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QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method

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Abstract

The Monte Carlo method was used for quantitative structure–activity relationships modeling of 36 quinoline/isoquinoline derivatives acting as dipeptidyl peptidase-4 inhibitors. Quantitative structure–activity relationships models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system with one random split into the training and the test set. The statistical quality of the developed model was good. The best calculated quantitative structure–activity relationships model had the following statistical parameters: r 2 = 0.9573 for the training set and r 2 = 0.9079 for the test set. Structural indicators defined as molecular fragments responsible for increases and decreases in the inhibition activity were calculated. The computer-aided design of new compounds as potential dipeptidyl peptidase-4 inhibitors with the application of defined structural alerts was presented.

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References

  • Al-masri IM, Mohammad MK, Taha MO (2008) Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening. Chem Med Chem 3:1763–1779

    Article  CAS  PubMed  Google Scholar 

  • Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’Min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to?. J Med Chem 57:4977–5010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Colberg SR, Sigal RJ, Fernhall B, Regensteiner JG, Blissmer BJ, Rubin RR, Chasan-Taber L, Albright AL, Braun B (2010) Exercise and type 2 diabetes: The American College Of Sports Medicine and The American Diabetes Association: joint position statement executive summary. Diabetes Care 33:2692–2696

    Article  PubMed  PubMed Central  Google Scholar 

  • Drucker DJ (2003) Glucagon-like peptide-1 and the Islet β-cell: augmentation of cell proliferation and inhibition of apoptosis. Endocrinology 144:5145–5148

    Article  CAS  PubMed  Google Scholar 

  • Duchowicz PR, Comelli NC, Ortiz EV, Castro EA (2012) QSAR study for carcinogenicity in a large set of organic compounds. Curr Drug Saf 7:282–288

    Article  CAS  PubMed  Google Scholar 

  • Fowler MJ (2007) Diabetes treatment, part 1: diet and exercise. Clin Diabetes 25:105–109

    Article  Google Scholar 

  • Gorrell MD, Gysbers V, McCaughan GW (2001) CD26: a multifunctional integral membrane and secreted protein of activated lymphocytes. Scand J Immunol 54:249–264

    Article  CAS  PubMed  Google Scholar 

  • Gramatica P (2007) Principles of QSAR models validation: Internal and external. QSAR Comb Sci 26:694–701

    Article  CAS  Google Scholar 

  • Green J, Feinglos M (2007) Update on type 2 diabetes mellitus: understanding changes in the diabetes treatment paradigm. Int J Clin Pract 61:3–11

    Article  Google Scholar 

  • Hansch C, Hoekman D, Gao H (1996) Comparative QSAR: toward a deeper understanding of chemicobiological interactions. Chem Rev 96:1045–1076

    Article  CAS  PubMed  Google Scholar 

  • Huang J, Liu G, Li J et al. (2012) Synthesis, structure–activity relationship, and pharmacophore modeling studies of pyrazole-3-carbohydrazone derivatives as dipeptidyl peptidase IV inhibitors. Chem Biol Drug Des 79:897–906

    Article  PubMed  Google Scholar 

  • Jessen N, Goodyear LJ (2010) Diabetes: exercise and type 2 diabetes mellitus good for body and mind?. Nat Rev Endocrinol 6:303–304

    Article  PubMed  Google Scholar 

  • Jiang C, Han S, Chen T, Chen J (2012) 3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors. Acta Pharm Sin B 2:411–420

    Article  CAS  Google Scholar 

  • Jiang Y-K (2010) Molecular docking and 3D-QSAR studies on betaphenylalanine derivatives as dipeptidyl peptidase IV inhibitors. J Mol Model 16:1239–1249

    Article  CAS  PubMed  Google Scholar 

  • Kuhn B, Hennig M, Mattei P (2007) Molecular recognition of ligands in dipeptidyl peptidase IV. Curr Top Med Chem 7:609–619

    Article  CAS  PubMed  Google Scholar 

  • Lankas GR, Leiting B, Roy RS, Eiermann GJ et al. (2005) Dipeptidyl peptidase IV inhibition for the treatment of type 2 diabetes: potential importance of selectivity over dipeptidyl peptidases 8 and 9. Diabetes 54:2988–2994

    Article  CAS  PubMed  Google Scholar 

  • Maezaki H, Banno Y, Miyamoto Y, Moritou Y, Asakawa T, Kataoka O et al. (2011) Discovery of potent, selective, and orally bioavailable quinoline-based dipeptidyl peptidase IV inhibitors targeting Lys554. Bioorg Med Chem 19:4482–4498

    Article  CAS  PubMed  Google Scholar 

  • Murphy KG, Dhillo WS, Bloom SR (2006) Gut peptides in the regulation of food intake and energy homeostasis. Endocr Rev 27:719–727

    Article  CAS  PubMed  Google Scholar 

  • Murugesan V, Sethi N, Prabhakar YS, Katti SB (2011) CoMFA and CoMSIA of diverse pyrrolidine analogues as dipeptidyl peptidase IV inhibitors: active site requirements. Mol Divers 15:457–466

    Article  CAS  PubMed  Google Scholar 

  • Ojha PK, Mitra I, Das R, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107:194–205

