Generic placeholder image

Infectious Disorders - Drug Targets


ISSN (Print): 1871-5265
ISSN (Online): 2212-3989

Research Article

3D QSAR, Docking, Molecular Dynamics Simulations and MM-GBSA studies of Extended Side Chain of the Antitubercular Drug (6S) 2-Nitro-6- {[4-(trifluoromethoxy) benzyl] oxy}-6,7-dihydro-5H-imidazo[2,1-b] [1,3] oxazine.

Author(s): Hemchandra K. Chaudhari* and Akshata Pahelkar

Volume 19, Issue 2, 2019

Page: [145 - 166] Pages: 22

DOI: 10.2174/1871526518666181015145545

Price: $65


Background: PA-824 analogues have been proposed on a promising approach for treating MDR/XDR tuberculosis. In order to understand the structural requirement of reported extended side chain analogues were studied to get insight into their structural requirements responsible for high affinity as a ligand-based pharmacophore, 3D-QSAR model have been developed. Docking and molecular dynamics studies revealed the better binding interaction of inhibitor binding pocket of deazaflavin dependent nitroreductase (Ddn) with cofactor F420 crystal.

Methods: For pharmacophore generation and atom-based 3D-QSAR analysis, a dataset of 84 compounds were selected which were carried out using PHASE. The docking studies were performed using Glide module consists of five steps protein preparation, ligand preparation, receptor grid generation, actual docking procedure and finally viewing the docking results using the poseviewer. QikProp provides ranges for comparing a particular molecule’s properties with those of 95% of known drugs. Molecular dynamics (MD) simulations for docking complex of deazaflavin dependent nitroreductase (Ddn) with molecule 63 were performed using Desmond. Prime Molecular Mechanics/Generalized-Born/Surface Area (MM-GBSA) was used for the calculation of binding free energy for the docked complexes.

Results: The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R2 = 0.8988 for training set compounds, higher variance ratio F= 127.3 and the model generated showed excellent predictive power, with a correlation coefficient of structure to analyses Q2= 0.8543 for a randomly chosen test set of 17 compounds. The binding position of most active molecule 63 is shown in figure 4. Several favorable interactions between ligand and enzyme clearly observed; H-bond showed between O atom presence as spacer in C-6- 2-Nitro-6-{[4-(trifluoromethoxy) benzyl] oxy}-6,7-dihydro-5H-imidazo[2,1-b] [1,3] oxazine and Asn 62. Weak hydrogen bonding observed between N1 atom in imidazole ring and Asn91. The binding of imidazole nucleus occurs at site, which has extensive hydrophobic interaction with Arg60 residues. All these pharmacokinetic parameters within the acceptable range defined for human use, thereby indicating their potential as drug- like molecule. Stability of deazaflavin dependent nitroreductase (Ddn) with molecule 63 complex was evaluated through 100 ns molecular dynamic simulations. Main contributions to the tight binding of molecule 63 to Ddn are the exceptionally electrostatics (dG_bind_Coulomb) and enhance hydrogen bond interactions (dG_bind_Hbond).

Conclusion: Docking, MM-GBSA, MD stimulation, pharmacophore model and 3D-QSAR studies as well as QikProp pharmacokinetic analysis presented in this paper is hoped to be a primer towards the development of various novel PA-824 with different chemical scaffolds and further its biological activity predictions to invent novel, potent, selective and safe PA-824 analogues for the treatment of MDR/XDR tuberculosis. Moreover, further use of contemporary experimental and computational techniques to data presented here may widen its scope and applicability.

Keywords: 3D-QSAR, PA-824, PHASE, MDR- tuberculosis, pharmacophore model, simulations.

