Review of synthesis, biological assay, and QSAR studies of HMGR inhibitors

Current Topics in Medicinal Chemistry

Volumn 12, Issue 8, 2012, Pages 895-919

  García, Isela ^a   Fall, Yagamare ^a   Gómez, Generosa ^a  

^a UNIVERSITY OF VIGO   (Spain)

Author keywords

3 hydroxy 3 methyl glutaryl coenzyme a reductase; Antiparasite drug; Atherosclerotic disease; Chagas' disease; Cholesterol level; COMFA; Complex network; CoMSIA; Lipid lowering agent; QSAR; Topological indices; Trypanosoma cruzi

Indexed keywords

6 ARYLOXY 3,5 DIHYDROXYHEXANOIC ACID; 9,9 BIS(4 FLUOROPHENYL) 3,5 DIHYDROXY 6,8 NONADIENOIC ACID; DECARESTRICTIN DERIVATIVE; HYDROXYMETHYLGLUTARYL COENZYME A REDUCTASE; HYDROXYMETHYLGLUTARYL COENZYME A REDUCTASE INHIBITOR; IMIDAZOLE DERIVATIVE; N PYRROLYL HEPTENOATE DERIVATIVE; PITAVASTATIN; PYRIDINE DERIVATIVE; PYRIMIDINE DERIVATIVE; UNCLASSIFIED DRUG;

BIOASSAY; CHOLESTEROL SYNTHESIS; COMPARATIVE MOLECULAR FIELD ANALYSIS; COMPARATIVE MOLECULAR SIMILARITY INDICES ANALYSIS; DRUG STRUCTURE; DRUG SYNTHESIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW;

ANIMALS; HUMANS; HYDROXYMETHYLGLUTARYL COA REDUCTASES; HYDROXYMETHYLGLUTARYL-COA REDUCTASE INHIBITORS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84860293939     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/156802612800166729     Document Type: Review

References (140)

1. Shimokata, K.; Yamada, Y.; Kondo, T.; Ichihara, S.; Izawa, H.; Nagata, K.; Murohara, T.; Ohno, M.; Yokota, M. Association of gene polymorphisms with coronary artery disease in individuals with or without nonfamilial hypercholesterolemia. Atherosclerosis, 2004, 172, 167-173.
2. Smilde, T.J.; van Wissen, S.; Wollersheim, H.; Kastelein, J.J.; Stalenhoef, A.F. Genetic and metabolic factors predicting risk of cardiovascular disease in familial hypercholesterolemia. Neth. J. Med., 2001, 59, 184-195.
3. Leitersdorf, E.; Eisenberg, S.; Eliav, O.; Friedlander, Y.; Berkman, N.; Dann, E.J.; Landsberger, D.; Sehayek, E.; Meiner, V.; Wurm, M. Genetic determinants of responsiveness to the HMG-CoA reductase inhibitor fluvastatin in patients with molecularly defined heterozygous familial hypercholesterolemia. Circulation, 1993, 87, III35-44.
4. Salazar, L.A.; Hirata, M.H.; Quintao, E.C.; Hirata, R.D. Lipidlowering response of the HMG-CoA reductase inhibitor fluvastatin is influenced by polymorphisms in the low-density lipoprotein receptor gene in Brazilian patients with primary hypercholesterolemia. J. Clin. Lab. Anal., 2000, 14, 125-131.
5. Suzuki, M.; Iwasaki, H.; Fujikawa, Y.; Sakashita, M.; Kitahara, M.; Sakoda, R. Synthesis and Biological Evaluations of Quinolinebased HMG-CoA Reductase Inhibitors. Bioorg. Med. Chem., 2001, 9, 2727.
6. Calabuig, C.; Anton-Fos, G.M.; Galvez, J.; Garcia-Domenech, R. New hypoglycaemic agents selected by molecular topology. Int. J. Pharm., 2004, 278, 111-118.
7. González-Díaz, H.; Olazábal, E.; Santana, L.; Uriarte, E.; Castañedo, N. QSAR study of anticoccidial activity for diverse chemical compounds: prediction and experimental assay of trans-2-(2- Nitrovinyl)furan. Bioorg. Med. Chem., 2007, 15, 962-968.
8. Gonzalez-Diaz, H.; Prado-Prado, F.J.; Santana, L.; Uriarte, E. Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species. Bioorg. Med. Chem., 2006, 14, 5973-5980.
