QSAR rationales for the dipeptidyl peptidase-4 (DPP-4) inhibitors: The imidazolopyrimidine amides

Raghuraj Parihar; Brij Kishore Sharma

doi:10.30574/gscbps.2020.11.3.0169

Authors

Raghuraj Parihar Department of Chemistry, Government College, Bundi-323 001 (Rajasthan), India.
Brij Kishore Sharma Department of Chemistry, Government College, Bundi-323 001 (Rajasthan), India.

DOI:

https://doi.org/10.30574/gscbps.2020.11.3.0169

Keywords:

Quantitative structure-activity relationship (QSAR), DPP-4 inhibitors, Combinatorial protocol in multiple linear regression (CP-MLR) analysis, Chemometric descriptors, Imidazolopyrimidine amides.

Abstract

The DPP4 inhibition activity of imidazolopyrimidine amides has been quantitatively analyzed in terms of chemometric descriptors. The statistically validated QSAR models provided rationales to explain the inhibition activity of these congeners. The descriptors identified through CP-MLR analysis have highlighted the role of mean electrotopological state (Ms), number of double bonds in molecular structure (nDB), 2D Petitijean shape index (PJI2), Moran autocorrelation of lag-2/weighted by atomic polarizabilities (MATS2p), Moran autocorrelation of lag-6 and lowest eigenvalue n.5 of Burden matrix /weighted by atomic Sanderson electronegativities (MATS6e and BELe5), lowest eigenvalue n.3 and highest eigenvalue n.1 of Burden matrix/weighted by atomic van der Waals volumes (BELv3 and BEHv1). In addition to these 2^nd order mean Galvez topological charge index (JGI2), number of ring tertiary C(sp3) (nCrHR) and R--CR--X type structural fragments (C-028) have also shown prevalence to model the inhibitory activity.

From statistically validated models, positive contribution of descriptors Ms, PJI2, JGI2, MATS2p, BELe5, BELv3 and BEHv1 suggested that higher values of these are conducive in improving the DPP4 inhibition activity. On the other hand, negative contribution of descriptors nDB, C-028, nCrHR and MATS6e advocated that absence of number of double bonds (nDB), R--CR--X type structural fragment (C-028), number of ring tertiary C(sp3) (nCrHR) and lower value of descriptor MATS6e would be advantageous. PLS analysis has confirmed the dominance of the CP‐MLR identified descriptors and applicability domain analysis revealed the acceptable predictability of suggested models. All the compounds are within the applicability domain of the proposed models and were evaluated correctly.

Metrics

Metrics Loading ...

References

Wideman RD and Kieffer TJ. (2009). Mining incretin hormone pathways for novel therapies. Trends in Endocrinology and Metabolism, 20, 280-286.

Triplitt C, Wright A and Chiquette E. (2006). Incretin mimetics and dipeptidyl peptidase-IV inhibitors: potential new therapies for type 2 diabetes mellitus. Pharmacotherapy, 26, 360-374.

Gallwitz B. (2005). Glucagon-like peptide-1-based therapies for the treatment of type 2 diabetes mellitus. Treatments in Endocrinology, 4, 361-370.

Holst JJ. (1994). Glucagon-like peptide-1 (GLP-1) a newly discovered GI hormone. Gastroenterology, 107, 1048-1055.

Drucker DJ. (1998). Glucagon-like peptides. Diabetes, 47, 159-169.

Vilsboll T and Holst JJ. (2004). Incretins, insulin secretion and type 2 diabetes mellitus. Diabetologia, 47, 357-366.

Elahi D, McAloon-Dyke M, Fukagawa NK, Meneilly GS, Sclater AL, Minaker KL, Habener JF and Andersen DK. (1994). The insulinotropic actions of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (7-37) in normal and diabetic subjects. Regulatory Peptides, 51, 63-75.

Nauck MA, Bartels E, Oerskov C, Ebert R and Creutzfedlt WJ. (1993). Additive insulinotropic effects of exogenous synthetic human gastric inhibitory polypeptide and glucagon-like peptide-1-(7-36) amide infused at near-physiological insulinotropic hormone and glucose concentrations. Clinical Endocrinology and Metabolism, 76, 912-917.

Gentilella R, Bianchi C, Rossi A and Rotella CM. (2009). Exenatide: a review from pharmacology to clinical practice. Diabetes, Obesity and Metabolism, 11,544-556.

