Login Register Cart 0
Generic placeholder image

Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Quantitative Structure Activity/Pharmacokinetics Relationship Studies of HIV-1 Protease Inhibitors Using Three Modelling Methods

Author(s): Dan Han, Jianjun Tan*, Jingrui Men, Chunhua Li and Xiaoyi Zhang

Volume 17, Issue 4, 2021

Published on: 26 August, 2019

Page: [396 - 406] Pages: 11

DOI: 10.2174/1573406415666190826154505

Price: $65

Abstract

Background: HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application.

Objective: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities.

Methods: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN).

Results: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and r2pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r2 of 0.952, 0.869 and 0.960, respectively, and the r2pred of 0.852, 0.628 and 0.814, respectively.

Conclusion: Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.

Keywords: Human immunodeficiency virus, HIV, Protease inhibitors, SVM, PLSR, BPNN, descriptors.

« Previous Next »
Graphical Abstract

[1]
Han, D.; Su, M.; Tan, J.J.; Li, C.H.; Zhang, X.Y.; Wang, C.X. Structure–activity relationship and binding mode studies for a series of diketo-acids as HIV integrase inhibitors by 3D-QSAR, molecular doc king and molecular dynamics simulations. RSC. Adv., 2016, 6, 27594-27606.
[http://dx.doi.org/10.1039/C6RA00713A]
[2]
Yedavalli, V.R.; Jeang, K.T. Methylation: a regulator of HIV-1 replication? Retrovirology, 2007, 4(1), 9.
[http://dx.doi.org/10.1186/1742-4690-4-9] [PMID: 17274823]
[3]
Tan, J.; Su, M.; Zeng, Y.; Wang, C. Design, synthesis and activity evaluation of novel peptide fusion inhibitors targeting HIV-1 gp41. Bioorg. Med. Chem., 2016, 24(2), 201-206.
[http://dx.doi.org/10.1016/j.bmc.2015.12.003] [PMID: 26706116]
[4]
Barré-Sinoussi, F.; Ross, A.L.; Delfraissy, J.F. Past, present and future: 30 years of HIV research. Nat. Rev. Microbiol., 2013, 11(12), 877-883.
[http://dx.doi.org/10.1038/nrmicro3132] [PMID: 24162027]
[5]
Chiappini, E.; Berti, E.; Gianesin, K.; Petrara, M.R.; Galli, L.; Giaquinto, C.; de Martino, M.; De Rossi, A. Pediatric human immunodeficiency virus infection and cancer in the highly active antiretroviral treatment (HAART) era. Cancer Lett., 2014, 347(1), 38-45.
[http://dx.doi.org/10.1016/j.canlet.2014.02.002] [PMID: 24513180]
[6]
Stein, J.; Storcksdieck Genannt Bonsmann, M.; Streeck, H. Barriers to HIV Cure. HLA, 2016, 88(4), 155-163.
[http://dx.doi.org/10.1111/tan.12867] [PMID: 27620852]
[7]
Plosker, G.L.; Scott, L.J. Saquinavir: a review of its use in boosted regimens for treating HIV infection. Drugs, 2003, 63(12), 1299-1324.
[http://dx.doi.org/10.2165/00003495-200363120-00007] [PMID: 12790697]
[8]
Hsu, A.; Granneman, G.R.; Bertz, R.J. Erratum to Ritonavir: Clinical Pharmacokinetics and interactions with other anti-HIV agents. Clin. Pharmacokinet., 2012, 35(6), 473.
[http://dx.doi.org/10.1007/BF03259712]
[9]
Plosker, G.L.; Noble, S. Indinavir: A review of its use in the management of HIV infection. Drugs, 1999, 58(6), 1165-1203.
[http://dx.doi.org/10.2165/00003495-199958060-00011] [PMID: 10651394]
[10]
Perry, C.M.; Frampton, J.E.; McCormack, P.L.; Siddiqui, M.A.; Cvetković, R.S. Nelfinavir: A review of its use in the management of HIV infection. Drugs, 2005, 65(15), 2209-2244.