9. González, M.P.; González-Díaz, H.; Molina Ruiz, R.; Cabrera, M.A.; Ramos de Armas, R. TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides. J. Chem. Inf. Comput. Sci., 2003, 43, 1192-1199.
10. Helguera, A.M.; Rodriguez-Borges, J.E.; Garcia-Mera, X.; Fernandez, F.; Cordeiro, M.N. Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set. J. Med. Chem., 2007, 50, 1537-1545.
11. Marrero-Ponce, Y.; Castillo-Garit, J.A.; Olazabal, E.; Serrano, H.S.; Morales, A.; Castanedo, N.; Ibarra-Velarde, F.; Huesca- Guillen, A.; Sánchez, A.M.; Torrens, F.; Castro, E.A. Atom, atomtype and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic. Bioorg Med Chem, 2005, 13, 1005-1020.
12. Mathur, K.C.; Gupta, S.; Khadikar, P.V. Topological modelling of analgesia. Bioorg. Med. Chem., 2003, 11, 1915-1928.
13. Murcia-Soler, M.; Perez-Gimenez, F.; Garcia-March, F.J.; Salabert- Salvador, M.T.; Diaz-Villanueva, W.; Medina-Casamayor, P. Discrimination and selection of new potential antibacterial compounds using simple topological descriptors. J. Mol. Graph. Model., 2003, 21, 375-390.
14. Prado-Prado, F.J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte, E. Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance. Bioorg. Med. Chem., 2007, 15, 897-902.
15. Votano, J.R.; Parham, M.; Hall, L.H.; Kier, L.B.; Oloff, S.; Tropsha, A.; Xie, Q.; Tong, W. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis, 2004, 19, 365-377.
16. Urbina, J.A.; Lazardi, K.; Marchan, E.; Visbal, G.; Aguirre, T.; Piras, M.M. Mevinolin (lovastatin) potentiates the antiproliferative effects of ketoconazole and terbinafine against Trypanosoma (Schizotrypanum) cruzi: in vitro and in vivo studies. Antimicrob. Agents Chemother., 1993, 37, 580-591.
17. Santana, L.; Uriarte, E.; González-Díaz, H.; Zagotto, G.; Soto- Otero, R.; Mendez-Alvarez, E. A. QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. J. Med. Chem., 2006, 49, 1149-1156.
18. Marrero-Ponce, Y. Linear indices of the "molecular pseudograph's atom adjacency matrix": definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors. J. Chem. Inf. Comput. Sci., 2004, 44, 2010-2026.
19. Vilar, S.; Santana, L.; Uriarte, E. Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action. J. Med. Chem., 2006, 49, 1118-1124.
20. Casanola-Martin, G.M.; Marrero-Ponce, Y.; Khan, M.T.; Ather, A.; Khan, K.M.; Torrens, F.; Rotondo, R. Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays. Eur. J. Med. Chem., 2007, 42, 1370-1381.
21. Casanola-Martin, G.M.; Marrero-Ponce, Y.; Khan, M.T.; Ather, A.; Sultan, S.; Torrens, F.; Rotondo, R. TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices. Bioorg. Med. Chem., 2007, 15, 1483-1503.
22. Marrero-Ponce, Y.; Khan, M.T.; Casanola Martin, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F.; Rotondo, R. Prediction of tyrosinase inhibition activity using atom-based bilinear indices. ChemMedChem, 2007, 2, 449-478.
23. Freund, J.A.; Poschel, T. In Lect. Notes Phys; Springer-Verlag: Berlin, Germany, 2000.
24. Nunez, M.B.; Maguna, F.P.; Okulik, N.B.; Castro, E.A. QSAR modeling of the MAO inhibitory activity of xanthones derivatives. Bioorg. Med. Chem. Lett., 2004, 14, 5611-5617.
25. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors., 2000.
26. González-Díaz, H.; González-Díaz, Y.; Santana, L.; Ubeira, F.M.; Uriarte, E. Proteomics, networks and connectivity indices. Proteomics, 2008, 8, 750-778.
27. Chen, J.; Shen, B. Computational Analysis of Amino Acid Mutation: a Proteome Wide Perspective. Curr. Proteomics, 2009, 6, 228-234.
28. Chou, K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics, 2009, 6, 262-274.
29. Giuliani, A.; Di Paola, L.; Setola, R. Proteins as Networks: A mesoscopic approach using haemoglobin molecule as case study. Curr. Proteomics, 2009, 6, 235-245.
