Advanced search
Publication Cover
Expert Opinion on Drug Discovery Volume 11, 2016 - Issue 2
Submit an article Journal homepage
337
Views
21
CrossRef citations to date
0
Altmetric
Reviews

QSAR studies in the discovery of novel type-II diabetic therapies

Areej AbuhammadDepartment of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman11942, Jordan
&
Mutasem O. TahaDepartment of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman11942, JordanCorrespondence[email protected]
Pages 197-214 | Published online: 01 Dec 2015

Bibliography

  • Papers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers.
  • WHO. WHO factsheets. 2015. Available from: http://wwwwhoint/mediacentre/factsheets/fs312/en/.
  • Guariguata L, Whiting D, Hambleton I, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–149.
  • de Boer IH. Kidney disease and related findings in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care. 2014;37(1):24–30.
  • Aiello LP. Diabetic retinopathy and other ocular findings in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care. 2014;37(1):17–23.
  • Huang ES, Laiteerapong N, Liu JY, et al. Rates of complications and mortality in older patients with diabetes mellitus: the diabetes and aging study. JAMA Intern Med. 2014;174(2):251–258.
  • Dart AB, Martens PJ, Rigatto C, et al. Earlier onset of complications in youth with type 2 diabetes. Diabetes Care. 2014;37(2):436–443.
  • Ahrén B. Creative use of novel glucose-lowering drugs for type 2 diabetes: where will we head in the next 50 years? Diabetologia. 2015;58(8):1–5.
  • Dutta S. Application of qsar in drug design and drug discovery. Drugs. 2015;9:10.
  • Vaidya A, Jain S, Jain S, et al. Quantitative structure-activity relationships: a novel approach of drug design and discovery. J Pharm Sci Pharmacol. 2014;1(3):219–232.
  • Boussageon R, Bejan-Angoulvant T, Saadatian-Elahi M, et al. Effect of intensive glucose lowering treatment on all cause mortality, cardiovascular death, and microvascular events in type 2 diabetes: meta-analysis of randomised controlled trials. BMJ. 2011;343:d4169.
  • Plutzky J, Viberti G, Haffner S. Atherosclerosis in type 2 diabetes mellitus and insulin resistance: mechanistic links and therapeutic targets. J Diabetes Complications. 2002;16(6):401–415.
  • Krentz AJ, Bailey CJ. Oral antidiabetic agents: current role in type 2 diabetes mellitus. Drugs. 2005;65(3):385–411.
  • Stein SA, Lamos EM, Davis SN. A review of the efficacy and safety of oral antidiabetic drugs. Expert Opin Drug Saf. 2013;12(2):153–175.

• This review discusses important validation methods for quantitative structure–activity relationship models.

  • Mittermayer F, Caveney E, Oliveira CD, et al. Addressing unmet medical needs in type 2 diabetes: a narrative review of drugs under development. Curr Diabetes Rev. 2015;11(1):17–31.
  • Taygerly JP, McGee LR, Rubenstein SM, et al. Discovery of INT131: a selective PPARγ modulator that enhances insulin sensitivity. Bioorg Med Chem. 2013;21(4):979–992.
  • Roy K, Mitra I. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Throughput Screen. 2011;14(6):450–474.
  • Taha MO, Habash M, Al-Hadidi Z, et al. Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies. J Chem Inf Model. 2011;51(3):647–669.
  • Al-Sha’er MA, Taha MO. Application of docking-based comparative intermolecular contacts analysis to validate Hsp90α docking studies and subsequent in silico screening for inhibitors. J Mol Model. 2012;18(11):4843–4863.
  • Jaradat N, Khanfar M, Habash M, et al. Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors. J Comput Aided Mol Des. 2015;29(6):561–581.
  • Taha M, Habash M, Khanfar M. The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators. J Comput Aided Mol Des. 2014;28(5):509–547.

•• This review provides an overview of different feature selection techniques applied in quantitative structure–activity relationship modelling.

  • Goodarzi M, Dejaegher B, Heyden YV. Feature selection methods in QSAR studies. J AOAC Int. 2012;95(3):636–651.
  • Pratim Roy P, Paul S, Mitra I, et al. On two novel parameters for validation of predictive QSAR models. Molecules. 2009;14(5):1660.
  • Roy K, Chakraborty P, Mitra I, et al. Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data. J Comput Chem. 2013;34(12):1071–1082.

