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Fragment-based strategy for structural optimization in combination with 3D-QSAR

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Abstract

Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure–activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.

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References

  1. Desjarlais RL (2011) Using computational techniques in fragment-based drug discovery. Methods Enzymol 493:137–155

    Article  CAS  Google Scholar 

  2. Kumar A, Voet A, Zhang KY (2012) Fragment based drug design: from experimental to computational approaches. Curr Med Chem 19:5128–5147

    Article  CAS  Google Scholar 

  3. Baker M (2013) Fragment-based lead discovery grows up. Nat Rev Drug Discov 12:5–7

    Article  CAS  Google Scholar 

  4. Villar HO, Hansen MR (2007) Computational techniques in fragment based drug discovery. Curr Top Med Chem 7:1509–1513

    Article  CAS  Google Scholar 

  5. Law R, Barker O, Barker JJ, Hesterkamp T, Godemann R, Andersen O, Fryatt T, Courtney S, Hallett D, Whittaker M (2009) The multiple roles of computational chemistry in fragment-based drug design. J Comput Aided Mol Des 23:459–473

    Article  CAS  Google Scholar 

  6. Sheng C, Zhang W (2013) Fragment informatics and computational fragment-based drug design: an overview and update. Med Res Rev 33:554–598

    Article  CAS  Google Scholar 

  7. Yuan H, Lu T, Ran T, Liu H, Lu S, Tai W, Leng Y, Zhang W, Wang J, Chen Y (2011) Novel strategy for three-dimensional fragment-based lead discovery. J Chem Inf Model 51:959–974

    Article  CAS  Google Scholar 

  8. Erlanson DA, McDowell RS, O’Brien T (2004) Fragment-based drug discovery. J Med Chem 47:3463–3482

    Article  CAS  Google Scholar 

  9. 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

    Article  CAS  Google Scholar 

  10. 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 37:4130–4146

    Article  CAS  Google Scholar 

  11. Katz JD, Jewell JP, Guerin DJ, Lim J, Dinsmore CJ, Deshmukh SV, Pan BS, Marshall CG, Lu W, Altman MD, Dahlberg WK, Davis L, Falcone D, Gabarda AE, Hang G, Hatch H, Holmes R, Kunii K, Lumb KJ, Lutterbach B, Mathvink R, Nazef N, Patel SB, Qu X, Reilly JF, Rickert KW, Rosenstein C, Soisson SM, Spencer KB, Szewczak AA, Walker D, Wang W, Young J, Zeng Q (2011) Discovery of a 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one (MK-2461) inhibitor of c-Met kinase for the treatment of cancer. J Med Chem 54:4092–4108

    Article  CAS  Google Scholar 

  12. Underiner TL, Herbertz T, Miknyoczki SJ (2010) Discovery of small molecule c-Met inhibitors: evolution and profiles of clinical candidates. Anticancer Agents Med Chem 10:7–27

    Article  CAS  Google Scholar 

  13. Zou HY, Li Q, Lee JH, Arango ME, McDonnell SR, Yamazaki S, Koudriakova TB, Alton G, Cui JJ, Kung PP, Nambu MD, Los G, Bender SL, Mroczkowski B, Christensen JG (2007) An orally available small-molecule inhibitor of c-Met, PF-2341066, exhibits cytoreductive antitumor efficacy through antiproliferative and antiangiogenic mechanisms. Cancer Res 67:4408–4417

    Article  CAS  Google Scholar 

  14. Bellon SF, Kaplan-Lefko P, Yang Y, Zhang Y, Moriguchi J, Rex K, Johnson CW, Rose PE, Long AM, O’Connor AB, Gu Y, Coxon A, Kim TS, Tasker A, Burgess TL, Dussault I (2008) c-Met inhibitors with novel binding mode show activity against several hereditary papillary renal cell carcinoma-related mutations. J Biol Chem 283:2675–2683