30. González-Díaz, H.; Prado-Prado, F.; Pérez-Montoto, L.G.; Duardo- Sánchez, A.; López-Díaz, A. QSAR models for proteins of parasitic organisms, plants and human guests: theory, applications, legal protection, taxes, and regulatory issues. Curr. Proteomics, 2009, 6, 214-227.
31. Pérez-Montoto, L.G.; Prado-Prado, F.; Ubeira, F.M.; González- Díaz, H. Study of parasitic infections, cancer, and other diseases with mass-spectrometry and quantitative proteome-disease relationships. Curr. Proteomics, 2009, 6(4), 246-261.
32. Torrens, F.; Castellano, G. Topological charge-transfer indices: from small molecules to proteins. Curr. Proteomics, 2009, 6(4), 204-213.
33. Vázquez, J.M.; Aguiar, V.; Seoane, J.A.; Freire, A.; Serantes, J.A.; Dorado, J.; Pazos Sierra, A.; Munteanu, C.R. Star graphs of protein sequences and proteome mass spectra in cancer prediction. Curr. Proteomics, 2009, 6, 275-288.
34. Gonzalez-Diaz, H. Quantitative studies on structure-activity and structure-property relationships (QSAR/QSPR). Curr. Top. Med. Chem., 2008, 8, 1554.
35. Caballero, J.; Fernandez, M. Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr. Top. Med. Chem., 2008, 8, 1580-1605.
36. Duardo-Sanchez, A.; Patlewicz, G.; Lopez-Diaz, A. Current topics on software use in medicinal chemistry: intellectual property, taxes, and regulatory issues. Curr. Top. Med. Chem., 2008, 8, 1666-1675.
37. Gonzalez-Diaz, H.; Prado-Prado, F.; Ubeira, F.M. Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr. Top. Med. Chem., 2008, 8, 1676-1690.
38. Gonzalez, M.P.; Teran, C.; Saiz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: an overview. Curr. Top. Med. Chem., 2008, 8, 1606-1627.
39. Helguera, A.M.; Combes, R.D.; Gonzalez, M.P.; Cordeiro, M.N. Applications of 2D descriptors in drug design: a DRAGON tale. Curr. Top. Med. Chem., 2008, 8, 1628-1655.
40. Ivanciuc, O. Weka machine learning for predicting the phospholipidosis inducing potential. Curr. Top. Med. Chem., 2008, 8, 1691-1709.
41. Vilar, S.; Cozza, G.; Moro, S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr. Top. Med. Chem., 2008, 8, 1555-1572.
42. Wang, J.F.; Wei, D.Q.; Chou, K.C. Drug candidates from traditional chinese medicines. Curr. Top. Med. Chem., 2008, 8, 1656-1665.
43. Wang, J.F.; Wei, D.Q.; Chou, K.C. Pharmacogenomics and personalized use of drugs. Curr. Top. Med. Chem., 2008, 8, 1573-1579.
44. Gonzalez-Diaz, H. Network topological indices, drug metabolism, and distribution. Curr. Drug Metab., 11, 283-284.
45. Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab., 11, 369-378.
46. Garcia, I.; Diop, Y.F.; Gomez, G. QSAR & complex network study of the HMGR inhibitors structural diversity. Curr. Drug Metab., 2010, 11, 307-314.
47. Gonzalez-Diaz, H.; Duardo-Sanchez, A.; Ubeira, F.M.; Prado- Prado, F.; Perez-Montoto, L.G.; Concu, R.; Podda, G.; Shen, B. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr. Drug Metab., 11, 379-406.
48. Khan, M.T. Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr. Drug Metab., 11, 285-295.
49. Martinez-Romero, M.; Vazquez-Naya, J.M.; Rabunal, J.R.; Pita-Fernandez, S.; Macenlle, R.; Castro-Alvarino, J.; López-Roses, L.; Ulla, J.L.; Martínez-Calvo, A.V.; Vázquez, S.; Pereira, J.; Porto- Pazos, A.B.; Dorado, J.; Pazos, A.; Munteanu, C.R. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. Curr. Drug Metab., 11, 347-368.
50. Mrabet, Y.; Semmar, N. Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts. Curr. Drug Metab., 11, 315-341.
51. Wang, J.F.; Chou, K.C. Molecular modeling of cytochrome P450 and drug metabolism. Curr. Drug Metab., 11, 342-346.
52. Zhong, W.Z.; Zhan, J.; Kang, P.; Yamazaki, S. Gender specific drug metabolism of PF-02341066 in rats--role of sulfoconjugation. Curr. Drug Metab., 11, 296-306.