[http://dx.doi.org/10.2165/00003495-200565150-00015] [PMID: 16225378]
[11]
Wire, M.B.; Shelton, M.J.; Studenberg, S. Fosamprenavir: Clinical Pharmacokinetics and drug interactions of the amprenavir prodrug. Clin. Pharmacokinet., 2006, 45(2), 137-168.
[http://dx.doi.org/10.2165/00003088-200645020-00002] [PMID: 16485915]
[12]
Oldfield, V.; Plosker, G.L. Lopinavir/ritonavir: A review of its use in the management of HIV infection. Drugs, 2006, 66(9), 1275-1299.
[http://dx.doi.org/10.2165/00003495-200666090-00012] [PMID: 16827606]
[13]
Croxtall, J.D.; Perry, C.M. Lopinavir/Ritonavir: A review of its use in the management of HIV-1 infection. Drugs, 2010, 70(14), 1885-1915.
[http://dx.doi.org/10.2165/11204950-000000000-00000] [PMID: 20836579]
[14]
Swainston Harrison, T.; Scott, L.J.; Atazanavir, A. Atazanavir: A review of its use in the management of HIV infection. Drugs, 2005, 65(16), 2309-2336.
[http://dx.doi.org/10.2165/00003495-200565160-00010] [PMID: 16266202]
[15]
Croom, K.F.; Dhillon, S.; Keam, S.J. Atazanavir: A review of its use in the management of HIV-1 infection. Drugs, 2009, 69(8), 1107-1140.
[http://dx.doi.org/10.2165/00003495-200969080-00009] [PMID: 19496633]
[16]
Noble, S.; Goa, K.L. Amprenavir: A review of its clinical potential in patients with HIV infection. Drugs, 2000, 60(6), 1383-1410.
[http://dx.doi.org/10.2165/00003495-200060060-00012] [PMID: 11152018]
[17]
Fung, H.B.; Kirschenbaum, H.L.; Hameed, R. Amprenavir: A new human immunodeficiency virus type 1 protease inhibitor. Clin. Ther., 2000, 22(5), 549-572.
[http://dx.doi.org/10.1016/S0149-2918(00)80044-2] [PMID: 10868554]
[18]
Luna, B.; Townsend, M.U. Tipranavir: The first nonpeptidic protease inhibitor for the treatment of protease resistance. Clin. Ther., 2007, 29(11), 2309-2318.
[http://dx.doi.org/10.1016/j.clinthera.2007.11.007] [PMID: 18158073]
[19]
Ghosh, A.K.; Sridhar, P.R.; Leshchenko, S.; Hussain, A.K.; Li, J.; Kovalevsky, A.Y.; Walters, D.E.; Wede Kind, J.E.; Grum-Tokars, V.; Das, D.; Koh, Y.; Maeda, K.; Gatanaga, H.; Weber, I.T.; Mitsuya, H. Structure-based design of novel HIV-1 protease inhibitors to combat drug resistance. J. Med. Chem., 2006, 49(17), 5252-5261.
[http://dx.doi.org/10.1021/jm060561m] [PMID: 16913714]
[20]
McCoy, C. Darunavir: A nonpeptidic antiretroviral protease inhibitor. Clin. Ther., 2007, 29(8), 1559-1576.
[http://dx.doi.org/10.1016/j.clinthera.2007.08.016] [PMID: 17919539]
[21]
Robertson, J.; Feinberg, J. Darunavir: A nonpeptidic protease inhibitor for antiretroviral-naive and treatment-experienced adults with HIV infection. Expert Opin. Pharmacother., 2012, 13(9), 1363-1375.
[http://dx.doi.org/10.1517/14656566.2012.681776] [PMID: 22594781]
[22]
Boffito, M.; Maitland, D.; Samarasinghe, Y.; Pozniak, A. The Pharmacokinetics of HIV protease inhibitor combinations. Curr. Opin. Infect. Dis., 2005, 18(1), 1-7.
[http://dx.doi.org/10.1097/00001432-200502000-00002] [PMID: 15647693]
[23]
Pan, A.C.; Borhani, D.W.; Dror, R.O.; Shaw, D.E. Molecular determinants of drug-receptor binding kinetics. Drug Discov. Today, 2013, 18(13-14), 667-673.
[http://dx.doi.org/10.1016/j.drudis.2013.02.007] [PMID: 23454741]
[24]
Lu, H.; Tonge, P.J. Drug-target residence time: Critical information for lead optimization. Curr. Opin. Chem. Biol., 2010, 14(4), 467-474.
[http://dx.doi.org/10.1016/j.cbpa.2010.06.176] [PMID: 20663707]