•• The following three articles clarify good practices in quantitative structure–activity relationship modelling.

• The following four articles deal with quantitative structure–activity relationship modelability of compound datasets and activity cliffs.

  • Koutsoukas A, Paricharak S, Galloway WRJD, et al. How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. J Chem Inf Model. 2014;54(1):230–242.
  • Golbraikh A, Muratov E, Fourches D, et al. Data set modelability by QSAR. J Chem Inf Model. 2014;54(1):1–4.
  • Guha R, Van Drie JH. Structure−activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model. 2008;48(3):646–658.
  • Seebeck B, Wagener M, Rarey M. From activity cliffs to target-specific scoring models and pharmacophore hypotheses. Chem Med Chem. 2011;6(9):1630–1639.
  • Stumpfe D, Hu Y, Dimova D, et al. Recent progress in understanding activity cliffs and their utility in medicinal chemistry: miniperspective. J Med Chem. 2013;57(1):18–28.
  • Hu Y, Stumpfe D, Bajorath J. Advancing the activity cliff concept. F1000Research. 2013;2:199.

•• The following three articles discuss pitfalls and errors in quantitative structure–activity relationship modelling.

  • Dearden J, Cronin M, Kaiser K. How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR QSAR Environ Res. 2009;20(3–4):241–266.
  • Cramer RD. The inevitable QSAR renaissance. J Comput Aided Mol Des. 2012;26(1):35–38.
  • Cherkasov A, Muratov EN, Fourches D, et al. QSAR modeling: where have you been? Where are you going to? J Med Chem. 2014;57(12):4977–5010.
  • Kulkarni SS, Gediya LK, Kulkarni VM. Three-dimensional quantitative structure activity relationships (3-D-QSAR) of antihyperglycemic agents. Bioorg Med Chem. 1999;7(7):1475–1485.
  • Kurogi Y. Three-dimensional quantitative structure-activity relationships (3D-QSAR) of antidiabetic thiazolidinediones. Drug Des Discov. 1999;16(2):109–118.
  • So S-S, Karplus M. A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors. J Comput Aided Mol Des. 1999;13(3):243–258.
  • Bharatam P, Patel D, Adane L, et al. Modeling and informatics in designing anti-diabetic agents. Curr Pharm Des. 2007;13(34):3518–3530.
  • Luan F, Xu X, Liu HT, et al. QSAR studies of PTP1B inhibitors: recent advances and perspectives. Curr Med Chem. 2012;19(25):4208–4217.
  • Alexiou P, Pegklidou K, Chatzopoulou M, et al. Aldose reductase enzyme and its implication to major health problems of the 21st century. Curr Med Chem. 2009;16(6):734–752.
  • Khanna S, Bahal R, Bharatam PV. In silico studies on PPARγ agonistic heterocyclic systems. In: Gupta SP, editor. QSAR and molecular modeling studies in heterocyclic drugs I. Berlin: Springer; 2006. p. 149–180.
  • Hayes JM, Leonidas DD. Computation as a tool for glycogen phosphorylase inhibitor design. Mini Rev Med Chem. 2010;10(12):1156–1174.
  • Fang J, Huang D, Zhao W, et al. A new protocol for predicting novel GSK-3beta ATP competitive inhibitors. J Chem Inf Model. 2011;51(6):1431–1438.
  • Crisan L, Pacureanu L, Avram S, et al. PLS and shape-based similarity analysis of maleimides–GSK-3 inhibitors. J Enzyme Inhib Med Chem. 2014;29(4):599–610.
  • Akhtar M, Bharatam PV. 3D-QSAR and molecular docking studies on 3-anilino-4-arylmaleimide derivatives as glycogen synthase kinase-3beta inhibitors. Chem Biol Drug Des. 2012;79(4):560–571.
  • Fu G, Liu S, Nan XF, et al. Quantitative structure-activity relationship analysis and a combined ligand-based/structure-based virtual screening study for glycogen synthase kinase-3. Mol Inform. 2014;33(9):627–640.