53. Marrero-Ponce, Y.; Casañola-Martín, G.M.; Khan, M.T.H.; Torrens, F. A. R.; Abad, C. Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds. Curr. Pharm. Des., 2010, 16(24),2601-2624.
54. Roy, K.; Ghosh, G. Exploring QSARs with extended topochemical atom (ETA) indices for modeling chemical and drug Toxicity. Curr. Pharm. Des., 2010, 16(24),2625-2639.
55. Munteanu, C.R.; Fernández-Blanco, E.; Seoane, J.A.; Izquierdo- Novo, P.; Rodríguez-Fernández, J.A.; Prieto-González, J.M.; Rabuñal, J.R.; Pazos, A. Drug discovery and design for complex diseases through QSAR computational methods. Curr. Pharm. Des., 2010, 16(24),2640-2655.
56. Vázquez-Naya, J.M.; Martínez-Romerol, M.; Porto-Pazos, A.B.; Novoa, F.; Valladares-Ayerbes, M.; Pereira, J.; Munteanu, C.R.; Dorado, J. Ontologies of drug discovery and design for neurology, cardiology and oncology. Curr. Pharm. Des., 2010, 16(24),2724-2736.
57. Speck-Planche, A.; Scotti, M.T.; de Paulo-Emerenciano, V.; Current pharmaceutical design of antituberculosis drugs: Future perspectives. Curr. Pharm. Des., 2010, 16 (24), 2656-2665.
58. García, I.; Fall, Y.; Gómez, G. QSAR, Docking, and CoMFA studies of GSK3 inhibitors. Curr. Pharm. Des., 2010, 16 (24),2666-2675.
59. Estrada, E.; Molina, E.; Nodarse, D.; Uriarte, E. Structural contributions of substrates to their binding to P-gycoprotein. A TOPSMODE approach. Curr. Pharm. Des., 2010, 16 (24),2676-2709.
60. Concu, R.; Podda, G.; Ubeira, F.M.; González-Díaz, H. Review of QSAR models for enzyme classes of drug targets: Theoretical background and applications in parasites, hosts, and other organisms. Curr. Pharm. Des., 2010, 16 (24), 2710-2723.
61. González-Díaz, H.; Romaris, F.; Duardo-Sanchez, A.; Pérez- Montoto, L.G.; Prado-Prado, F.; Patlewicz, G.; Ubeira, F.M. Predicting drugs and proteins in parasite infections with topological indices of complex networks: Theoretical backgrounds, aplications, and legal issues. Curr. Pharm. Des., 2010, 16(24), 2737-2764.
62. Castillo-Garit, J.A.; Marrero-Ponce, Y.; Torrens, F. Atom-based 3D-chiral quadratic indices. Part 2: Prediction of the corticosteroidbinding globulin binding affinity of the 31 benchmark steroids data set. Bioorg. Med. Chem., 2006, 14, 2398-2408.
63. Castillo-Garit, J.A.; Marrero-Ponce, Y.; Torrens, F.; Rotondo, R. Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification. J. Mol. Graph. Model., 2007, 26, 32-47.
64. de Julián-Ortiz, J.V.; de Gregorio Alapont, C.; Ríos-Santamarina, I.; García-Doménech, R.; Gálvez, J. Prediction of properties of chiral compounds by molecular topology. J. Mol. Graph. Model., 1998, 16, 14-18.
65. Fabian, W.M.; Stampfer, W.; Mazur, M.; Uray, G. Modeling the chromatographic enantioseparation of aryl- and hetarylcarbinols on ULMO, a brush-type chiral stationary phase, by 3D-QSAR techniques. Chirality, 2003, 15, 271-275.
66. Golbraikh, A.; Bonchev, D.; Tropsha, A. Novel chirality descriptors derived from molecular topology. J. Chem. Inf. Comput. Sci., 2001, 41, 147-158.
67. Golbraikh, A.; Tropsha, A. QSAR modeling using chirality descriptors derived from molecular topology. J. Chem. Inf. Comput. Sci., 2003, 43, 144-154.
68. González-Díaz, H.; Sanchez, I.H.; Uriarte, E.; Santana, L. Symmetry considerations in Markovian chemicals 'in silico' design (MARCH-INSIDE) I: central chirality codification, classification of ACE inhibitors and prediction of sigma-receptor antagonist activities. Comput. Biol. Chem., 2003, 27, 217-227.