Gonzalez C, Beruto V, Keller G, Santoro S and Di Girolamo G. (2006). Investigational treatments for type 2 diabetes mellitus: Exenatide and Liraglutide. Expert Opinion on Investigational Drugs, 15, 887-895.

Holst JJ. (2002). Therapy of type 2 diabetes mellitus based on the actions of glucagon-like peptide-1. Diabetes/ Metabolism Research and Reviews, 18, 430-441.

Hansen L, Deacon CF, Orskov C and Holst JJ. (1999). Glucagon-likepeptide-1-(7-36) amide is transformed to glucagon-like peptide-1-(9-36)amide by dipeptidyl peptidase IV in the capillaries supplying the L cells of the porcine intestine. Endocrinology, 140, 5356-5363.

Nagakura T, Yasuda N, Yamazaki K, Ikuta H, Yoshikawa S, Asano O and Tanaka I. (2001). Improved glucose tolerance via enhanced glucose-dependent insulin secretion in dipeptidyl peptidase IV-deficient Fischer rats. Biochemical and Biophysical Research Communications, 284, 501-506.

Marguet D, Baggio L, Kobayashi T, Bernard A, Pierres M, Nielsen PF, Ribel U, Watanabe T, Drucker DJ and Wagtmann N. (2000). Enhanced insulin secretion and improved glucose tolerance in mice lacking CD26. Proceedings of the National Academy of Sciences of the United States of America, 97, 6874-6879.

Durinx C, Lambeir A, Bosmans E, Falmagne J, Berghmans R, Haemers A, Scharpe S and De Meester I. (2000). Molecular characterization of dipeptidyl peptidase activity in serum. Soluble CD26/dipeptidyl peptidase IV is responsible for the release of X-Pro dipeptides. European Journal of Biochemistry, 267, 5608-5613.

Iwaki-Egawa S, Watanabe Y, Kikuya Y and Fujimoto Y. (1998). Dipeptidyl peptidase IV from human serum: purification, characterization, and N-terminal amino acid sequence. Journal of Biochemistry, 124, 428-433.

Villhauer EB, Brinkman JA, Naderi GB, Burkey BF, Dunning BE, Prasad K, Mangold BL, Russell ME and Hughes TE. (2003). 1-[[(3-Hydroxy-1-adamantyl)amino]acetyl]-2-cyano-(S)-pyrrolidine: A potent, selective, and orally bio available dipeptidyl peptidase IV inhibitor with antihyperglycemic properties. Journal of Medicinal Chemistry, 46, 2774-2789.

Kim D, Wang L, Beconi M, Eiermann GJ, Fisher MH, He H, Hickey GJ, Kowalchick JE, Leiting B, Lyons K, Marsilio F, McCann ME, Patel RA, Petrov A, Scapin G, Patel SB, Roy RS, Wu JK, Wyvratt MJ, Zhang BB, Zhu L, Thornberry NA and Weber AE. (2005). (2R)-4-Oxo-4-[3-(trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-]pyrazin-7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine: a potent, orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. Journal of Medicinal Chemistry, 48, 141-151.

Augeri DJ, Robl JA, Betebenner DA, Magnin DR, Khanna A, Robertson JG, Wang A, Simpkins LM, Taunk P, Huang Q, Han SP, Abboa-Offei B, Cap M, Xin L, Tao L, TozzoE, Welzel GE, Egan DM, Marcinkeviciene J, Chang SY, Biller SA, Kirby MS, Parker RA and Hamann LG. (2005). Discovery and preclinical profile of saxagliptin (BMS-477118): a highly potent, long-acting, orally active dipeptidyl peptidase IV inhibitor for the treatment of Type 2 diabetes. Journal of Medicinal Chemistry, 48, 5025-5037.

Feng J, Zhang Z, Wallace MB, Stafford JA, Kaldor SW, Kassel DB, Navre M, Shi L, Skene RJ, Asakawa T, Takeuchi K, Xu R, Webb DR and Gwaltney SL. (2007). Discovery of alogliptin: a potent, selective, bio available, and efficacious inhibitor of dipeptidyl peptidase IV. Journal of Medicinal Chemistry, 50, 2297-2300.

Havale SH and Pal M. (2009). Medicinal chemistry approaches to the inhibition of dipeptidyl peptidase-4 for the treatment of type 2 diabetes. Bioorganic and Medicinal Chemistry, 17, 1783-1802.