  • Prasanna S, Daga PR, Xie A, et al. Glycogen synthase kinase-3 inhibition by 3-anilino-4-phenylmaleimides: insights from 3D-QSAR and docking. J Comput Aided Mol Des. 2009;23(2):113–127.
  • Lather V, Kristam R, Saini JS, et al. QSAR models for prediction of glycogen synthase kinase-3β inhibitory activity of indirubin derivatives. QSAR Comb Sci. 2008;27(6):718–728.
  • Patel DS, Bharatam PV. Selectivity criterion for pyrazolo[3,4-b]pyrid[az]ine derivatives as GSK-3 inhibitors: CoMFA and molecular docking studies. Eur J Med Chem. 2008;43(5):949–957.
  • Taha MO, Bustanji Y, Al-Ghussein MAS, et al. Pharmacophore modeling, quantitative structure–activity relationship analysis, and in silico screening reveal potent glycogen synthase kinase-3β inhibitory activities for cimetidine, hydroxychloroquine, and gemifloxacin. J Med Chem. 2008;51(7):2062–2077.
  • Dessalew N, Patel DS, Bharatam PV. 3D-QSAR and molecular docking studies on pyrazolopyrimidine derivatives as glycogen synthase kinase-3beta inhibitors. J Mol Graph Model. 2007;25(6):885–895.
  • Malla P, Kumar R, Kumar M. Validation of formylchromane derivatives as protein tyrosine phosphatase 1B inhibitors by pharmacophore modeling, atom-based 3D-QSAR and docking studies. Chem Biol Drug Des. 2013;82(1):71–80.
  • Malla P, Kumar R, Mattewal SK, et al. A paradigm for development of novel PTP 1B inhibitors: pharmacophore modelling, atom-based 3D-QSAR and docking studies. Med Chem Res. 2014;23(2):927–938.
  • Thareja S, Kokil GR, Aggarwal S, et al. Sulphonamides as inhibitors of protein tyrosine phosphatase 1B: a three-dimensional quantitative structure-activity relationship study using self-organizing molecular field analysis approach. Chem Pharm Bull. 2010;58(4):526–532.
  • Thareja S, Aggarwal S, Bhardwaj T, et al. Self-organizing molecular field analysis of 2, 4-thiazolidinediones: a 3D-QSAR model for the development of human PTP1B inhibitors. Eur J Med Chem. 2010;45(6):2537–2546.
  • Cheng Y, Zhou M, Tung CH, et al. Studies on two types of PTP1B inhibitors for the treatment of type 2 diabetes: hologram QSAR for OBA and BBB analogues. Bioorg Med Chem Lett. 2010;20(11):3329–3337.
  • Ma Y, Jin YY, Wang YL, et al. The discovery of a novel and selective inhibitor of PTP1B over TCPTP: 3D QSAR pharmacophore modeling, virtual screening, synthesis, and biological evaluation. Chem Biol Drug Des. 2014;83(6):697–709.
  • Sachan N, Kadam SS, Kulkarni VM. Human protein tyrosine phosphatase 1B inhibitors: QSAR by genetic function approximation. J Enzyme Inhib Med Chem. 2007;22(3):267–276, 371–373.
  • Sobhia ME, Bharatam PV. Comparative molecular similarity indices analysis (CoMSIA) studies of 1,2-naphthoquinone derivatives as PTP1B inhibitors. Bioorg Med Chem. 2005;13(6):2331–2338.
  • Nair PC, Sobhia ME. CoMFA based de novo design of pyridazine analogs as PTP1B inhibitors. J Mol Graph Model. 2007;26(1):117–123.
  • Taha MO, Bustanji Y, Al-Bakri AG, et al. Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening. J Mol Graph Model. 2007;25(6):870–884.
  • Xu J, Yuan H, Ran T, et al. A selectivity study of sodium-dependent glucose cotransporter 2/sodium-dependent glucose cotransporter 1 inhibitors by molecular modeling. J Mol Recognit. 2015;28(8);467–479.
  • Zhi H, Zheng J, Chang Y, et al. QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA. J Mol Struct. 2015;1098:199–205.
  • Vyas VK, Bhatt HG, Patel PK, et al. CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents. SAR QSAR Environ Res. 2013;24(7):519–551.