    Article  CAS  Google Scholar 

  • Ojha PK, Roy K (2011a) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemom Intell Lab Syst 109:146–161

    Article  CAS  Google Scholar 

  • Ojha PK, Roy K (2011b) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemom Intell Lab 109:146–161

    Article  CAS  Google Scholar 

  • Patel BD, Ghate MD (2014) Recent approaches to medicinal chemistry and therapeutic potential of dipeptidyl peptidase-4 (DPP-4) inhibitors. Eur J Med Chem 74:574–605

    Article  CAS  PubMed  Google Scholar 

  • Patel BD, Ghate MD (2015) 3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods. Med Chem Res 24:1060–1069

    Article  CAS  Google Scholar 

  • Pissurlenkar RRS, Shaikh MS, Coutinho EC (2007) 3D-QSAR studies of dipeptidyl peptidase IV inhibitors using a docking based alignment. J Mol Model 13:1047–1071

    Article  CAS  PubMed  Google Scholar 

  • Pospisilik JA, Stafford SG, Demuth H-U, Brownsey R, Parkhouse H et al. (2002) Long-term treatment with the dipeptidyl peptidase IV inhibitor P32/98 causes sustained improvements in glucose tolerance, insulin sensitivity, hyperinsulinemia, and b-cell glucose responsiveness in VDF (fa/fa) zucker rats. Diabetes 51:943–950

    Article  CAS  PubMed  Google Scholar 

  • Rosenbloom AL, Joe JR, Young RS, Winter WE (1999) Emerging epidemic of type 2 diabetes in youth. Diabetes Care 22:345–354

    Article  CAS  PubMed  Google Scholar 

  • Rosenblum JS, Kozarich JW (2003) Prolyl peptidases: a serine protease subfamily with high potential for drug discovery. Curr Opin Chem Biol 7:496–504

    Article  CAS  PubMed  Google Scholar 

  • Roy K (2007) On some aspects of validation of predictive quantitative structure activity relationship models. Expert Opin Drug Dis 2:1567–1577

    Article  CAS  Google Scholar 

  • Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18–33

    Article  CAS  Google Scholar 

  • Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52:396–408

    Article  CAS  PubMed  Google Scholar 

  • Roy PP, Leonard JT, Roy K (2008) Exploring the impact of the size of training sets for the development of predictive QSAR models. Chemom Intell Lab Syst 90:31–42

    Article  CAS  Google Scholar 

  • Saqib U, Siddiqi MI (2009) 3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents. SAR QSAR Environ Res 20:519–535

    Article  CAS  PubMed  Google Scholar 

  • Sigal RJ, Kenny GP, Wasserman DH, Castaneda-Sceppa C, White RD (2006) Physical activity/exercise and type 2 diabetes: a consensus statement from the American Diabetes Association. Diabetes Care 29:1433–1438

    Article  PubMed  Google Scholar 

  • Talevi A, Bellera CL, Ianni MD, Duchowicz PR, Bruno-Blanch LE, Castro EA (2012) An integrated drug development approach applying topological descriptors. Curr Comput Aided Drug Des 8:172–181

    Article  CAS  PubMed  Google Scholar 

  • Thorens B (1995) Glucagon like peptide-1 and control of insulin secretion. Diabetes Metab 21:311–318

    CAS  Google Scholar 

  • Toropov AA, Toropova AP, Lombardo A, Roncaglioni A, Benfenati E, Gini G (2011) CORAL: building up the model for bioconcentration factor and defining it’s applicability domain. Eur J Med Chem 46:1400–1403

    Article  CAS  PubMed  Google Scholar 

  • Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92:31–37

    Article  CAS  PubMed  Google Scholar 

  • Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13:3494–3504

    Article  CAS  PubMed  Google Scholar 

  • Veselinović AM, Veselinović JB, Živković JV, Nikolić GM (2015) Application of SMILES notation based optimal descriptors in drug discovery and design. Curr Top Med Chem 15:1768–1779

    Article  PubMed  Google Scholar 

  • Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  • Weininger D (1990) SMILES. 3. Depict. Graphical depiction of chemical structures. J Chem Inf Comput Sci 30:237–243

    Article  CAS  Google Scholar 

  • Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29:97–101

    Article  CAS  Google Scholar 

  • Wu S-Y, Lu I-L, Tsai K-C, Chiang Y-K, Jiaang W-T, Wu S-H (2008) A three-dimensional pharmacophore model for dipeptidyl peptidase IV inhibitors. Eur J Med Chem 43:1603–1611

    Article  PubMed  Google Scholar 

  • Yang X, Li M, Su Q, Wu M, Gu T, Lu W (2013) QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type-2 diabetes. Med Chem Res 22:5274–5283

    Article  CAS  Google Scholar 

  • Zeng J, Liu G, Tang Y, Jiang HD (2007) QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA. J Mol Model 13:993–1000

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work has been supported by the Ministry of Education and Science, the Republic of Serbia, under Project Number 43012.

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Correspondence to Aleksandar M. Veselinović.

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Sokolović, D., Ranković, J., Stanković, V. et al. QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med Chem Res 26, 796–804 (2017). https://doi.org/10.1007/s00044-017-1792-2

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  • DOI: https://doi.org/10.1007/s00044-017-1792-2

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