69. Marrero-Ponce, Y.; Castillo-Garit, J.A. 3D-chiral atom, atom-type, and total non-stochastic and stochastic molecular linear indices and their applications to central chirality codification. J. Comput. Aided. Mol. Des., 2005, 19, 369-383.
70. Zhang, Z.; Grigorov, M.G. Similarity networks of protein binding sites. Proteins, 2006, 62, 470-478.
71. Bonchev, D, Buck GA From molecular to biological structure and back. J. Chem. Inf. Model., 2007, 47, 909-917.
72. Ivanciuc, O. QSAR comparative study of Wiener descriptors for weighted molecular graphs. J. Chem. Inf. Comput. Sci., 2000, 40, 1412-1422.
73. Ivanciuc, O.; Ivanciuc, T.; Klein, D.J. Quantitative structureproperty relationships generated with optimizable even/odd Wiener polynomial descriptors. SAR QSAR Environ Res., 2001, 12, 1-16.
74. Ivanciuc, O.; Ivanciuc, T.; Klein, D.J.; Seitz, W.A.; Balaban, A.T. Wiener index extension by counting even/odd graph distances. J. Chem. Inf. Comput. Sci., 2001, 41, 536-549.
75. Gupta, N.; Mangal, N.; Biswas, S. Evolution and similarity evaluation of protein structures in contact map space. Proteins, 2005, 59, 196-204.
76. Gan, H.H.; Pasquali, S.; Schlick, T. Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design. Nucleic Acids Res., 2003, 31, 2926-2943.
77. González-Díaz, H.; Agüero-Chapin, G.; Varona, J.; Molina, R.; Delogu, G.; Santana, L.; Uriarte, E.; Podda, G. 2D-RNA-coupling numbers: A new computational chemistry approach to link secondary structuretopology with biological function. J. Comput. Chem., 2007, 28, 1049-1056.
78. Marrero-Ponce, Y.; Nodarse, D.; González-Díaz, H.; Ramos de Armas, R.; Romero-Zaldivar, V.; Torrens, F.; Castro, A. E. Nucleic acid quadratic Indices of the "Macromolecular Graph's Nucleotides Adjacency Matrix". modeling of footprints after the interaction of paromomycin with the HIV-1 -RNA packaging region. Int. J. Mol. Sci., 2004, 5, 276-293.
79. Estrada, E.; Rodriguez-Velazquez, J.A. Subgraph centrality in complex networks. Phys. Rev. E, 2005, 71, 056103.
80. Rose, A.; Schraegle, S.J.; Stahlberg, E.A. Meier I Coiled-coil protein composition of 22 proteomes--differences and common themes in subcellular infrastructure and traffic control. BMC Evol. Biol., 2005, 5, 66.
81. Yu, X.; Lin, J.; Masuda, T.; Esumi, N.; Zack, D.J.; Qian, J. Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae. Nucleic Acids Res., 2006, 34, 917-927.
82. Guido, N.J.; Wang, X.; Adalsteinsson, D.; McMillen, D.; Hasty, J.; Cantor, C.R.; Elston, T.C.; Collins, J.J. A bottom-up approach to gene regulation. Nature, 2006, 439, 856-860.
83. Williams, R.J.; Berlow, E.L.; Dunne, J.A.; Barabasi, A.L.; Martinez, N.D. Two degrees of separation in complex food webs. Proc. Natl. Acad. Sci. USA, 2002, 99, 12913-12916.
84. González-Díaz, H.; de Armas, R.R.; Molina, R. Markovian negentropies in bioinformatics. 1. A picture of footprints after the interaction of the HIV-1 Psi-RNA packaging region with drugs. Bioinformatics, 2003, 19, 2079-2087.
85. Marrero-Ponce, Y.; Castillo Garit, J.A.; Nodarse, D. Linear indices of the 'macromolecular graph's nucleotides adjacency matrix' as a promising approach for bioinformatics studies. Part 1: prediction of paromomycin's affinity constant with HIV-1 psi-RNA packaging region. Bioorg. Med. Chem., 2005, 13, 3397-3404.
86. Vullo, A.; Frasconi, P. Prediction of protein coarse contact maps. J. Bioinform. Comput. Biol., 2003, 1, 411-431.
87. Lange, B.M.; Ghassemian, M. Comprehensive post-genomic data analysis approaches integrating biochemical pathway maps. Phytochemistry, 2005, 66, 413-451.
88. Chou, K.C.; Cai, Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome. Res., 2006, 5, 316-322.
89. Yu, X.; Lin, J.; Zack, D.J.; Qian, J. Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues. Nucleic Acids Res., 2006, 34, 4925-4936.
