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  1. American Journal of Drug Discovery and Development
  2. Vol 1 (2), 2011
  3. 85-104
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American Journal of Drug Discovery and Development

Year: 2011 | Volume: 1 | Issue: 2 | Page No.: 85-104
DOI: 10.3923/ajdd.2011.85.104

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Research Article

Quantitative Structure Activity Relationship Analysis of N-(mercaptoalkanoyl)- and [(acylthio)alkanoyl] Glycine Derivatives as ACE Inhibitors

Sarvesh Kumar Paliwal
Department of Pharmacy, Banasthali University, Banasthali, Tonk-304022, Rajasthan, India

Anubhuti Pandey
Department of Pharmacy, Banasthali University, Banasthali, Tonk-304022, Rajasthan, India

Shailendra Paliwal
Department of Pharmacy, L.L.R.M. Medical College, Meerut, U.P., India

Angiotensin Converting Enzyme (ACE) inhibitors have been acknowledged as the first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. The present study aims to develop Quantitative Structure-Activity Relationship (QSAR) models for a series of N- (mercaptoalkanoyl)- and [(acylthio)alkanoyl]glycines derivatives for the prediction of the activity of novel compounds as ACE inhibitors. Multiple Linear Regression (MLR) and Partial Least Square (PLS) analyses were used to establish the QSAR between ACE inhibitory activities and molecular descriptors. The models generated by MLR and PLS analyses were comparable and demonstrated good predictive ability with statistical values of R2 = 0.76, F = 33.35 and s = 0.328 for MLR and statistical significance value of 0.536 and fraction of variance explained = 0.733 for PLS analysis. Cross-validated R2 values of 0.706 (for MLR) and 0.712 (for PLS) were obtained which indicates good internal predictive capacity of the models. Validation through external test set provided R2 values of 0.698 and 0.726 for the MLR and PLS analyses, respectively. The results obtained indicate the importance of steric (Verloop B1, K-alpha 3) hydrophobic (lipole Z component) and topological (Wiener index) descriptors in determining the activity of ACE inhibitors. Also, druggability of the compounds was checked by assessing the Lipinski’s rule of five and no violation to this rule was found. The utility of this study is for the effective design of novel, more potent ACE inhibitors as it provides important information about the geometrical and structural requirements for the interaction of glycine derivatives with ACE.
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How to cite this article

Sarvesh Kumar Paliwal, Anubhuti Pandey and Shailendra Paliwal, 2011. Quantitative Structure Activity Relationship Analysis of N-(mercaptoalkanoyl)- and [(acylthio)alkanoyl] Glycine Derivatives as ACE Inhibitors. American Journal of Drug Discovery and Development, 1: 85-104.

DOI: 10.3923/ajdd.2011.85.104

URL: https://scialert.net/abstract/?doi=ajdd.2011.85.104

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References


  1. Bajorath, J., 2005. Molecular Similarity Methods and Quantitative Structure-Activity Relationship Models as Tools for Virtual Screening. In: Drug Discovery Handbook, Gad, S.C. (Ed.). Wiley Interscience, New Jersey pp: 87-122.

  2. Bersuker, I.B., S. Bahcueci and J.E. Boggs, 2000. Improved electron-conformational method of pharmacophore identification and bioactivity prediction. Application to angiotensin converting enzyme inhibitors. J. Chem. Inform. Comput. Sci., 40: 1363-1376.
    CrossRef

  3. Bohacek, R., S. De Lombaert, C. McMartin, J. Priestle and M. Grutter, 1996. Three-dimensional models of ACE and NEP inhibitors and their use in the design of potent dual ACE/NEP inhibitors. J. Am. Chem. Soc., 118: 8231-8249.
    CrossRef

  4. DePriest, S.A., D. Mayer, C.B. Naylor and G.R. Marshall, 1993. 3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries. J. Am. Chem. Soc., 115: 5372-5384.
    CrossRef

  5. Fogo, A.B., 1999. New insights into the renin angiotensin system and hypertensive renal disease. Curr. Hypertens. Rep., 1: 187-194.
    CrossRefPubMedDirect Link

  6. Golbraikh, A., D. Bonchev and A. Tropsha, 2001. Novel chirality descriptors derived from molecular topology. J. Chem. Inform. Comput. Sci., 41: 147-158.
    CrossRefDirect Link

  7. Gupta, S.P. and S. Kumaran, 2006. Quantitative structure-activity relationship studies on benzodiazepine hydroxamic acid inhibitors of matrix metalloproteinases and tumor necrosis factor-α converting enzyme. Asian J. Biochem., 1: 47-56.
    CrossRefDirect Link

  8. Hansch, C. and T. Fujita, 1964. p-σ-π analysis, a method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc., 86: 1616-1626.
    CrossRefDirect Link

  9. Hoffman, B., S.J. Cho, W. Zheng, S. Wyrick, D.E. Nichols, R.B. Mailman and A. Tropsha, 1999. Quantitative structure-activity relationship modeling of dopamine D1 antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares and K nearest neighbor methods. J. Med. Chem., 42: 3217-3226.
    CrossRefDirect Link

  10. Jackson, E.K., 2001. Renin and Angiotensin. In: Goodman and Gilman's the Pharmacologic Basis of Therapeutics, Hardman, J.G., L.E. Limberd and A.G. Gilman (Eds.). 10th Edn., McGraw-Hill, New York, pp: 809-829.

