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An integrated QSAR modeling approach to explore the structure–property and selectivity relationships of N-benzoyl-l-biphenylalanines as integrin antagonists

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Abstract

Integrins \({\upalpha }_{4}{\upbeta }_{7}\) and \({\upalpha }_{4}{\upbeta }_{1}\) are important targets to treat different inflammatory diseases, such as multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, atherosclerosis, and asthma. Despite being valuable targets, only a few work has been reported to date regarding molecular modeling studies on these integrins. Not only that, none of these reports addressed the selectivity issue between integrins \({\upalpha }_{4}{\upbeta }_{7}\) and \({\upalpha }_{4}{\upbeta }_{1}\). Therefore, a major challenge regarding the design and discovery of selective integrin antagonists remains. In this study, a series of 142 N-benzoyl-l-biphenylalanines having both integrin \({\upalpha }_{4}{\upbeta }_{7}\) and \({\upalpha }_{4}{\upbeta }_{1}\) inhibitory activities were considered for a variety of QSAR approaches including regression and classification-based 2D-QSARs, Hologram QSARs, 3D-QSAR CoMFA and CoMSIA studies to identify the structural requirements of these integrin antagonists. All these QSAR models were statistically validated and subsequently correlated with each other to get a detailed understanding of the activity and selectivity profiles of these molecules.

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References

  1. Campbell ID, Humphries MJ (2011) Integrin structure, activation, and interactions. Cold Spring Harb Perspect Biol 3:a004994. https://doi.org/10.1101/cshperspect.a004994

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hynes RO (2002) Integrins: bidirectional, allosteric signaling machines. Cell 110:673–687. https://doi.org/10.1016/S0092-8674(02)00971-6

    Article  CAS  PubMed  Google Scholar 

  3. Nermut MV, Green NM, Eason P, Yamada SS, Yamada KM (1998) Electron microscopy and structural model of human fibronectin receptor. EMBO J 7:4093–4099

    Google Scholar 

  4. Meyer A, Auernheimer J, Modlinger A, Kessler H (2006) Targeting RGD recognizing integrins: drug development, biomaterial research, tumor imaging and targeting. Curr Pharm Des 12:2723–2747. https://doi.org/10.2174/138161206777947740

    Article  CAS  PubMed  Google Scholar 

  5. Girard A, Rochereau N, Roblin X, Genin C, Paul S (2015) Targeting and role of \({\upalpha }_{4}{\upbeta }_{7}\) integrin in the pathophysiology of IBD and HIV infection. Med Sci 31:895–903. https://doi.org/10.1051/medsci/20153110016

    Google Scholar 

  6. Wu C, Fields AJ, Kapteijn BA, McDonald JA (1995) The role of alpha 4 beta 1 integrin in cell motility and fibronectin matrix assembly. J Cell Sci 108:821–829

    CAS  PubMed  Google Scholar 

  7. Chigaev A, Wu Y, Williams DB, Smagley Y, Sklar LA (2011) Discovery of very late antigen-4 (VLA-4, alpha 4 beta 1 integrin) allosteric antagonists. J Biol Chem 286:5455–5463. https://doi.org/10.1074/jbc.M110.162636

    Article  CAS  PubMed  Google Scholar 

  8. Porter JR, Archibald SC, Brown JA, Childs K, Critchley D, Head JC, Hutchinson B, Parton TA, Robinson MK, Shock A, Warrellow GJ, Zomaya A (2002) Discovery and evaluation of N-(triazin-1,3,5-yl) phenylalanine derivatives as VLA-4 integrin antagonists. Bioorg Med Chem Lett 12:1591–1594. https://doi.org/10.1016/S0960-894X(02)00237-8

    Article  CAS  PubMed  Google Scholar 

  9. Villablanca EJ, Cassani B, von Andrian UH, Mora JR (2011) Blocking lymphocyte localization to the gastrointestinal mucosa as a therapeutic strategy for IBD. Gastroenterology 140:1776–1784. https://doi.org/10.1053/j.gastro.2011.02.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sircar I, Gudmundsson KS, Martin R, Linq J, Nomura S, Javakumar H, Teeqarden BR, Nowlin DM, Cardarelli PM, Mah JR, Connell S, Griffith RC, Lazarides E (2002) Synthesis and SAR of N-benzoyl-L-biphenylalanine derivatives: discovery of TR-14035, a dual alpha(4)beta(7)/alpha(4)beta(1) integrin antagonist. Bioorg Med Chem 10:2051–2066. https://doi.org/10.1016/S0968-0896(02)00021-4

