Pharmacophore mapping, 3D QSAR, docking, and ADME prediction studies of novel Benzothiazinone derivatives

Year : 2024 | Volume : 01 | Issue : 01 | Page : 59-82

    Jahaan Shaikh

  1. Afzal Nagani

  2. Salman Patel

  3. Harnisha Patel


 Background
Tuberculosis is a major public health concern worldwide which is caused by Mycobacterium tuberculosis. DprE1
(Decaprenyl Phosphoryl Ribose 2’- Epimerase) is the most challenging target for development of novel anti-
tubercular agents because it is a small protein and located into cytoplasmic membrane. So, novel anti-TB drugs did
not bound effectively with it. DprE1 catalyzes the oxidation of the 2’ hydroxyl group of DPR (Decaprenyl
Phosphoryl D- Ribose) to give Ketoribose DPX (Decaprenyl Phosphoryl 2-Ketoribose). Benzothiazinone is the most
potent Pharmacophore as DprE1 inhibitors. BTZ043, BTZ038, PBTZ169 and TMC-207 (Bedaquiline) are clinical
trial drugs as DprE1 inhibitors.
 Objective
To develop novel Benzothiazinone derivatives by performing Pharmacophore mapping, 3D QSAR, docking and
ADME prediction studies.
 Method
Pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives gives
information about essential features required for producing a biological response. Benzothiazinone (Ligand code:
73) has the essential features with the Fitness score for active ligands- 3.000 and as a Reference Ligand.
(Pharmacophore Hypothesis: AHHRR_1). It is validated by ROC (Receiver Operating Characteristics) value as 0.71
which indicates perfect performance of Pharmacophore mapping. Benzothiazinone congeneric series are required for
building 3D QSAR model. The validation criteria of 3D QSAR Model- Standard Deviation is 0.1531, R 2 value is
0.9754, R 2 CV (Cross Validation) value is 0.2118, R 2 Scramble value is 0.9500, Stability value is 0.261, F value
(Variance) is 205.0 (For Training set), P value (Probability value) is 1.58e-23, Q 2 value is 0.7632 and Pearson-r
value is 0.8939 (For Test set). PBTZ129 is the Reference Molecule (MIC value- 0.00019 µM). The contour maps
indicate the positions at which steric, electrostatic, hydrophobic, H-bond acceptor and H-bond donor groups are
required for generating SAR.
 Result
Novel Benzothiazinone derivatives designed according to contour maps and their biological activities were
predicted. Docking scores (PDB ID- 4NCR) are good and amino acid interactions of novel Benzothiazinone
derivatives are matched with in-built ligand 26J. Docking validation was also performed by comparing RMSD
values of superimposed atoms of co-crystal ligand and its conformer.
 Conclusion
These novel molecules can be proposed for synthesis and biological evaluation.

Keywords: DprE1, Benzothiazinone, Pharmacophore mapping, 3D QSAR, Docking, ADME prediction.

[This article belongs to International Journal of Antibiotics(ijab)]

How to cite this article: Jahaan Shaikh, Afzal Nagani, Salman Patel, Harnisha Patel Pharmacophore mapping, 3D QSAR, docking, and ADME prediction studies of novel Benzothiazinone derivatives ijab 2024; 01:59-82
How to cite this URL: Jahaan Shaikh, Afzal Nagani, Salman Patel, Harnisha Patel Pharmacophore mapping, 3D QSAR, docking, and ADME prediction studies of novel Benzothiazinone derivatives ijab 2024 {cited 2024 Feb 01};01:59-82. Available from:


  1. World Health Organization. Global Tuberculosis Report 2022. InGlobal tuberculosis report 2022 2022.
  2. Centers for Disease Control and Prevention. Tuberculosis (TB). Available from:
  3. Mikusova K, Makarov V, Neres J. DprE1–from the discovery to the promising tuberculosis drug target. Current pharmaceutical design. 2014 Aug 1;20(27):4379-403.
  4. Mak PA, Rao SP, Tan MP, et al. A high-throughput screen to identify inhibitors of DprE1, a novel enzyme target for tuberculosis therapy. PLoS One. 2012;7(3):e35851.
  5. Zumla A, Chakaya J, Centis R, et al. Tuberculosis treatment and management—an update on treatment regimens, trials, new drugs, and adjunct therapies. Lancet Respir Med. 2015;3(3):220-234.
  6. Zhang G, Guo S, Cui H, Qi J. Virtual screening of small molecular inhibitors against DprE1. Molecules. 2018 Feb 27;23(3):524.
  7. Piton J, Foo CS, Cole ST. Structural studies of Mycobacterium tuberculosis DprE1 interacting with its inhibitors. Drug discovery today. 2017 Mar 1;22(3):526-33.
  8. Mikusova K, Slayden RA, Besra GS, Brennan PJ. Biogenesis of the mycobacterial cell wall and the site of action of ethambutol. Antimicrob Agents Chemother. 1995;39(11):2484-2489.
  9. Hu XP, Yang L, Chai X, Lei YX, Alam MS, Liu L, Shen C, Jiang DJ, Wang Z, Liu ZY, Xu L. Discovery of novel DprE1 inhibitors via computational bioactivity fingerprints and structure-based virtual screening. Acta Pharmacologica Sinica. 2022 Jun;43(6):1605-15.
  10. Makarov V, Manina G, Mikusova K, et al. Benzothiazinones kill Mycobacterium tuberculosis by blocking arabinan synthesis. Science. 2009;324(5928):801-804.
  11. Trefzer C, Škovierová H, Buroni S, et al. Benzothiazinones: Prodrugs that covalently modify the decaprenylphosphoryl-β-d-ribose 2′-epimerase DprE1 of Mycobacterium tuberculosis. J Am Chem Soc. 2012;134(2):912-915.
  12. Manjunatha U, Boshoff HI, Barry CE 3rd. The mechanism of action of PA-824: Novel insights from transcriptional profiling. Commun Integr Biol. 2009;2(3):215-218.
  13. Lv K, You X, Wang B, Wei Z, Chai Y, Wang B, Wang A, Huang G, Liu M, Lu Y. Identification of better pharmacokinetic benzothiazinone derivatives as new antitubercular agents. ACS Medicinal Chemistry Letters. 2017 Jun 8;8(6):636-41.
  14. Diacon AH, Pym A, Grobusch M, et al. Multidrug-resistant tuberculosis and culture conversion with bedaquiline. N Engl J Med. 2014;371(8):723-732.
  15. Peng CT, Gao C, Wang NY, You XY, Zhang LD, Zhu YX, Xv Y, Zuo WQ, Ran K, Deng HX, Lei Q. Synthesis and antitubercular evaluation of 4-carbonyl piperazine substituted 1, 3-benzothiazin-4-one derivatives. Bioorganic & medicinal chemistry letters. 2015 Apr 1;25(7):1373-6.
  16. Hu Y, Bajorath J. Computational methods for the analysis of ligand-receptor interactions. ChemMedChem. 2011;6(6):1042-1051.
  17. Kellenberger E, Rodrigo J, Müller P, Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins. 2004;57(2):225-242.
  18. Evers A, Klabunde T. Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the α1A adrenergic receptor. J Med Chem. 2005;48(4):1088-1097.
  19. Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem. 2006;49(23):6789-6801.
  20. Koes DR, Camacho CJ. Pharmer: efficient and exact pharmacophore search. J Chem Inf Model. 2011;51(5):1307-1314.
  21. Korb O, Stützle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model. 2009;49(1):84-96.
  22. Zhang J, Li Y, Li H, et al. Molecular dynamics simulations combined with pharmacophore-based virtual screening and docking led to the discovery of novel acetylcholinesterase inhibitors. J Biomol Struct Dyn. 2017;35(10):2225-2235.
  23. Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci. 2010;11(5):2212-2238.
  24. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 1988;110(18):5959-5967.
  25. Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6-7):476-488.
  26. Kubinyi H. QSAR and 3D QSAR in drug design. Part 1: Methodology. Drug Discov Today. 1997;2(10):457-467.
  27. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005;33(Web Server issue):W363-W367.
  28. Klebe G. Comparative molecular similarity indices (CoMSIA) and comparative molecular field analysis (CoMFA). QSAR Comb Sci. 2002;21(3):237-239.
