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2000
Volume 21, Issue 16
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Introduction

The High-density lipoprotein (HDL) receptor, Scavenger receptor class B, type I (SRBI) plays a crucial role in lipoprotein metabolism, cholesterol homeostasis, and atherosclerosis. In the present study, a quantitative structure-activity relationship study (QSAR) investigation was conducted on a data set of 31 novel indolinyl thiazole-based inhibitors of SR-BI mediated lipid uptake.

Methods

To build the QSAR model, Multiple linear regression analysis (MLR), partial least square analysis (PLS), and neural analysis (NN) were performed which were further evaluated internally as well as externally for the prediction of activity. The best QSAR model for MLR was selected with a correlation coefficient (r2) of 0.937, cross-validation r2cv of 0.908, and a standard error (S) value of 0.253. For PLS, r2 was 0.937 and for FFNN r2 was 0.961 (for the training set). This was further evaluated externally by a test set having r2 values 0.870 (MLR), 0.863(PLS), and 0.933(neural network) analysis.

Results

The final model comprised hydrophobic parameters (Lipole Z component) and steric parameters (molar refractivity and K alpha2 index).

Conclusion

All these descriptors generated comparable results which prove that the model generated is sound and has good predictability.

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References

  1. ShahA.M. BanerjeeT. MukherjeeD. Coronary, peripheral and cerebrovascular disease: A complex relationship.J. Indian Med. Assoc.2010108529229421121404
    [Google Scholar]
  2. RossR. Atherosclerosis--an inflammatory disease.N. Engl. J. Med.1999340211512610.1056/NEJM1999011434002079887164
    [Google Scholar]
  3. CleemanJ.I. LenfantC. The national cholesterol education program.J. Am. Diet. Assoc.198888111401140810.1016/S0002‑8223(21)08024‑X3183260
    [Google Scholar]
  4. Gillotte-TaylorK. BoullierA. WitztumJ.L. SteinbergD. QuehenbergerO. Scavenger receptor class B type I as a receptor for oxi-dized low density lipoprotein.J. Lipid Res.20014291474148210.1016/S0022‑2275(20)30281‑911518768
    [Google Scholar]
  5. ActonS.L. KozarskyK.F. RigottiA. The HDL receptor SR-BI: A new therapeutic target for atherosclerosis?Mol. Med. Today199951251852410.1016/S1357‑4310(99)01600‑710562717
    [Google Scholar]
  6. StanglH. StroblW.M. Role of SR-BI in HDL Metabolism.The HDL Handbook2017171185
    [Google Scholar]
  7. NielandT.J.F. ShawJ.T. JaipuriF.A. MaligaZ. DuffnerJ.L. KoehlerA.N. KriegerM. Influence of HDL-cholesterol-elevating drugs on the in vitro activity of the HDL receptor SR-BI.J. Lipid Res.20074881832184510.1194/jlr.M700209‑JLR20017533223
    [Google Scholar]
  8. RosensonR.S. BrewerH.B.Jr DavidsonW.S. FayadZ.A. FusterV. GoldsteinJ. HellersteinM. JiangX.C. PhillipsM.C. RaderD.J. RemaleyA.T. RothblatG.H. TallA.R. Yvan-CharvetL. Cholesterol efflux and atheroprotection: Advancing the concept of reverse cholesterol transport.Circulation2012125151905191910.1161/CIRCULATIONAHA.111.06658922508840
    [Google Scholar]
  9. DockendorffC. FaloonP.W. YuM. YoungsayeW. PenmanM. NielandT.J.F. NagP.P. LewisT.A. PuJ. BennionM. NegriJ. PatersonC. LamG. DandapaniS. PerezJ.R. MunozB. PalmerM.A. SchreiberS.L. KriegerM. Indolinyl-thiazole based inhibitors of scavenger receptor-BI (SR-BI)-mediated lipid transport.ACS Med. Chem. Lett.20156437538010.1021/ml500154q26478787
    [Google Scholar]
  10. KubinyiH. Evolutionary variable selection in regression and PLS analyses.J. Chemometr.199610211913310.1002/(SICI)1099‑128X(199603)10:2<119::AID‑CEM409>3.0.CO;2‑4
    [Google Scholar]
  11. SadowskiJ. GasteigerJ. From atoms and bonds to three-dimensional atomic coordinates: automatic model builders.Chem. Rev.1993932567258110.1021/cr00023a012
    [Google Scholar]
  12. TodeschiniV. Handbook of molecular descriptors.WeinheimWiley-VCH200010.1002/9783527613106
    [Google Scholar]
  13. KarelsonM. Molecular Descriptors in QSAR/QSPR.New YorkJohn Wiley & Sons2000
    [Google Scholar]
  14. M.M. C. Multivariate QSAR.J. Braz. Chem. Soc.200213742753
    [Google Scholar]
  15. KowalskiB.R. BenderC.F. Pattern recognition. II. Linear and nonlinear methods for displaying chemical data.J. Am. Chem. Soc.197395368669310.1021/ja00784a007
    [Google Scholar]
  16. WorldS. JohanssonE. CocchiM. PLS-partial least squares projections to latent str. In: 3D-QSAR in Drug Design, Theory, Methods, and Applications. KubinyiH. LeidenESCOM Science Publishers1993523550
    [Google Scholar]
  17. TetkoI.V. LuikA.I. PodaG.I. Applications of neural networks in structure-activity relationships of a small number of molecules.J. Med. Chem.199336781181410.1021/jm00059a0038464034
    [Google Scholar]
  18. McFarlandJ.W. GansD.J. Cluster significance analysis contrasted with three other quantitative structure-activity relationship methods.J. Med. Chem.1987301464910.1021/jm00384a0083806603
    [Google Scholar]
  19. WorldS. ErikssonL. Statistical validation of QSAR results.Chemometric methods in molecular design.New YorkVCH1995309318
    [Google Scholar]
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  • Article Type:
    Research Article
Keyword(s): drug design; FFNN; HDL; MLR; PLS; QSAR; SR-BI; TSAR
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