Graphical Abstract
Global Tuberculosis Control: WHO Report 2015., World Health Organization: Geneva, Switzerland,. 2015.
Cox, H.; Kebede, Y.; Allamuratova, Y.S.; Ismailov, G.; Davletmuratova, Z.; Byrnes, G.; Stone, C.; Niemann, S.; Rusch-Gerdes, S.; Blok, L.; Doshetov, D. Tuberculosis recurrence and mortality after successful treatment: impact of drug resistance. PLoS Med., 2006, 31, 836-1843.
Kirimuhuzya, C. . Multi-Drug/Extensively D rug Resistant Tuberculosis (Mdr/Xdr-Tb): Renewed Global Battle Against Tuberculosis? Understanding Tuberculosis - New Approaches to Fighting Against Drug Resistance, Dr. Pere-Joan Cardona (Ed.), Publisher: InTech, , 2012; pp. 1-32.
Migliori, G.B.; Dheda, K.; Centis, R.; Mwaba, P.; Bates, M.; O’Grady, J.; Hoelscher, M.; Zumla, A. Review of multidrug-resistant and extensively drug-resistant TB: global perspectives with a focus on sub-Saharan Africa. Trop. Med. Int, 2010, 15, 1052-1066.
Zumla, A.; Nahid, P.; Cole, S.T. Advances in the development of new tuberculosis drugs and treatment regimens. J. Nat. Rev, 2013, 12, 388-404.
Luetkemeyer, A.F.; Getahun, H.; Chamie, G.; Lienhardt, C.; Havlir, D.V. Tuberculosis drug development: ensuring people living with HIV are not left behind. Am. J. Respir. Crit. Care Med., 2011, 184, 1107-1113.
Wong, E.B.; Cohen, K.A.; Bishai, W.R. Rising to the challenge: new therapies for tuberculosis. J. Trends Microbiol, 2013, 21, 493-501.
Field, S.K. Safety and Efficacy of Delamanid in the Treatment of Multidrug-Resistant Tuberculosis (MDR-TB). Clin. Med. Insights Ther., 2013, 5, 137-149.
Dawson, R.; Diacon, A. PA-824, moxifloxacin and pyrazinamide combination therapy for tuberculosis. Expert Opin. Investig. Drugs, 2013, 22, 927-932.
Manjunatha, U.; Boshoff, H.I.M.; Barry, C.E. The mechanism of action of PA-824: Novel insights from transcriptional profiling. Commun. Integr. Biol., 2009, 2, 215-218.
Palmer, B.D.; Hamish, S.; Blaser, A.; Kmentova, I.; Franzblau, S.G.; Wan, B.; Wang, Y.; Denny, W.A.; Thompson, A.M. Synthesis and structure-activity relationships for extended side chain analogues of the antitubercular drug (6S)-2-nitro-6-[4-(trifluoro-methoxy) benzyl] oxy-6,7-dihydro-5H-imidazo[2,1-b] [1,3] oxazine (PA-824). J. Med. Chem., 2015, 58, 3036-3059.
Phase 3.9; Schrödinger, LLC: New York, NY, 2014.
Dixon, S.L.; Smondyrev, A.M. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput. Aided Mol. Des., 2006, 20, 647-671.
MacroModel 9.7; Schrödinger, LLC: New York, NY, 2014.
LigPrep 2.3; Schrödinger, LLC: New York, NY, 2014.
Regina, G.L.; Silvestri, R. New pyrrole inhibitors of monoamine oxidase: synthesis, biological evaluation, and structural determinants of MAO-A and MAO-B selectivity. J. Med. Chem., 2007, 50, 922-931.
Friesner, R.A. LBanks, J.; Murphy, R. B.; Halgren, T. A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47, 1739-1749.
Glide 6.3; Schodinger, LLC: New York, NY, 2014.
QikProp 3.2; Schodinger, LLC: New York, NY, 2012.
Desmond 3.8; Schodinger, LLC: New York, NY, 2014.
Prime 2.1; Schodinger,LLC: New York, NY, 2014.
Rastelli, G.; Del Rio, A.; Degliesposti, G.; Sgobba, M. Fast andaccurate predictions of binding free energies using MM-PBSAand MM-GBSA. J. Comput. Chem., 2010, 31, 797-810.

Rights & Permissions Print Export Cite as
© 2024 Bentham Science Publishers | Privacy Policy