90. Margolin, A.A.; Nemenman, I.; Basso, K.; Wiggins, C.; Stolovitzky, G.; Dalla Favera, R. et al ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 2006, 7 (Suppl 1), S7.
91. Estrada, E. Protein bipartivity and essentiality in the yeast proteinprotein interaction network. J. Proteome Res., 2006, 5, 2177-2184.
92. Newman, M.E. Finding community structure in networks using the eigenvectors of matrices. Phys Rev. E. Stat. Nonlin. Soft. Matter Phys., 2006, 74, 036104.
93. Kleinberg, J.M. Navigation in a small world. Nature, 2000, 406, 845.
94. González-Díaz, H.; Prado-Prado, F. Unified QSAR and Networkbased computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals. J. Comput. Chem., 2008, 29, 656-657.
95. Ghosh, A.K. Asymmetric synthesis of anti-aldol segments via a nonaldol route: synthetic applications to statines and (-)- tetrahydrolipstatin. J. Organ. Chem., 2009, 74, 4508-4518.
96. Farran, D.; Toupet, L.; Martinez, J.; Dewynter, G. Stereocontrolled synthesis of 2,4-diamino-3-hydroxyacids starting from Diketopiperazines: A new route for the preparation of statine analogues. Organ. Lett., 2007, 9, 4833-4836
97. Travins, J.M.; Bursavich, M.G.; Veber, D.F.; Rich, D.H. Aspartic protease inhibitors: expedient synthesis of 2-substituted statines. Organ. Lett., 2001, 3, 2725-2728.
98. Scholz, D.; Billich, A.; Charpiot, B.; Ettmayer, P.; Lehr, P.; Rosenwirth, B.; Schreiner, E.; Gstach, H. Inhibitors of HIV-1 proteinase containing 2-heterosubstituted 4-amino-3-hydroxy-5- phenylpentanoic acid: synthesis, enzyme inhibition, and antiviral activity. J. Med. Chem., 1994, 37, 3079-3089.
99. Colle, S.; Taillefumier, C.; Chapleur, Y.; Liebl, R.; Schmidt, A. Synthesis and biological testings as inhibitors of HMGCoA reductase of the seco-acid of tuckolide and its C-7 epimer. Bioorgan. Med. Chem., 1999, 1049-1057.
100. García, I.; Gómez, G.; Teijeira, M.; Terán, C.; Fall, Y. The furan approach to oxacycles. Part 4: A synthesis of (+)-decarestrictine L. Tetrahedron Lett., 2006, 47, 1333-1335.
101. García, I.; Pérez, M.; Besada, P.; Gómez, G.; Fall, Y. Synthetic studies toward zoapatanol. Tetrahedron Lett., 2007, 49, 1344-1347.
102. García, I.; Pérez, M.; Gándara, Z.; Gómez, G.; Fall Y The furan approach to azacyclic compounds. Tetrahedron Lett., 2008, 49, 3609-3612.
103. Prabhakar, Y.S. Analysis of tetrahedral carbon in QSAR studies. J. Chem. Inf. Comput. Sci., 1999, 39, 650-653.
104. Mutsukado, M.; Suzuki, M. A new HMG-CoA reductase inhibitor, NK-104: QSAR studies. Kagaku Toronkai, Kozo Kassei Sokan Shinpojiumu Koen Yoshishu, 2000, 23-28, 192-193.
105. Saxena, M.; Soni, L.K.; Gupta, A.K.; Wakode, S.R.; Saxena, A.K.; Kaskhedikar, S.G. Development of pharmacophoric model of condensed pyridine and pyrimidine analogs as hydroxymethyl glutaryl coenzyme A reductase inhibitors. Indian J. Biochem. Biophisics (IJBB), 2006, 43, 32-36.
106. Web. CSot.
107. Motoc, I.; Sit, S.Y.; Harte, W.E.; Balasubramanian, N.; Wright, J.J. 3-Hydroxy-3-methylglutaryl-coenzyme a reductase: molecular modeling, three-dimensional structure-activity relationships, inhibitor design. QSAR Combinatorial Sci., 2006, 10, 30-35.
108. Garcia, I.; Munteanu, C.R.; Fall, Y.; Gomez, G.; Uriarte, E.; Gonzalez-Diaz, H. QSAR and complex network study of the chiral HMGR inhibitor structural diversity. Bioorg. Med. Chem., 2009, 17, 165-175.
