines and chemokine receptors in diabetic nephropathy. , 2008, Frontiers in bioscience : a journal and virtual library.

[38]  J. I The Design of Experiments , 1936, Nature.

[39]  J. Gong,et al.  An Antagonist of Monocyte Chemoattractant Protein 1 (MCP-1) Inhibits Arthritis in the MRL-lpr Mouse Model , 1997, The Journal of experimental medicine.

[40]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[41]  C. Molloy,et al.  The discovery of novel cyclohexylamide CCR2 antagonists. , 2011, Bioorganic & medicinal chemistry letters.

[42]  L. Heitman,et al.  Synthesis and biological evaluation of spirocyclic antagonists of CCR2 (chemokine CC receptor subtype 2). , 2015, Bioorganic & medicinal chemistry.

[43]  Johann Gasteiger,et al.  Deriving the 3D structure of organic molecules from their infrared spectra , 1999 .

[44]  G. Klebe,et al.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.

[45]  Johann Gasteiger,et al.  The Coding of the Three-Dimensional Structure of Molecules by Molecular Transforms and Its Application to Structure-Spectra Correlations and Studies of Biological Activity , 1996, J. Chem. Inf. Comput. Sci..

[46]  Lixin Shao,et al.  Discovery of INCB8761/PF-4136309, a Potent, Selective, and Orally Bioavailable CCR2 Antagonist. , 2011, ACS medicinal chemistry letters.

[47]  Maykel Pérez González,et al.  Radial Distribution Function descriptors for predicting affinity for vitamin D receptor. , 2008, European journal of medicinal chemistry.

[48]  S. Gayen,et al.  Structural requirements of some derivatives based on natural alkaloid lycorine for their dengue inhibitory activity to accelerate dengue drug discovery efforts , 2016 .

[49]  Sk Abdul Amin,et al.  Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques , 2018, Journal of biomolecular structure & dynamics.

[50]  I. Edafiogho,et al.  Novel Piperazino-Enaminones Suppress Pro-Inflammatory Cytokines and Inhibit Chemokine Receptor CCR2 , 2016, Inflammation.

[51]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[52]  R. Horuk,et al.  Chemokine Receptor Antagonists , 2000, Medicinal research reviews.