  11. Jain, A. and S.C. Chaturvedi, 2008. Rationalization of physicochemical property of some substituted benzimidazole bearing acidic heterocyclic towards angiotensin ii antagonist: A QSAR approach. Asian J. Biochem., 3: 330-336.
    CrossRefDirect Link

  12. Jamloki, A., C. Karthikeyan, S.K. Sharma, N.S.H.N. Moorthy and P. Trivedi, 2006. QSAR studies on some GSK-3α Inhibitory 6-aryl-pyrazolo (3,4-b) pyridines. Asian J. Biochem., 1: 236-243.
    CrossRefDirect Link

  13. Karthikeyan, C., D. Santosh, N.S.H.N. Moorthy and P. Trivedi, 2007. QSAR analysis of HIV-1 reverse transcriptase inhibitory 5-Alkyl-2-[(Aryl and Alkyloxylcarbonylmethyl) Thio]-6-(1-Napthylmethyl) Pyrimidin-4 (3H)-Ones. Int. J. Virol., 3: 19-27.
    CrossRefDirect Link

  14. Karthikeyan, C., P.M. Kumar, N.S.H.N. Moorthy, S.K. Shrivastava and T. Piyush, 2006. Quantitative structure activity relationships of some selective inhibitors of glucagon receptor: A hansch approach. Asian J. Biochem., 1: 307-315.
    CrossRefDirect Link

  15. Kier, L.B. and L.H. Hall, 1999. The Kappa Indices for Modeling Molecular Shape and Flexibility. In: Topological Indices and Related Descriptors in QSAR and QSPR, Devillers, J. and A.T. Balaban (Eds.). Gordon and Breach Science Publishers, The Netherlands, pp: 455-489.

  16. Klocker, J., B. Wailzer, G. Buchbauer and P. Wolschann, 2002. Bayesian neural networks for aroma classification. J. Chem. Inf. Comput. Sci., 42: 1443-1449.
    CrossRefDirect Link

  17. Kovatcheva, A., G. Buchbauer, A. Golbraikh and P. Wolschann, 2003. QSAR modeling of alpha campholenic derivatives with sandalwood odor. J. Chem. Inform. Comput. Sci., 43: 259-266.
    CrossRefPubMedDirect Link

  18. Kubinyi, H., 1996. Evolutionary variable selection in regression and PLS analyses. J. Chemometr., 10: 119-133.
    CrossRefDirect Link

  19. Kumar, R., R. Sharma, K. Bairwa, R.K. Roy, A. Kumar and A. Baruwa, 2010. Modern development in ACE inhibitors. Der Pharmacia Lett., 2: 388-419.
    Direct Link

  20. Kuoppala, A., K.A. Lindstedt, J. Saarinen, P.T. Kovanen and J.O. Kokkonen, 2000. Inactivation of bradykinin by angiotensin-converting enzyme and by carboxypeptidase N in human plasma. Am. J. Physiol. Heart. Circ. Physiol., 278: H1069-H1074.
    PubMedDirect Link

  21. Latini, R., A.P. Maggioni, M. Flather, P. Sleight and G. Tognoni, 1995. ACE inhibitor use in patients with myocardial infarction. Summary of evidence from clinical trials. Circulation, 92: 3132-3137.
    PubMedDirect Link

  22. Liao, C., A. Xie, L. Shi, J. Zhou and X. Lu, 2004. Eigenvalue analysis of peroxisome proliferator-activated receptor gamma agonists. J. Chem. Inf. Comput. Sci., 44: 230-238.
    CrossRefPubMedDirect Link

  23. Lipinski, C.A., F. Lombardo, B.W. Dominy and P.J. Feeney, 1997. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 23: 3-25.
    CrossRef

  24. Mayer, D., C.B. Naylor, I. Motoc and G.R. Marshall, 1987. A unique geometry of the active site of angiotensin-converting enzyme consistent with structure-activity studies. J. Comput.-Aided Mol. Des., 1: 3-16.
    CrossRef

  25. Moorthy, N.S.H.N. and P. Trivedi, 2006. QSAR modeling of some 2-methoxy Acridones: Cytotoxic agents in multi drug resistant cells. Int. J. Cancer Res., 2: 267-276.
    CrossRefDirect Link

  26. Nesto, R.W. and S. Zarich, 1998. Acute myocardial infarction in diabetes mellitus: Lessons learned from ACE inhibition. Circulation, 97: 12-15.
    Direct Link

  27. Paliwal, S., A. Narayan and S. Paliwal, 2009. Quantitative structure activity relationship analysis of dicationic diphenylisoxazole as potent anti-trypanosomal agents. QSAR Comb. Sci., 28: 1367-1375.
    CrossRef

  28. Paliwal, S.K., M. Pal and A.A. Siddiqui, 2010. Quantitative structure activity relationship analysis of angiotensin II AT1 receptor antagonists. Med. Chem. Res., 19: 475-489.
    CrossRefDirect Link