[25]
Copeland, R.A.; Pompliano, D.L.; Meek, T.D. Drug-target residence time and its implications for lead optimization. Nat. Rev. Drug Discov., 2006, 5(9), 730-739.
[http://dx.doi.org/10.1038/nrd2082] [PMID: 16888652]
[26]
Daryaee, F.; Chang, A.; Schiebel, J.; Lu, Y.; Zhang, Z.; Kapilashrami, K.; Walker, S.G.; Kisker, C.; Sotriffer, C.A.; Fisher, S.L.; Tonge, P.J. Correlating Drug-Target kinetics and In vivo Pharmacodynamics: Long Residence Time Inhibitors of the FabI Enoyl-ACP Reductase. Chem. Sci. (Camb.), 2016, 7(9), 5945-5954.
[http://dx.doi.org/10.1039/C6SC01000H] [PMID: 27547299]
[27]
Ren, S.; Gao, L. Improvement of the prediction ability of multivariate calibration by a method based on the combination of data fusion and least squares support vector machines. Analyst (Lond.), 2011, 136(6), 1252-1261.
[http://dx.doi.org/10.1039/c0an00433b] [PMID: 21243169]
[28]
Balabin, R.M.; Loma Kina, E.I. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? Phys. Chem. Chem. Phys., 2011, 13(24), 11710-11718.
[http://dx.doi.org/10.1039/c1cp00051a] [PMID: 21594265]
[29]
Fu, H.Y.; Wu, H.L.; Zou, H.Y.; Tang, L.J.; Xu, L.; Cai, C.B.; Nie, J.F.; Yu, R.Q. Automatic configuration of optimized sample-weighted least-squares support vector machine by particle swarm optimization for multivariate spectral analysis. Anal. Methods, 2010, 2(3), 282.
[http://dx.doi.org/10.1039/b9ay00250b]
[30]
Mandi, P.; Shoombuatong, W.; Phanusumporn, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V.; Bulow, L.; Nantasenamat, C. Exploring the origins of structure-oxygen affinity relationship of human hemoglobin allosteric effector. Mol. Simul., 2014, 41(15), 1283-1291.
[http://dx.doi.org/10.1080/08927022.2014.981180]
[31]
Adhikari, N.; Halder, A.K.; Saha, A.; Das Saha, K.; Jha, T. Structural findings of phenylindoles as cytotoxic antimitotic agents in human breast cancer cell lines through multiple validated QSAR studies. Toxicol. In Vitro, 2015, 29(7), 1392-1404.
[http://dx.doi.org/10.1016/j.tiv.2015.05.017] [PMID: 26026499]
[32]
Pérez-Rodríguez, M.; Horák-Terra, I.; Rodríguez-Lado, L.; Martínez Cortizas, A. Modelling mercury accumulation in minerogenic peat combining FTIR-ATR spectroscopy and partial least squares (PLS). Spectrochim. Acta A Mol. Biomol. Spectrosc., 2016, 168, 65-72.
[http://dx.doi.org/10.1016/j.saa.2016.05.052] [PMID: 27280857]
[33]
Sun, L.Z.; Ling, Z.C.; Zhang, J.; Li, B.; Chen, J.; Wu, Z.C.; Liu, J.Z. Lunar iron and optical maturity mapping: Results from partial least squares modeling of Chang’E-1 IIM data. Icarus, 2016, 280, 183-198.
[http://dx.doi.org/10.1016/j.icarus.2016.07.010]
[34]
Schindler, M. A QSAR for the prediction of rate constants for the reaction of VOCs with nitrate radicals. Chemosphere, 2016, 154, 23-33.
[http://dx.doi.org/10.1016/j.chemosphere.2016.03.096] [PMID: 27037771]
[35]
Chourasiya, R.K.; Mourya, V.; Agrawal, R.K. QSAR analysis for some β-carboline derivatives as anti-tumor. J. Saudi Chem. Soc., 2012, 14(5), 536-542.
[http://dx.doi.org/10.1016/j.jscs.2012.07.015]
[36]
Vimaladevi, M.; Kalaavathi, B. A microarray gene expression data classification using hybrid back propagation neural network. Genetika, 2014, 46(3), 1013-1026.
[http://dx.doi.org/10.2298/GENSR1403013V]
[37]