    Article  CAS  PubMed  Google Scholar 

  11. Berlin C, Berq EL, Briskin MJ, Andrew DP, Kilshaw PJ, Holzmann B, Weissman IL, Hamann A, Butcher EC (1993) Alpha 4 beta 7 integrin mediates lymphocyte binding to the mucosal vascular addressin MAdCAM-1. Cell 74:185–195. https://doi.org/10.1016/0092-8674(93)90305-A

    Article  CAS  PubMed  Google Scholar 

  12. Miyake K, Weissman IL, Greenberger JS, Kincade PW (1991) Evidence for a role of the integrin VLA-4 in lympho-hemopoiesis. J Exp Med 173:599–607. https://doi.org/10.1084/jem.173.3.599

    Article  CAS  PubMed  Google Scholar 

  13. Rossi F, Newsome SD, Viscidi R (2015) Molecular diagnostic tests to predict the risk of progressive multifocal leukoencephalopathy in natalizumab-treated multiple sclerosis patients. Mol Cell Probes 29:54–62. https://doi.org/10.1016/j.mcp.2014.11.007

    Article  CAS  PubMed  Google Scholar 

  14. Shimaoka M, Springer TA (2003) Therapeutic antagonists and conformational regulation of integrin function. Nat Rev Drug Discov 2:703–716. https://doi.org/10.1038/nrd1174

    Article  CAS  PubMed  Google Scholar 

  15. Woodside DG, Vanderslice P (2008) Cell adhesion antagonists: therapeutic potential in asthma and chronic obstructive pulmonary disease. BioDrugs 22:85–100. https://doi.org/10.2165/00063030-200822020-00002

    Article  CAS  PubMed  Google Scholar 

  16. Hansch C, Hoekman D, Leo A, Weininger D, Selassie CD (2002) Chem-bioinformatics: comparative QSAR at the interface between chemistry and biology. Chem Rev 102:783–812. https://doi.org/10.1021/cr0102009

    Article  CAS  PubMed  Google Scholar 

  17. Amin SA, Adhikari N, Jha T, Gayen S (2016) First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington’s disease: a proposal to chemists!. Bioorg Med Chem Lett 26:5712–5718. https://doi.org/10.1016/j.bmcl.2016.10.058

    Article  PubMed  Google Scholar 

  18. Amin SA, Gayen S (2016) Modelling the cytotoxic activity of pyrazolo-triazole hybrids using descriptors calculated from the open source tool “PaDEL-descriptor”. J Taibah Univ Sci 10:896–905. https://doi.org/10.1016/j.jtusci.2016.04.009

    Article  Google Scholar 

  19. Verma RP, Hansch C (2009) Camptothecins: a SAR/QSAR study. Chem Rev 109:213–235. https://doi.org/10.1021/cr0780210

    Article  CAS  PubMed  Google Scholar 

  20. Singh J, van Vlijmen H, Liao Y, Lee W-C, Cornebise M, Harris M, Shu I, Gill A, Cuervo JH, Abraham WM, Adams SP (2002) Identification of potent and novel \({\upalpha }4{\upbeta }1\) antagonists using in silico screening. J Med Chem 45:2988–2993. https://doi.org/10.1021/jm020054e

    Article  CAS  PubMed  Google Scholar 

  21. Singh J, van Vlijmen H, Lee WC, Liao Y, Lin KC, Ateeq H, Cuervo J, Zimmerman C, Hammond C, Karpusas M, Palmer R, Chattopadhyay T, Adams SP (2002) 3D QSAR (COMFA) of a series of potent and highly selective VLA-4 antagonists. J Comp Aided Mol Des 16:201–211. https://doi.org/10.1023/A:1020130418084

    Article  CAS  Google Scholar 

  22. Khandelwal A, Narayanan R, Gopalkrishnan B (2003) 3D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists. Bioorg Med Chem 11:4235–4244. https://doi.org/10.1016/S0968-0896(03)00408-5

    Article  CAS  PubMed  Google Scholar 

  23. Macchiarulo A, Costantino G, Meniconi M, Pleban K, Ecker G, Bollecchi D, Pellicciari R (2004) Insights into phenylalanine derivatives recognition of VLA-4 integrin: from a pharmacophoric study to 3D-QSAR and molecular docking analyses. J Chem Inf Comput Sci 44:1829–1839. https://doi.org/10.1021/ci049914l