  • Nakka S, Guruprasad L. Structural insights into the active site of human sodium dependent glucose co-transporter 2: homology modelling, molecular docking, and 3D-QSAR studies. Aust J Chem. 2012;65(9):1314–1324.
  • Suryanarayanan V, Sudha A, Rajamanikandan S, et al. Atom-based 3D QSAR studies on novel N-β-d-xylosylindole derivatives as SGLT2 inhibitors. Med Chem Res. 2013;22(2):615–624.
  • Tang C, Zhu X, Huang D, et al. A specific pharmacophore model of sodium-dependent glucose co-transporter 2 (SGLT2) inhibitors. J Mol Model. 2012;18(6):2795–2804.
  • Prasoona RK, Jyoti A, Mukesh Y, et al. Optimization of Gaussian Kernel Function in Support Vector Machine aided QSAR studies of C-aryl glucoside SGLT2 inhibitors. Interdiscip Sci. 2013;5(1):45–52.
  • Guasch L, Sala E, Valls C, et al. Development of docking-based 3D-QSAR models for PPARgamma full agonists. J Mol Graph Model. 2012;36:1–9.
  • Khanna S, Sobhia ME, Bharatam PV. Additivity of molecular fields: coMFA study on dual activators of PPARalpha and PPARgamma. J Med Chem. 2005;48(8):3015–3025.
  • Sundriyal S, Bharatam PV. ‘Sum of activities’ as dependent parameter: a new CoMFA-based approach for the design of pan PPAR agonists. Eur J Med Chem. 2009;44(1):42–53.
  • Giaginis C, Theocharis S, Tsantili-Kakoulidou A. Quantitative structure-activity relationships for PPAR-γ binding and gene transactivation of tyrosine-based agonists using multivariate statistics. Chem Biol Drug Des. 2008;72(4):257–264.
  • Liao C, Xie A, Zhou J, et al. 3D QSAR studies on peroxisome proliferator-activated receptor γ agonists using CoMFA and CoMSIA. J Mol Model. 2004;10(3):165–177.
  • Gee VM, Wong FS, Ramachandran L, et al. Identification of novel peroxisome proliferator-activated receptor-gamma (PPARgamma) agonists using molecular modeling method. J Comput Aided Mol Des. 2014;28(11):1143–1151.
  • Maltarollo VG, Homem-de-Mello P, Honorio KM. Role of physicochemical properties in the activation of peroxisome proliferator-activated receptor delta. J Mol Model. 2011;17(10):2549–2558.
  • Garcia TS, Silva DC, Gertrudes JC, et al. Molecular features related to the binding mode of PPARdelta agonists from QSAR and docking analyses. SAR QSAR Environ Res. 2013;24(2):157–173.
  • Al-Najjar BO, Wahab HA, Tengku Muhammad TS, et al. Discovery of new nanomolar peroxisome proliferator-activated receptor gamma activators via elaborate ligand-based modeling. Eur J Med Chem. 2011;46(6):2513–2529.
  • Patil S, Sharma R, Abhishek A. Comparative study to predict dipeptidyl peptidase IV inhibitory activity of β-amino amide scaffold. Indian J Pharm Sci. 2015;77(2):142–150.
  • Jain SV, Ghate M. Atom-based pharmacophore modeling, CoMFA/CoMSIA-based 3D-QSAR studies and lead optimization of DPP-4 inhibitors for the treatment of type 2 diabetes. Med Chem Res. 2014;23(7):3436–3450.
  • Murugesan V, Sethi N, Prabhakar YS, et al. CoMFA and CoMSIA of diverse pyrrolidine analogues as dipeptidyl peptidase IV inhibitors: active site requirements. Mol Divers. 2011;15(2):457–466.
  • Paliwal S, Seth D, Yadav D, et al. Development of a robust QSAR model to predict the affinity of pyrrolidine analogs for dipeptidyl peptidase IV (DPP- IV). J Enzyme Inhib Med Chem. 2011;26(1):129–140.
  • Jiang YK. Molecular docking and 3D-QSAR studies on beta-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors. J Mol Model. 2010;16(7):1239–1249.
  • Saqib U, Siddiqi MI. 3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents. SAR QSAR Environ Res. 2009;20(5–6):519–535.
  • Al-masri IM, Mohammad MK, Taha MO. Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening. Chem Med Chem. 2008;3(11):1763–1779.