Yu, H.; Rossi, G.; Braglia, A.; Perrone, G. Application of Gaussian beam ray-equivalent model and back-propagation artificial neural network in laser diode fast axis collimator assembly. Appl. Opt., 2016, 55(23), 6530-6537.
[http://dx.doi.org/10.1364/AO.55.006530] [PMID: 27534506]
[38]
Pan, Y.; Jiang, J.C.; Wang, R.; Cao, H.Y. Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds. Chemometr. Intell. Lab., 2008, 92(2), 169-178.
[http://dx.doi.org/10.1016/j.chemolab.2008.03.002]
[39]
Wang, R.; Jiang, J.; Pan, Y.; Cao, H.; Cui, Y. Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices. J. Hazard. Mater., 2009, 166(1), 155-186.
[http://dx.doi.org/10.1016/j.jhazmat.2008.11.005] [PMID: 19101083]
[40]
Wachtmeister, J.; Muhlman, A.; Claasson, B.; Kvarnstrom, I.; Hallberg, A.; Samuelsson, B. Impact of the central hydroxyl groups on the activity of symmetrical HIV-1 protease inhibitors derived from L-mannaric acid. Tetrahedron Lett., 2000, 56(20), 3219-3225.
[http://dx.doi.org/10.1016/S0040-4020(00)00220-9]
[41]
Dierynck, I.; De Wit, M.; Gustin, E.; Keuleers, I.; Vandersmissen, J.; Hallenberger, S.; Hertogs, K. Binding kinetics of darunavir to human immunodeficiency virus type 1 protease explain the potent antiviral activity and high genetic barrier. J. Virol., 2007, 81(24), 13845-13851.
[http://dx.doi.org/10.1128/JVI.01184-07] [PMID: 17928344]
[42]
Darke, P.L.; Jordan, S.P.; Hall, D.L.; Zugay, J.A.; Shafer, J.A.; Kuo, L.C. Dissociation and association of the HIV-1 protease dimer subunits: equilibria and rates. Biochemistry, 1994, 33(1), 98-105.
[http://dx.doi.org/10.1021/bi00167a013] [PMID: 8286367]
[43]
Alterman, M.; Björsne, M.; Mühlman, A.; Classon, B.; Kvarnström, I.; Danielson, H.; Markgren, P.O.; Nillroth, U.; Unge, T.; Hallberg, A.; Samuelsson, B. Design and synthesis of new potent C2-symmetric HIV-1 protease inhibitors. Use of L-mannaric acid as a peptidomimetic scaffold. J. Med. Chem., 1998, 41(20), 3782-3792.
[http://dx.doi.org/10.1021/jm970777b] [PMID: 9748353]
[44]
Callebaut, C.; Stray, K.; Tsai, L.; Williams, M.; Yang, Z.Y.; Cannizzaro, C.; Leavitt, S.A.; Liu, X.; Wang, K.; Murray, B.P.; Mulato, A.; Hatada, M.; Pris Kich, T.; Par Kin, N.; Swaminathan, S.; Lee, W.; He, G.X.; Xu, L.; Cihlar, T. In vitro characterization of GS-8374, a novel phosphonate-containing inhibitor of HIV-1 protease with a favorable resistance profile. Antimicrob. Agents Chemother., 2011, 55(4), 1366-1376.
[http://dx.doi.org/10.1128/AAC.01183-10] [PMID: 21245449]
[45]
Hanlon, M.H.; Porter, D.J.T.; Furfine, E.S.; Spaltenstein, A.; Carter, H.L.; Danger, D.; Shu, A.Y.L.; Kaldor, I.W.; Miller, J.F.; Samano, V.A. Inhibition of wild-type and mutant human immunodeficiency virus type 1 proteases by GW0385 and other arylsulfonamides. Biochemistry, 2004, 43(45), 14500-14507.
[http://dx.doi.org/10.1021/bi0488799] [PMID: 15533054]
[46]
Markgren, P.O.; Hämäläinen, M.; Danielson, U.H. Kinetic analysis of the interaction between HIV-1 protease and inhibitors using optical biosensor technology. Anal. Biochem., 2000, 279(1), 71-78.
[http://dx.doi.org/10.1006/abio.1999.4467] [PMID: 10683232]
[47]
Markgren, P.O.; Schaal, W.; Hämäläinen, M.; Karlén, A.; Hallberg, A.; Samuelsson, B.; Danielson, U.H. Relationships between structure and interaction kinetics for HIV-1 protease inhibitors. J. Med. Chem., 2002, 45(25), 5430-5439.
[http://dx.doi.org/10.1021/jm0208370] [PMID: 12459011]