    Article  CAS  PubMed  Google Scholar 

  24. Bhargava D, Karthikeyan C, Moorthy NSHN, Trivedi P (2009) Quantitative structure activity relationship studies of piperazinyl phenylalanine derivatives as VLA-4/VCAM-1 inhibitors. Med Chem 5:446–454. https://doi.org/10.2174/157340609789117822

    Article  CAS  PubMed  Google Scholar 

  25. Hutt OE, Saubern S, Winkler DA (2011) Modeling the molecular basis for a4b1 integrin antagonism. Bioorg Med Chem 19:5903–5911. https://doi.org/10.1016/j.bmc.2011.08.011

    Article  CAS  PubMed  Google Scholar 

  26. Thangapandian S, John S, Sakkiah S, Lee KW (2011) Discovery of potential integrin VLA-4 antagonists using pharmacophore modeling, virtual screening and molecular docking studies. Chem Biol Drug Des 78:289–300. https://doi.org/10.1111/j.1747-0285.2011.01127.x

    Article  CAS  PubMed  Google Scholar 

  27. http://adisinsight.springer.com/drugs/800012563. Accessed 9 Dec 2016

  28. Faruhama A, Hasunuma K, Aoki Y (2015) Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols. SAR QSAR Environ Res 26:301–323. https://doi.org/10.1080/1062936X.2015.1032347

    Article  Google Scholar 

  29. Lessigiarska I, Worth AP, Netzeva TI, Dearden JC, Cronon MT (2006) Quantitative structure–activity–activity and quantitative structure–activity investigations of human and rodent toxicity. Chemosphere 65:1878–1887. https://doi.org/10.1016/j.chemosphere.2006.03.067

    Article  CAS  PubMed  Google Scholar 

  30. Ambure P, Roy K (2014) Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer’s disease: an in silico approach. RSC Adv 4:6702–6709. https://doi.org/10.1039/C3RA46861E

    Article  CAS  Google Scholar 

  31. Halder AK, Amin SA, Jha T, Gayen S (2017) Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches. SAR QSAR Environ Res 28:253–273. https://doi.org/10.1080/1062936X.2017.1302991

    Article  CAS  PubMed  Google Scholar 

  32. Amin SA, Adhikari N, Gayen S, Jha T (2016) Insight into the structural requirements of theophylline-based aldehyde dehydrogenase lAl (ALDHlAl) inhibitors through multi-QSAR modeling and molecular docking approaches. Curr Drug Discov Technol 13:84–100. https://doi.org/10.2174/1570163813666160429115628

    Article  PubMed  Google Scholar 

  33. Mondal C, Halder AK, Adhikari N, Jha T (2014) Structural findings of cinnolines as anti-schizophrenic PDE10A inhibitors through comparative chemometric modeling. Mol Divers 18:655–671. https://doi.org/10.1007/s11030-014-9523-9

    Article  CAS  PubMed  Google Scholar 

  34. Adhikari N, Maiti MK, Jha T (2010) Predictive comparative QSAR modelling of (phenylpiperazinyl-alkyl) oxindoles as selective 5-HT1A antagonists by stepwise regression, PCRA, FA-MLR and PLS techniques. Eur J Med Chem 45:1119–1127. https://doi.org/10.1016/j.ejmech.2009.12.011

    Article  CAS  PubMed  Google Scholar 

  35. Adhikari N, Maiti MK, Jha T (2010) Exploring structural requirements of 1-N-substituted thiocarbamoyl-3-phenyl-2-pyrazolines as antiamoebic agents using comparative QSAR modelling. Bioorg Med Chem Lett 20:4021–4026. https://doi.org/10.1016/j.bmcl.2010.05.098

    Article  CAS  PubMed  Google Scholar 

  36. Debnath B, Gayen S, Samanta S, Basu A, Ghosh B, Jha T (2006) QSAR study on some synthesized and biologically evaluated glutamine analogs as possible anticancer agents. Ind J Chem 45A:93–99

    CAS  Google Scholar 

  37. ChemDraw Ultra 8.0, Cambridge Soft Corporation, USA. http://www.cambridgesoft.com

  38. DRAGON-Software for the calculation of molecular descriptors version 6, TALETE srl, Via V. Pisani, 13, 20124 Milano, Italy. http://www.talete.mi.it/products/dragon_description.htm