    Article  CAS  Google Scholar 

  15. Pan BS, Chan GK, Chenard M, Chi A, Davis LJ, Deshmukh SV, Gibbs JB, Gil S, Hang G, Hatch H, Jewell JP, Kariv I, Katz JD, Kunii K, Lu W, Lutterbach BA, Paweletz CP, Qu X, Reilly JF, Szewczak AA, Zeng Q, Kohl NE, Dinsmore CJ (2010) MK-2461, a novel multitargeted kinase inhibitor, preferentially inhibits the activated c-Met receptor. Cancer Res 70:1524–1533

    Article  CAS  Google Scholar 

  16. Berthou S, Aebersold DM, Schmidt LS, Stroka D, Heigl C, Streit B, Stalder D, Gruber G, Liang C, Howlett AR, Candinas D, Greiner RH, Lipson KE, Zimmer Y (2004) The Met kinase inhibitor SU11274 exhibits a selective inhibition pattern toward different receptor mutated variants. Oncogene 23:5387–5393

    Article  CAS  Google Scholar 

  17. Qi J, McTigue MA, Rogers A, Lifshits E, Christensen JG, Janne PA, Engelman JA (2011) Multiple mutations and bypass mechanisms can contribute to development of acquired resistance to MET inhibitors. Cancer Res 71:1081–1091

    Article  CAS  Google Scholar 

  18. Dussault I, Bellon SF (2008) c-Met inhibitors with different binding modes: two is better than one. Cell Cycle 7:1157–1160

    Article  CAS  Google Scholar 

  19. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341

    Article  CAS  Google Scholar 

  20. Rickert KW, Patel SB, Allison TJ, Byrne NJ, Darke PL, Ford RE, Guerin DJ, Hall DL, Kornienko M, Lu J, Munshi SK, Reid JC, Shipman JM, Stanton EF, Wilson KJ, Young JR, Soisson SM, Lumb KJ (2011) Structural basis for selective small molecule kinase inhibition of activated c-Met. J Biol Chem 286:11218–11225

    Article  CAS  Google Scholar 

  21. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565

    Article  CAS  Google Scholar 

  22. Sandor M, Kiss R, Keseru GM (2010) Virtual fragment docking by Glide: a validation study on 190 protein-fragment complexes. J Chem Inf Model 50:1165–1172

    Article  CAS  Google Scholar 

  23. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69:146–148

    Article  CAS  Google Scholar 

  24. Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, p38, and HIV protease. J Med Chem 47:2768–2775

    Article  CAS  Google Scholar 

  25. Rees DC, Congreve M, Murray CW, Carr R (2004) Fragment-based lead discovery. Nat Rev Drug Discov 3:660–672

    Article  CAS  Google Scholar 

  26. Larsson EA, Jansson A, Ng FM, Then SW, Panicker R, Liu B, Sangthongpitag K, Pendharkar V, Tai SJ, Hill J, Dan C, Ho SY, Cheong WW, Poulsen A, Blanchard S, Lin GR, Alam J, Keller TH, Nordlund P (2013) Fragment-based ligand design of novel potent inhibitors of tankyrases. J Med Chem 56:4497–4508

    Article  CAS  Google Scholar 

  27. Wyatt PG, Woodhead AJ, Berdini V, Boulstridge JA, Carr MG, Cross DM, Davis DJ, Devine LA, Early TR, Feltell RE, Lewis EJ, McMenamin RL, Navarro EF, O’Brien MA, O’Reilly M, Reule M, Saxty G, Seavers LC, Smith DM, Squires MS, Trewartha G, Walker MT, Woolford AJ (2008) Identification of N-(4-piperidinyl)-4-(2,6-dichlorobenzoylamino)-1H-pyrazole-3-carboxamide (AT7519), a novel cyclin dependent kinase inhibitor using fragment-based X-ray crystallography and structure based drug design. J Med Chem 51:4986–4999

    Article  CAS  Google Scholar 

  28. Dey F, Caflisch A (2008) Fragment-based de novo ligand design by multiobjective evolutionary optimization. J Chem Inf Model 48:679–690