109. Istvan, E.S.; Deisenhofer, J. Structural Mechanism for Statin Inhibition of HMG-CoA Reductase. Science, 2001, 292, 1160-1164.
110. Zhang, Q.Y.; Wan, J.; Xu, X.; Yang, G.F.; Ren, Y.L.; Liu, J.J.; Wang, H.; Guo, Y. Structure-based rational quest for potential novel inhibitors of human HMG-CoA reductase by combining CoMFA 3D QSAR modeling and virtual screening. J. Comb. Chem., 2007, 9, 131-138.
111. Thilagavathi, R.; Kumar, R.; Aparna, V.; Sobhia, M.E.; Gopalakrishnan, B.; Chakraborti, A.K. Three-dimensional quantitative structure (3D QSAR) activity relationship studies on imidazolyl and N-pyrrolyl heptenoates as 3-hydroxy-3-methylglutaryl CoA reductase (HMGR) inhibitors by comparative molecular similarity indices analysis (CoMSIA). Bioorg. Med. Chem. Lett., 2005, 15, 1027-1032.
112. González-Díaz, H.; Munteanu, C.R. Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks; Kerala, India: Transworld Research Network, 2010.
113. Kuroda, M.; Endo, A. Inhibition of in vitro cholesterol synthesis by fatty acids. Biochim. Biophys. Acta, 1977, 486, 70-81.
114. Hill, T.; Lewicki, P. STATISTICS Methods and Applications. A Comprehensive Reference for Science, Industry and Data Mining; Tulsa: StatSoft, 2006.
115. Lilien, R.H.; Farid, H.; Donald, B.R. Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum. J. Comput. Biol., 2003, 10, 925-946.
116. James, A.; Hanley, B.J.M. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982, 143, 29-36.
117. Gómez, G.; Rivera, H.; García, I.; Estévez, L.; Fall, Y. The furan approach to carbocyclic systems. Synthesis of cyclohexane derivatives from butenolides through an intramolecular Michael addition Tetrahedron Lett., 2005, 46, 5819-5822.
118. Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M.; Hwang, D.U. Complex networks: Structure and dynamics. Physics Reports, 2006, 424, 175-308.
119. Bianconi, G.; Barabasi, A.L. Bose-Einstein condensation in complex networks. Phys. Rev. Lett., 2001, 86, 5632-5635.
120. Bornholdt, S.; Schuster, H.G. Handbook of Graphs and Complex Networks: From the Genome to the Internet; Wheinheim: WILEYVCH GmbH & CO. KGa., 2003.
121. Thilagavathi, R.; Kumar, R.; Aparna, V.; Sobhia, M.E.; Gopalakrishnan, B.; Chakraborti, A.K. Three-dimensional quantitative structure (3-D QSAR) activity relationship studies on imidazolyl and N-pyrrolyl heptenoates as 3-hydroxy-3-methylglutaryl- CoA reductase (HMGR) inhibitors by comparative molecular similarity indices analysis (CoMSIA). Bioorg. Med. Chem. Lett., 2005, 15, 1027-1032.
122. Alali, F.; Zeng, L.; Zhang, Y.; Ye, Q.; Hopp, D.C.; Schwedler, J.T.; McLaughlin, J.L. 4-Deoxyannomontacin and (2,4-cis and trans)- annomontacinone, new bioactive mono-tetrahydrofuran annonaceous acetogenins from Goniothalamus giganteus. Bioorg. Med. Chem., 1997, 5, 549-555.
123. Beck, G.; Kesseler, K.; Baader, E.; Bartmann, W.; Bergmann, A.; Granzer, E.; Jendralla, H.; von Kerekjarto, B.; Krause, R.; Paulus, E. Synthesis and biological activity of new HMG-CoA reductase inhibitors. 1. Lactones of pyridine- and pyrimidine-substituted 3,5- Dihydroxy-6-hptenoic(-heptanoic) Acids. J. Med. Chem., 1990, 33, 52-60.
124. Chan, C.; Bailey, E.J.; Hartley, C.D.; Hayman, D.F.; Hutson, J.L.; Inglis, G.G.A.; Jones, P.S.; Keeling, S.E.; Kirk, B.E.; Lamont, R.B. Inhibitors of cholesterol biosynthesis. 1. 3,5-Dihydroxy-7-(Nimidazolyl)-6-heptenoates and -heptanoates, a novel series of HMG-CoA reductase inhibitors. J. Med. Chem., 1993, 36, 3646-3657.
