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image of Design of Novel Acat Inhibitors as Potent Anti-Hyperlipidemic Agents Using Chemometric Approaches

Abstract

Aim

A 2D QSAR study of acyl-coenzyme A (CoA): cholesterol acyltransferase (ACAT) inhibitors revealed that electronic, topological, and steric properties are important structural features required for activity against ACAT.

Background

In order to interpret the evidence encrypted by the molecular structure of the compounds, a standard physicochemical descriptors-centered, and Quantitative Structure-Activity Relationship (QSAR) approach was implemented on a data set of Indoline derivatives were reported to be acyl-coenzyme A (CoA): cholesterol acyltransferase ACAT inhibitors.

Objective

The ACAT enzyme plays an important role in the absorption of dietary cholesterol. Therefore, the inhibition of ACAT is a key strategy or primary objective for the treatment of hypercholesterolemia and atherosclerosis.

Method

Chemo metric models were designed by inserting a battery of statistical techniques in the current study that demonstrate the linear approaches of analysis, including multiple linear regression (MLR), partial least square PLS, and non-linear methods such as artificial neural networks (ANN).

Result

The activity contributions of these molecules were analyzed through regression equation, and the best QSAR model was created with an excellent correlative and predictive ability. Significant statistical values S = 0.35, F = 60.30, r = 0.92, r2 = 0.85, r2 (CV) = 0.82 of the designed models were obtained using stepwise MLR and a comparable PLS and FFNN model with r2 (CV) = 0.82, 0.88 and 0.86 respectively and the relevant descriptors like inertia moment 1 size, Kier Chiv4 (cluster) index, Kier Chiv6(ring) index offered important information regarding this model.

Conclusion

The model reveals that inertia moment 1 size, Kier Chiv4 (cluster) index, and Kier Chiv6 (ring) index are prerequisite descriptors to determine other promising ACAT antagonists with high and liable potency against the target. Therefore, these characteristics may be used efficiently for the design and evaluation of active compounds as new ACAT inhibitors thanks to their utilization.

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2024-12-02
2025-01-24
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References