  39. Awasthi M, Amin SA, Shukla V, Jain S, Patil UK, Gayen S (2016) Structural requirements of some derivatives based on natural alkaloid lycorine for their dengue inhibitory activity to accelerate dengue drug discovery efforts. Indian J Nat Prod Resour 7:221–228

    Google Scholar 

  40. Amin SA, Adhikari N, Bhargava S, Jha T, Gayen S (2017) Designing potential antitrypanosomal thiazol-2-ethylamines through predictive regression based and classification based QSAR analyses. Curr Drug Discov Technol 14:39–52. https://doi.org/10.2174/1570163813666161117144137

    Google Scholar 

  41. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11:10–18. https://doi.org/10.1145/1656274.1656278

  42. The simple, user-friendly and reliable online standalone tools freely available at http://dtclab.webs.com/software-tools. Accessed 1 Dec 2016

  43. Klon AE, Lowrie JF, Diller DJ (2006) Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J Chem Inf Model 46:1945–1956. https://doi.org/10.1021/ci0601315

    Article  CAS  PubMed  Google Scholar 

  44. Discovery Studio 3.0, Accelrys Inc., San Diego, USA. http://www.accelrys.com

  45. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  46. Waller CL (2004) A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds. J Chem Inf Comput Sci 44:758–765. https://doi.org/10.1021/ci0342526

    Article  CAS  PubMed  Google Scholar 

  47. SYBYL-X 2.0. Certara USA, Inc., USA. http://www.certara.com

  48. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967. https://doi.org/10.1021/ja00226a005

    Article  CAS  PubMed  Google Scholar 

  49. Adhikari N, Halder AK, Mondal C, Jha T (2014) Structural findings of quinolone carboxylic acids in cytotoxic, antiviral, and anti-HIV-1 integrase activity through validated comparative molecular modeling studies. Med Chem Res 23:3096–3127. https://doi.org/10.1007/s00044-013-0897-5

    Article  CAS  Google Scholar 

  50. Amin SA, Adhikari N, Jha T, Gayen S (2016) Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches. Can J Chem 94:637–644. https://doi.org/10.1139/cjc-2016-0050

    Article  Google Scholar 

  51. Ojha PK, Roy K (2011) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemom Intell Lab Sys 109:146–161. https://doi.org/10.1016/j.chemolab.2011.08.007

    Article  CAS  Google Scholar 

  52. Multiregress, a software developed by Dept. of Pharm. Tech., Jadavpur University, India

  53. Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29. https://doi.org/10.1016/j.chemolab.2015.04.013

    Article  CAS  Google Scholar 

  54. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics (2 volumes). Wiley, Weinheim

    Book  Google Scholar 

  55. Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graph Model 20:269–276. https://doi.org/10.1016/S1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  56. Ambure P, Roy K (2016) Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against A\({\upbeta }\) plaques in Alzheimer’s disease: a predictive QSAR approach. RSC Adv 6:28171–28186. https://doi.org/10.1039/c6ra04104c

  57. Mirfazli SS, Khoshneviszadeh M, Jeiroudi M, Foroumadi A, Kobarfard F, Shafiee A (2016) Synthesis and antimicrobial evaluation of hydrazones derived from 4-methylbenzenesulfonohydrazide in aqueous medium. Med Chem Res 25:1–18. https://doi.org/10.1007/s00044-015-1440-7

    Article  CAS  Google Scholar 

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Acknowledgements

NA is grateful to University Grants Commission (UGC), New Delhi, India, for providing Rajiv Gandhi National Fellowship (Grant No. F1-17.1/2014-15/RGNF-2014-15-SC-WES-73725/SA-III/Website). SG thanks to the UGC, New Delhi, India, for awarding UGC-Start up Grant (No. F.30-106/2015-BSR). Authors are thankful to the authority of Jadavpur University, India, and Dr. Harisingh Gour University, India, for providing research facilities.

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Amin, S.A., Adhikari, N., Bhargava, S. et al. An integrated QSAR modeling approach to explore the structure–property and selectivity relationships of N-benzoyl-l-biphenylalanines as integrin antagonists. Mol Divers 22, 129–158 (2018). https://doi.org/10.1007/s11030-017-9789-9

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