  29. Panday, S.K., M. Dikshit and D.K. Dikshit, 2009. Synthesis of N-[3'-(acetylthio)alkanoyl] and N-[3'-mercaptoalkanoyl]-4-α(s)-(phenylmethyl) pyroglutamic acids and prolines as potent ACE inhibitors. Med. Chem. Res., 18: 566-578.
    CrossRef

  30. Rogers, D. and A.J. Hopfinger, 1994. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci., 34: 854-866.
    CrossRefDirect Link

  31. Sadowski, J. and J. Gasteiger, 1993. From atoms and bonds to three-dimensional atomic coordinates: Automatic model builders. Chem. Rev., 93: 2567-2581.
    CrossRefDirect Link

  32. San Juan, A.A. and S.J. Cho, 2005. 3D-QSAR studies on angiotensin-converting enzyme (ACE) inhibitors: A molecular design in hypertensive agents. Bull. Korean Chem. Soc., 26: 952-958.
    Direct Link

  33. Shen, M., A. LeTiran, Y. Xiao, A. Golbraikh, H. Kohn and A. Tropsha, 2002. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. J. Med. Chem., 45: 2811-2823.
    CrossRefPubMedDirect Link

  34. Shreedharan, R. and D. Bockenhauer, 2005. Congenital nephrotic syndrome responsive to angiotensin-converting enzyme inhibition. Pediatr. Nephrol., 20: 1340-1342.
    CrossRefDirect Link

  35. Suh, J.T., J.W. Skiles, B.E. Williams, R.D. Youssefyeh and H. Jones et al., 1985. Angiotensin-converting enzyme inhibitors. New orally active antihypertensive (mercaptoalkanoyl)- and [(acylthio)alkanoyl]glycine derivatives. J. Med. Chem., 28: 57-66.
    CrossRefPubMedDirect Link

  36. Sutters, M., 2008. Systemic Hypertension. In: Current Medical Diagnosis and Treatment, McPhee, S.J., M.A. Papadakis and L.M. Tierney (Eds.). McGraw-Hill, New Jersey, pp: 371-399.

  37. Thakur, A., B.K. Tiwari, M. Thakur, S. Thakur, N.D. Pandey and S.S. Narvi, 2007. 2D, 3D modeling of inhibition activity of reverse transcriptase-1 by HEPT derivatives. Asian J. Biochem., 2: 84-100.
    CrossRefDirect Link

  38. Abhilash, T., T. Mamta and T. Suprajnya, 2006. QSAR study on triazine derivatives as DHFR inhibitors using electrotopological state. Asian J. Biochem., 1: 138-147.
    CrossRefDirect Link

  39. Tropsha, A., S.J. Cho and W. Zheng, 1999. New Tricks for an Old Dog: Development and Application of Novel QSAR Methods for Rational Design of Combinatorial Chemical Libraries and Database Mining. In: Rational Drug Design: Novel Methodology and Practical Applications, Parril, A.L. and M.R. Reddy (Eds.). ACS Symposium Series 719, American Chemical Society, Washington DC., pp: 198-211.

  40. Vengurlekar, S., R. Sharma and P. Trivedi, 2010. Two- and three-dimensional QSAR studies on benzyl amide-ketoacid inhibitors of HIV integrase and their reduced analogues. Med. Chem. Res., 19: 1106-1120.
    CrossRef

  41. Verloop, A., 1961. The STERIMOL Approach to Drug Design. Marcel Dekker, New York.

  42. Vinter, J.G., A. Davis and M.R. Saunders, 1987. Strategic approaches to drug design. I. An integrated software framework for molecular modelling. J. Comput. Aided Mol. Des., 1: 31-51.
    CrossRefPubMedDirect Link

  43. Wiener, H., 1947. Structural determination of paraffin boiling points. J. Am. Chem. Soc., 69: 17-20.
    CrossRefDirect Link

  44. Wright, J.W., S. Mizutani and J.W. Harding, 2008. Pathways involved in the transition from hypertension to hypertrophy to heart failure. Treatment strategies. Heart Fail. Rev., 13: 367-375.
    CrossRefPubMedDirect Link

  45. Wyvratt, M.J. and A.A. Patchett, 1985. Recent developments in the design of angiotensin-converting enzyme inhibitors. Med. Res. Rev., 5: 483-531.
    PubMedDirect Link

  46. Yokoyama, H., O. Tomonaga, M. Hirayama, A. Ishii and M. Takeda et al., 1997. Predictors of the progression of diabetic nephropathy and the beneficial effect of angiotensin-converting enzyme inhibitors in NIDDM patients. Diabetologia, 40: 405-411.
    CrossRefPubMedDirect Link

  47. Zheng, W. and A. Tropsha, 2000. A novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J. Chem. Inform. Comput. Sci., 40: 185-194.
    CrossRefDirect Link

Keywords


  • Angiotensin-converting enzyme inhibitors
  • TSAR
  • partial least square
  • multiple linear regression
  • quantitative structure-activity relationship

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