  1. a Badimon J.J. Fuster V. Chesebro J.H. Badimon L. Coronary atherosclerosis. A multifactorial disease. Circulation 1993 87 3 II3 II16 8443920
    [Google Scholar]
  2. b Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: The Scandinavian Simvastatin Survival Study (4S). Lancet 1994 344 8934 1383 1389 7968073
    [Google Scholar]
  3. c Shepherd J. The west of Scotland Coronary Prevention study: A trial of cholesterol reduction in scottish men. Am. J. Cardiol. 1995 76 9 113C 117C 10.1016/S0002‑9149(99)80480‑9 7572679
    [Google Scholar]
  4. d Karam I. Yang Y.J. Li J.Y. Hyperlipidemia Background and Progress. SM J Cardiolog and Cardiovasc Disord 2017 3 2 2 8
    [Google Scholar]
  5. e Stoll F. Eidam A. Michael L. Bauer J.M. Haefeli W.E. Drug Treatment of Hypercholesterolemia in Older Adults: Focus on Newer Agents. Drugs Aging 2022 39 4 251 256 10.1007/s40266‑022‑00928‑z 35278206
    [Google Scholar]
  6. f The Lipid Research Clinics Coronary Primary Prevention Trial results. II. The relationship of reduction in incidence of coronary heart disease to cholesterol lowering. JAMA 1984 251 3 365 374 10.1001/jama.1984.03340270043026 6361300
    [Google Scholar]
  7. Brown M.S. Dana S.E. Goldstein J.L. Cholesterol ester formation in cultured human fibroblasts. Stimulation by oxygenated sterols. J. Biol. Chem. 1975 250 10 4025 4027 10.1016/S0021‑9258(19)41498‑1 1126942
    [Google Scholar]
  8. Bayly G.R. Clinical Biochemistry: Metabolic and Clinical Aspects. Clinical biochemistry: Metabolic and clinical aspects. Churchill Livingstone 2014 702 736
    [Google Scholar]
  9. a Puglielli L. Konopka G. Pack-Chung E. Acyl-coenzyme A: cholesterol acyltransferase modulates the generation of the amyloid β-peptide. Nat. Cell Biol. 2001 3 10 905 912 10.1038/ncb1001‑905 11584272
    [Google Scholar]
  10. b Chang T.Y. Chang C.C.Y. Lin S. Yu C. Li B.L. Miyazaki A. Roles of acyl-coenzyme A: cholesterol acyltransferase-1 and -2. Curr. Opin. Lipidol. 2001 12 3 289 296 10.1097/00041433‑200106000‑00008 11353332
    [Google Scholar]
  11. a Brown M.S. Goldstein J.L. Lipoprotein metabolism in the macrophage: Implications for cholesterol deposition in atherosclerosis. Annu. Rev. Biochem. 1983 52 1 223 261 10.1146/annurev.bi.52.070183.001255 6311077
    [Google Scholar]
  12. b Largis E.E. Wang C.H. DeVries V.G. Schaffer S.A. CL 277,082: A novel inhibitor of ACAT-catalyzed cholesterol esterification and cholesterol absorption. J. Lipid Res. 1989 30 5 681 690 10.1016/S0022‑2275(20)38328‑0 2760542
    [Google Scholar]
  13. c Carr T.P. Parks J.S. Rudel L.L. Hepatic ACAT activity in African green monkeys is highly correlated to plasma LDL cholesteryl ester enrichment and coronary artery atherosclerosis. Arterioscler. Thromb. 1992 12 11 1274 1283 10.1161/01.ATV.12.11.1274 1420087
    [Google Scholar]
  14. d Krause B.R. Anderson M. Bisgaier C.L. In vivo evidence that the lipid-regulating activity of the ACAT inhibitor CI-976 in rats is due to inhibition of both intestinal and liver ACAT. J. Lipid Res. 1993 34 2 279 294 10.1016/S0022‑2275(20)40755‑2 8429262
    [Google Scholar]
  15. e Burrier R.E. Deren S. McGregor D.G. Hoos L.M. Smith A.A. Davis H.R. Jr Demonstration of a direct effect on hepatic acyl CoA:cholesterol acyl transferase (ACAT) activity by an orally administered enzyme inhibitor in the hamster. Biochem. Pharmacol. 1994 47 9 1545 1551 10.1016/0006‑2952(94)90530‑4 8185666
    [Google Scholar]
  16. Prasad K. Mishra M. Mechanism of hypercholesterolemia-induced atherosclerosis. Rev. Cardiovasc. Med. 2022 23 6 212 10.31083/j.rcm2306212 39077184
    [Google Scholar]
  17. a Gillies P.J. Robinson C.S. Rathgeb K.A. Regulation of ACAT activity by a cholesterol substrate pool during the progression and regression phases of atherosclerosis: Implications for drug discovery. Atherosclerosis 1990 83 2-3 177 185 10.1016/0021‑9150(90)90163‑D 2242095
    [Google Scholar]
  18. b Sliskovic D.R. White A.D. Therapeutic potential of ACAT inhibitors as lipid lowering and anti-atherosclerotic agents. Trends Pharmacol. Sci. 1991 12 5 194 199 10.1016/0165‑6147(91)90546‑5 1862535
    [Google Scholar]