    Article  CAS  Google Scholar 

  29. Urich R, Wishart G, Kiczun M, Richters A, Tidten-Luksch N, Rauh D, Sherborne B, Wyatt PG, Brenk R (2013) De novo design of protein kinase inhibitors by in silico identification of hinge region-binding fragments. ACS Chem Biol 8:1044–1052

    Article  CAS  Google Scholar 

  30. Chen H, Yang Z, Ding C, Chu L, Zhang Y, Terry K, Liu H, Shen Q, Zhou J (2013) Fragment-based drug design and identification of HJC0123, a novel orally bioavailable STAT3 inhibitor for cancer therapy. Eur J Med Chem 62:498–507

    Article  CAS  Google Scholar 

  31. Congreve M, Chessari G, Tisi D, Woodhead AJ (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680

    Article  CAS  Google Scholar 

  32. Fischer M, Hubbard RE (2009) Fragment-based ligand discovery. Mol Interv 9:22–30

    Article  CAS  Google Scholar 

  33. Fattori D, Squarcia A, Bartoli S (2008) Fragment-based approach to drug lead discovery: overview and advances in various techniques. Drugs R D 9:217–227

    Article  CAS  Google Scholar 

  34. Rognan D (2012) Fragment-based approaches and computer-aided drug discovery. Top Curr Chem 317:201–222

    Article  CAS  Google Scholar 

  35. Kolb P, Kipouros CB, Huang D, Caflisch A (2008) Structure-based tailoring of compound libraries for high-throughput screening: discovery of novel EphB4 kinase inhibitors. Proteins 73:11–18

    Article  CAS  Google Scholar 

  36. Zhu T, Lee H, Lei H, Jones C, Patel K, Johnson ME, Hevener KE (2013) Fragment-based drug discovery using a multidomain, parallel MD-MM/PBSA screening protocol. J Chem Inf Model 53(3):560–572

    Article  CAS  Google Scholar 

  37. Zhang Y, Liu H, Jiao Y, Yuan H, Wang F, Lu S, Yao S, Ke Z, Tai W, Jiang Y, Chen Y, Lu T (2012) De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein–ligand interaction fingerprint, and ADMET prediction. Mol Divers 16:787–802

    Article  Google Scholar 

  38. Northrup AB, Katcher MH, Altman MD, Chenard M, Daniels MH, Deshmukh SV, Falcone D, Guerin DJ, Hatch H, Li C, Lu W, Lutterbach B, Allison TJ, Patel SB, Reilly JF, Reutershan M, Rickert KW, Rosenstein C, Soisson SM, Szewczak AA, Walker D, Wilson K, Young JR, Pan BS, Dinsmore CJ (2013) Discovery of 1-[3-(1-methyl-1H-pyrazol-4-yl)-5-oxo-5H-benzo[4,5]cyclohepta[1,2-b]pyridin-7-yl]-N-(pyridin-2-ylmethyl)methanesulfonamide (MK-8033): a specific c-Met/Ron dual kinase inhibitor with preferential affinity for the activated state of c-Met. J Med Chem 56:2294–2310

    Article  CAS  Google Scholar 

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Acknowledgments

The authors thank Dr. Huifang Li (the University of British Columbia) for her help in drafting the manuscript. This work was supported by National Natural Science Foundation of China (81172933, 21102181 and 30973609); Fundamental Research Funds for the Central Universities (2J10004, JKZ2011004 and JKY2011020); Jiangsu Provincial Graduate Innovation Research Foundation (CXZZ12_0315); State Key Laboratory of Natural Medicines (China Pharmaceutical University) Foundation for major research projects (SKLNMZZ201205); and Specialized Research Fund for the Doctoral Program of Higher Education (No. 20100096110007).

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Correspondence to Yadong Chen or Tao Lu.

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Haoliang Yuan and Wenting Tai have contributed equally to this work.

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Yuan, H., Tai, W., Hu, S. et al. Fragment-based strategy for structural optimization in combination with 3D-QSAR. J Comput Aided Mol Des 27, 897–915 (2013). https://doi.org/10.1007/s10822-013-9687-x

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  • DOI: https://doi.org/10.1007/s10822-013-9687-x

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