Brigance RP, Meng W, Fura A, Harrity T, Wang A, Zahler R, Kirby MS and Hamman LG. (2010). Synthesis and SAR of azolopyrimidines as potent and selective dipeptidyl peptidase-4 (DPP4) inhibitors for type 2 diabetes. Bioorganic and Medicinal Chemistry Letters, 20, 4395-4398.

Meng W, Brigance RP, Chao HJ, Fura A, Harrity T, Marcinkkeviciene J, O’Connor SP, Tamura JK, Xie D, Zhang Y, Klei HE, Kish K, Weigett CA, Turdi H, Wang A, Zahler R, Kirby MS and Hamann LG. (2010). Discovery of 6-(Aminomethyl)-5-(2,4-dichlorophenyl)-7-methylimidazo[1,2-]pyrimidine-2-carboxamides as potent, selective dipeptidyl peptidase-4 (DPP4) inhibitors. Journal of Medicinal Chemistry,53, 5620-5628.

SYSTAT, Version 7.0; SPSS Inc 444 North Michigan Avenue, Chicago IL, 60611.

ChemDraw Ultra 6.0 and Chem3D Ultra, Cambridge Soft Corporation, Cambridge, USA.

Todeschini R and Consonni V. Dragon software (version 1.11-2001), Milano, Italy.

Prabhakar YS. (2003). A combinatorial approach to the variable selection in multiple linear regression: analysis of Selwood et al. Data Set-a case study. QSAR and Combinatorial Science, 22, 583-595.

Wold S. (1978). Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20, 397-405.

Kettaneh N, Berglund A and Wold S. (2005). PCA and PLS with very large data sets. Computational Statistics and Data Analysis, 48, 69-85.

Stahle L and Wold S. (1998). Multivariate data analysis and experimental design, in Biomedical Research, Progress in Medicinal Chemistry, Ellis GP and West WB, eds., Elsevier Science Publishers, BV, The Netherlands, 25, 291–338.

Sharma S, Prabhakar YS, Singh P and Sharma BK. (2008). QSAR study about ATP-sensitive potassium channel activation of cromakalim analogues using CP-MLR approach. European Journal of Medicinal Chemistry, 43, 2354-2360.

Sharma S, Sharma BK, Sharma SK, Singh P and Prabhakar YS. (2009). Topological descriptors in modeling the agonistic activity of human A3 adenosine receptor ligands: The derivatives of 2-Chloro-N6-substituted-4'-thioadenosine-5'-uronamide. European Journal of Medicinal Chemistry, 44, 1377-1382.

Sharma BK, Pilania P, Singh P and Prabhakar YS. (2010). Combinatorial protocol in multiple linear regression/partial least-squares directed rationale for the caspase-3 inhibition activity of isoquinoline-1,3,4-trione derivatives. SAR and QSAR in Environmental Research, 21, 169-185.

Sharma BK, Singh P, Sarbhai K and Prabhakar YS. (2010). A quantitative structure-activity relationship study on serotonin 5-HT6 receptor ligands: Indolyl and piperidinyl sulphonamides. SAR and QSAR in Environmental Research, 21, 369-388.

Sharma BK, Pilania P, Sarbhai K, Singh P and Prabhakar YS. (2010). Chemometric descriptors in modeling the carbonic anhydrase inhibition activity of sulfonamide and sulfamate derivatives. Molecular Diversity, 14, 371-384.

Sharma BK, Singh P, Shekhawat M, Sarbhai K and Prabhakar YS. (2011). Modelling of serotonin reuptake inhibitory and histamine H3 antagonistic activity of piperazine and diazepane amides: QSAR rationales for co-optimization of the activity profiles. SAR and QSAR in Environmental Research, 22, 365-383.

So S-S and Karplus M. (1997). Three-dimensional quantitative structure activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations. Journal of Medicinal Chemistry, 40, 4347-4359.

Prabhakar YS, Solomon VR, Rawal RK, Gupta MK and Katti SB. (2004). CP-MLR/PLS directed structure–activity modeling of the HIV-1RT inhibitory activity of 2,3-diaryl-1,3-thiazolidin-4-ones. QSAR and Combinatorial Science, 23, 234-244.

Gramatica P. (2007). Principles of QSAR models validation: internal and external. QSAR and Combinatorial Science, 26, 694-701.

Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM and Gramatica P. (2003). Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs. Environmental and Health Perspectives, 111, 1361-1375.

Golbraikh A and Tropsha A. (2002). Beware of q2! Journal of Molecular Graphics and Modeling, 20, 269-276.