  • Pissurlenkar RR, Shaikh MS, Coutinho EC. 3D-QSAR studies of Dipeptidyl peptidase IV inhibitors using a docking based alignment. J Mol Model. 2007;13(10):1047–1071.
  • Zeng J, Liu G, Tang Y, et al. 3D-QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA. J Mol Model. 2007;13(9):993–1000.
  • Gokhale KM. Role of glycogen synthase kinase (GSK-3) in type-2 diabetes and GSK-3 inhibitors as potential anti-diabetics. Int J Pharm Phytopharmacol Res. 2013;3(3):196–199.
  • Yarchoan M, Arnold SE. Repurposing diabetes drugs for brain insulin resistance in Alzheimer disease. Diabetes. 2014;63(7):2253–2261.
  • Arfeen M, Bharatam PV. Design of glycogen synthase kinase-3 inhibitors: an overview on recent advancements. Curr Pharm Des. 2013;19(26):4755–4775.
  • Takahashi-Yanaga F. Activator or inhibitor? GSK-3 as a new drug target. Biochem Pharmacol. 2013;86(2):191–199.
  • Avrahami L, Licht-Murava A, Eisenstein M, et al. GSK-3 inhibition: achieving moderate efficacy with high selectivity. Biochim Biophys Acta. 2013;1834(7):1410–1414.
  • Davis MI, Hunt JP, Herrgard S, et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol. 2011;29(11):1046–1051.
  • He R-J, Yu Z-H, Zhang R-Y, et al. Protein tyrosine phosphatases as potential therapeutic targets. Acta Pharmacol Sin. 2014;35:1227–1246.
  • Gurzov EN, Stanley WJ, Brodnicki TC, et al. Protein tyrosine phosphatases: molecular switches in metabolism and diabetes. Trends Endocrinol Metab. 2015;26(1):30–39.
  • Tamrakar AK, Maurya CK, Rai AK. PTP1B inhibitors for type 2 diabetes treatment: a patent review (2011–2014). Expert Opin Ther Pat. 2014;24(10):1101–1115.
  • Potenza M, Rayfield EJ. Targeting the incretin system in type 2 diabetes mellitus. Mt Sinai J Med. 2009;76(3):244–256.
  • Karagiannis T, Paschos P, Paletas K, et al. Dipeptidyl peptidase-4 inhibitors for treatment of type 2 diabetes mellitus in the clinical setting: systematic review and meta-analysis. BMJ. 2012;344:e1369.
  • Agrawal R, Jain P, Narayan Dikshit S, et al. Atom-based 3D-QSAR and docking studies of various 3-aminomethyl-1, 2-dihydro-4-phenyl-1-isoquinolones derivatives as an effective DPP-IV inhibitors and its validation. Immunol Endocr Metabol Agents Med Chem. 2013;13(3):221–231.
  • Gallo LA, Wright EM, Vallon V. Probing SGLT2 as a therapeutic target for diabetes: basic physiology and consequences. Diabetes Vasc Dis Res. 2015;12(2):78–89.
  • Ghosh RK, Ghosh SM, Chawla S, et al. SGLT2 inhibitors: a new emerging therapeutic class in the treatment of type 2 diabetes mellitus. J Clin Pharmacol. 2012;52(4):457–463.
  • Ansquer JC, Foucher C. The PPAR system in diabetes. In: Jenkins AJ, Toth PP, Lyons TJ, editors. Lipoproteins in diabetes mellitus. New York (NY): Springer; 2014. p. 357–372.
  • Liu Z-M, Hu M, Chan P, et al. Early investigational drugs targeting PPAR-α for the treatment of metabolic disease. Expert Opin Investig Drugs. 2015;24(5):611–621.
  • Ahmadian M, Suh JM, Hah N, et al. PPAR [gamma] signaling and metabolism: the good, the bad and the future. Nat Med. 2013;99(5):557–566.
  • Maiese K, Chong ZZ, Shang YC, et al. Novel directions for diabetes mellitus drug discovery. Expert Opin Drug Discov. 2013;8(1):35–48.
  • Wright MB, Bortolini M, Tadayyon M, et al. Minireview: challenges and opportunities in development of PPAR agonists. Mol Endocrinol. 2014;28(11):1756–1768.
  • Shah P, Mittal A, Bharatam PV. CoMFA analysis of dual/multiple PPAR activators. Eur J Med Chem. 2008;43(12):2784–2791.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.