[48]
Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; Tanchuk, V.Y.; Prokopenko, V.V. Virtual computational chemistry laboratory--design and description. J. Comput. Aided Mol. Des., 2005, 19(6), 453-463.
[http://dx.doi.org/10.1007/s10822-005-8694-y] [PMID: 16231203]
[49]
Tetko, I.V. Computing chemistry on the web. Drug Discov. Today, 2005, 10(22), 1497-1500.
[http://dx.doi.org/10.1016/S1359-6446(05)03584-1] [PMID: 16257371]
[50]
Todeschini, R.; Consonni, V.; Mannhold, R. Descriptors for Chemoinformatics volume i: Alphabetical listing / volume ii: appendices, references. In: Wiley; , 2009.
[51]
Han, D.; Tan, J.J.; Zhou, Z.Y.; Li, C.H.; Zhang, X.Y.; Wang, C.X. Combined topomer CoMFA and hologram QSAR studies of a series of pyrrole derivatives as potential HIV fusion inhibitors. Med. Chem. Res., 2018, 27(7), 1770-1781.
[http://dx.doi.org/10.1007/s00044-018-2190-0]
[52]
Ghasemi, J.B.; Nazarshodeh, E.; Abedi, H. Molecular doc king, 2D and 3D-QSAR studies of new indole-based derivatives as HCV-NS5B polymerase inhibitors. J. Iran. Chem. Soc., 2015, 12(10), 1789-1799.
[http://dx.doi.org/10.1007/s13738-015-0654-4]
[53]
Pratim Roy, P.; Paul, S.; Mitra, I.; Roy, K. On two novel parameters for validation of predictive QSAR models. Molecules, 2009, 14(5), 1660-1701.
[http://dx.doi.org/10.3390/molecules14051660] [PMID: 19471190]
[54]
Gramatica, P. Principles of QSAR models validation: internal and external. QSAR Comb. Sci., 2007, 26(5), 694-701.
[http://dx.doi.org/10.1002/qsar.200610151]
[55]
Guha, R.; Serra, J.R.; Jurs, P.C. Generation of QSAR sets with a self-organizing map. J. Mol. Graph. Model., 2004, 23(1), 1-14.
[http://dx.doi.org/10.1016/j.jmgm.2004.03.003] [PMID: 15331049]
[56]
Yang, L.; Sun, Q. Comparison of chemometric approaches for near-infrared spectroscopic data. Anal. Methods, 2016, 8(8), 1914-1923.
[http://dx.doi.org/10.1039/C5AY01304F]
[57]
Wold, S.; Sjostrom, M.; Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab., 2001, 58(2), 109-130.
[http://dx.doi.org/10.1016/S0169-7439(01)00155-1]
[58]
Li, R.H.; Meng, G.X.; Gao, N.K.; Xie, H.K. Combined use of partial least-squares regression and neural network for residual life estimation of large generator stator insulation. Meas. Sci. Technol., 2007, 18(17), 2074-2082.
[http://dx.doi.org/10.1088/0957-0233/18/7/038]
[59]
Hamadache, M.; Hanini, S.; Benkortbi, O.; Amrane, A.; Khaouane, L.; Moussa, C.S. Artificial neural network-based equation to predict the toxicity of herbicides on rats. Chemometr. Intell. Lab., 2016, 154, 7-15.
[http://dx.doi.org/10.1016/j.chemolab.2016.03.007]
[60]
Saidi, A.; Mirzaei, M. Predicton of AHAS inhibition by sulfonylurea herbicides using genetic algorithm and artificial neural network. Indian J. Chem. Technol., 2014, 23, 121-130.
[61]
Yang, T.M.; Fan, S.K.; Fan, C.; Hsu, N.S. Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis. Environ. Monit. Assess., 2014, 186(8), 4925-4934.
[http://dx.doi.org/10.1007/s10661-014-3748-z] [PMID: 24691737]
[62]
Lin, B.; Lin, G.; Liu, X.; Ma, J.; Wang, X.; Lin, F.; Hu, L. Application of back-propagation artificial neural network and curve estimation in Pharmacokinetics of losartan in rabbit. Int. J. Clin. Exp. Med., 2015, 8(12), 22352-22358.
[PMID: 26885213]

Rights & Permissions Print Cite
Article Metrics
14
1
© 2024 Bentham Science Publishers | Privacy Policy