  19. Setia N. Verma I.C. Khan B. Arora A. Premature coronary artery disease and familial hypercholesterolemia: Need for early diagnosis and cascade screening in the Indian population. Cardiol. Res. Pract. 2012 2012 1 4 10.1155/2012/658526 22111029
    [Google Scholar]
  20. Kubinyi H. QSAR and 3D QSAR in drug design Part 1: Methodology. Drug Discov. Today 1997 2 11 457 467 10.1016/S1359‑6446(97)01079‑9
    [Google Scholar]
  21. Patankar S.J. Jurs P.C. Prediction of IC50 values for ACAT inhibitors from molecular structure. J. Chem. Inf. Comput. Sci. 2000 40 3 706 723 10.1021/ci990125r 10850775
    [Google Scholar]
  22. Li X. Zou Y. Zhao Q. Synthesis, biological evaluation, and molecular docking studies of xanthone sulfonamides as ACAT inhibitors. Chem. Biol. Drug Des. 2015 85 3 394 403 10.1111/cbdd.12419 25146964
    [Google Scholar]
  23. Shoji Y. Takahashi K. Ohta M. Novel indoline-based acyl-CoA: Cholesterol acyltransferase inhibitor: Effects of introducing a methanesulfonamide group on physicochemical properties and biological activities. Bioorg. Med. Chem. 2009 17 16 6020 6031 10.1016/j.bmc.2009.06.047 19608421
    [Google Scholar]
  24. Sharma P. Paliwal S. Sharma S. Chauhan N. Jain S. Quantitative structure activity relationship studies of potent Endothelin-A receptor antagonist for the treatment of pulmonary arterial hypertension. Indian J. Chem. 2024 63 190 202
    [Google Scholar]
  25. Dalby A. Nourse J.G. Hounshell W.D. Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J. Chem. Inf. Comput. Sci. 1992 32 3 244 255 10.1021/ci00007a012
    [Google Scholar]
  26. Sadowski J. Gasteiger J. From atoms and bonds to three-dimensional atomic coordinates: Automatic model builders. Chem. Rev. 1993 93 7 2567 2581 10.1021/cr00023a012
    [Google Scholar]
  27. Kovatcheva A. Buchbauer G. Golbraikh A. Wolschann P. QSAR modeling of α-campholenic derivatives with sandalwood odor. J. Chem. Inf. Comput. Sci. 2003 43 1 259 266 10.1021/ci020296n 12546561
    [Google Scholar]
  28. Kumar Pali S. Pandey A. Paliwal S. Quantitative Structure Activity Relationship Analysis of N-(mercaptoalkanoyl)- and [(acylthio)alkanoyl] Glycine Derivatives as ACE Inhibitors. Am J Drug Discov Develop 2011 1 2 85 104 10.3923/ajdd.2011.85.104
    [Google Scholar]
  29. Paliwal S. Narayan A. Paliwal S. Quantitative Structure Activity Relationship Analysis of Dicationic Diphenylisoxazole as Potent Anti‐Trypanosomal Agents. QSAR Comb. Sci. 2009 28 11-12 1367 1375 10.1002/qsar.200860206
    [Google Scholar]
  30. Luco J.M. Ferretti F.H. QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. J. Chem. Inf. Comput. Sci. 1997 37 2 392 401 10.1021/ci960487o 9090857
    [Google Scholar]
  31. Mittal A Sharma M Singh A. QSAR Modelling of PDE5 Inhibitory Activity of Tetracyclic Guanine Derivatives as Antihypertensive Agents. Int J Drug Dev Res 2016 8 3 043 051
    [Google Scholar]
  32. Kesar S. Paliwal S. Sharma S. In-Silico QSAR Modelling of Predicted Rho Kinase Inhibitors Against Cardio Vascular Diseases. Curr. Computeraided Drug Des. 2019 15 5 421 432 10.2174/1573409915666190307163437 30848208
    [Google Scholar]
  33. Rinaldi D. Rivail J.L. Molecular polarizability and dielectric effect of medium in the liquid phase. Theoretical study of the water molecule and its dimers. Theor. Chim. Acta 1973 32 1 57 70 10.1007/BF01209416
    [Google Scholar]
  34. Khan S. Najmi A.Y. Kesar S. Madan K. Arya R. Development of QSAR Model using MLR, PLS and NN Approach to Elucidate the Physicochemical Properties Responsible for Antihyperlipidemic Potential of ACAT inhibitors. YMER 2023 22 3 981 999
    [Google Scholar]
  35. Jain S. Chauhan N. Bhardwaj A. Yadaw G. Singh M.K. Mishra A. QSAR Modeling of α-Ketooxazole Motif Analogues as Potent Anti-Alzheimer Agents. YMER Digital 2022 21 5 624 640 10.37896/YMER21.05/71
    [Google Scholar]
  36. Kursula P. Sikkilن H, Fukao T, Kondo N, Wierenga RK. High resolution crystal structures of human cytosolic thiolase (CT): A comparison of the active sites of human CT, bacterial thiolase, and bacterial KAS I. J. Mol. Biol. 2005 347 1 189 201 10.1016/j.jmb.2005.01.018 15733928
    [Google Scholar]
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  • Article Type:
    Research Article
Keywords: FFNN ; ACAT ; PLS ; MLR ; hypercholesterolemia ; QSAR
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