125. Colle, S.; Taillefumier, C.; Chapleur, Y.; Liebl, R.; Schmidt, A. Synthesis and biological testings as inhibitors of HMGCoA reductase of the seco-acid of tuckolide and its C-7 epimer. Bioorg. Med. Chem., 1999, 7, 1049-1057.
126. Gohrt, A.; Zeeck, A.; Hutter, K.; Kirsch, R.; Kluge, H.; Thiericke, R. Secundary metabolites by chemical screening. 9. decarestrictines, a new family of inhibitors of cholesterol biosynthesis from penicillium: ii. structure elucidation of the decarestrictines A to D. J. Antibiot. (Tokyo), 1992, 45, 66-73.
127. Grabley, S.; Granzer, E.; Hutter, K.; Ludwig, D.; Mayer, M.; Thiericke, R.; Till, G.; Wink, J.; Philipps, S.; Zeeck, A. Secundary metabolites by chemical screening. 8. decarestrictines, a new family of inhibitors of cholesterol biosynthesis from penicillium: I. Strain description, fermentation, isolation and properties. J. Antibiot. J. Antibiot. (Tokyo), 1992, 45, 56-65.
128. Grabley, S.; Hammann, P.; Hutter, K.; Kirsch, R.; Kluge, H.; Thiericke, R.; Mayer, M.; Zeeck, A. Secundary metabolites by chemical screening. 20. decarestrictines, a new family of inhibitors of cholesterol biosynthesis from penicillium: III. Decarestrictine E to M. J. Antibiot. (Tokyo), 1992, 45, 1176-1181.
129. Romo, D.; Harrison, P.H.; Jenkins, S.I.; Riddoch, R.W.; Park, K.; Yang, H.W.; Zhao, C.; Wright, G.D. Synthesis and inhibitory action on HMG-CoA synthase of racemic and optically active oxetan-2-ones (B-Lactones). Bioorg. Med. Chem., 1998, 6, 1255-1272.
130. Schroepfer, G.J. Jr.; Parish, E.J.; Chen, H.W.; Kandutsch, A.A. Inhibition of sterol biosynthesis in L cells and mouse liver cells by 15-oxygenated sterols. J. Biol. Chem., 1977, 252, 8975-8980.
131. Taillefumiera, C.; Fornelb, D.; Chapleur, Y.; Design and synthesis of bisubstrate analogues for 3-hydroxy-3-methyl-glutaryl coenzyme a reductase. Bioorg. Med. Chem. Lett., 1996, 6, 615-618.
132. Turabi, N.; DiPietro, R.A.; Mantha, S.; Ciosek, C.; Rich, L.; Tu, J.I. 3-Hydroxy-3-methylglutaryl-coenzyme a reductase inhibitors: Oxime ether analogs of pravastatin. Bioorg. Med. Chem., 1995, 3, 1479-1484.
133. Watanabe, M.; Koike, H.; Ishiba, T.; Okada, T.; Seo, S.; Hirai, K. Synthesis and biological activity of methanesulfonamide pyrimidine- and N-methanesulfonyl pyrrole-substituted 3,5- dihydroxy-6-heptenoates, a novel series of HMG-CoA reductase inhibitors. Bioorg. Med. Chem., 1997, 5, 437-444.
134. Zhang, Q.Y.; Wan, J.; Xu, X.; Yang, G.F.; Ren, Y.L.; Liu, J.J.; Wang, H.; Guo, Y. Structure-based rational quest for potential novel inhibitors of Human HMG-CoA reductase by combining CoMFA 3D QSAR modeling and virtual screening. J. Comb. Chem., 2007, 9, 131-138.
135. Stat Soft.Inc., 2002.
136. Microsoft.Corp., 2002, Microsoft Excel
137. Fernandez, M.; Caballero, J.; Tundidor-Camba, A. Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors. Bioorg. Med. Chem., 2006, 14, 4137-4150.
138. Estrada, E.; Molina, E. Automatic extraction of structural alerts for predicting chromosome aberrations of organic compounds. J. Mol. Graph. Model., 2006, 25, 275-288.
139. Estrada, E.; Uriarte, E.; Molina, E.; Simon-Manso, Y.; Milne, G.W. An integrated in silico analysis of drug-binding to human serum albumin. J. Chem. Inf. Model., 2006, 46, 2709-2724.
140. Prado-Prado, F.J.; Gonzalez-Diaz, H.; de la Vega, O.M.; Ubeira, F.M.; Chou, K.C. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem., 2008, 16, 5871-5880.