  29. Wang F, Yang W, Zhou B. Studies on the antibacterial activities and molecular mechanism of GyrB inhibitors by 3D-QSAR, molecular docking, and molecular dynamics simulation. Arabian Journal of Chemistry. 2022 Jun 1;15(6):103872.
  30. Panigrahi D, Mishra A, Sahu SK. Pharmacophore modeling, QSAR study, molecular docking, and insilico ADME prediction of 1, 2, 3-triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents. SN Applied Sciences. 2020 May;2(5):922.
  31. Duan J, Dixon SL, Lowrie JF, Sherman W. Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J Mol Graph Model. 2010;29(2):157-170.
  32. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3(11):935-949.
  33. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Mol Biol. 1982;161(2):269-288.
  34. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.
  35. Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol. 2011;9:71.
  36. Shoichet BK, McGovern SL, Wei B, Irwin JJ. Lead discovery using molecular docking. Curr Opin Chem Biol. 2002;6(4):439-446.
  37. Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785-2791.
  38. Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432(7019):862-865.
  39. Huang SY, Grinter SZ, Zou X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys. 2010;12(40):12899-12908.
  40. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337-341.
  41. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239-1249.
  42. Perola E, Walters WP, Charifson PS. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins. 2004;56(2):235-249.
  43. Egan WJ, Merz KM Jr, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem. 2000;43(21):3867-3877.
  44. Schieferdecker S, Bernal FA, Wojtas KP, Keiff F, Li Y, Dahse HM, Kloss F. Development of Predictive Classification Models for Whole Cell Antimycobacterial Activity of Benzothiazinones. Journal of Medicinal Chemistry. 2022 May 3;65(9):6748-63.
  45. Gleeson MP. Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem. 2008;51(4):817-834.
  46. Xu Y, Dai Z, Chen F, Gao S, Pei J, Lai L. Deep learning for drug-induced liver injury. J Chem Inf Model. 2015;55(10):2085-2093.
  47. Chikhale RV, Barmade MA, Murumkar PR, Yadav MR. Overview of the development of DprE1 inhibitors for combating the menace of tuberculosis. Journal of medicinal chemistry. 2018 May 31;61(19):8563-93.
  48. Lv K, You X, Wang B, Wei Z, Chai Y, Wang B, Wang A, Huang G, Liu M, Lu Y. Identification of better pharmacokinetic benzothiazinone derivatives as new antitubercular agents. ACS Medicinal Chemistry Letters. 2017 Jun 8;8(6):636-41.
  49. Gao C, Ye TH, Wang NY, Zeng XX, Zhang LD, Xiong Y, You XY, Xia Y, Xu Y, Peng CT, Zuo WQ. Synthesis and structure-activity relationships evaluation of benzothiazinone derivatives as potential anti-tubercular agents. Bioorganic & medicinal chemistry letters. 2013 Sep 1;23(17):4919-22.
  50. Piton J, Vocat A, Lupien A, Foo CS, Riabova O, Makarov V, Cole ST. Structure-based drug design and characterization of sulfonyl-piperazine benzothiazinone inhibitors of DprE1 from Mycobacterium tuberculosis. Antimicrobial agents and chemotherapy. 2018 Oct;62(10):e00681-18.
  51. Gao C, Peng C, Shi Y, You X, Ran K, Xiong L, Ye TH, Zhang L, Wang N, Zhu Y, Liu K. Benzothiazinethione is a potent preclinical candidate for the treatment of drug-resistant tuberculosis. Scientific reports. 2016 Jul 13;6(1):29717.
  52. Zhang G, Sheng L, Hegde P, Li Y, Aldrich CC. 8-cyanobenzothiazinone analogs with potent antitubercular activity. Medicinal Chemistry Research. 2021 Feb;30:449-58.
  53. Wang A, Xu S, Chai Y, Xia G, Wang B, Lv K, Ma C, Wang D, Wang A, Qin X, Liu M. Design, synthesis and biological activity of N-(amino) piperazine-containing benzothiazinones against Mycobacterium tuberculosis. European Journal of Medicinal Chemistry. 2021 Jun 5;218:113398.
  54. Mikusova K, Makarov V, Neres J. DprE1–from the discovery to the promising tuberculosis drug target. Current pharmaceutical design. 2014 Aug 1;20(27):4379-403.
  55. Makarov V, Lechartier B, Zhang M, Neres J, van der Sar AM, Raadsen SA, Hartkoorn RC, Ryabova OB, Vocat A, Decosterd LA, Widmer N. Towards a new combination therapy for tuberculosis with next generation benzothiazinones. EMBO molecular medicine. 2014 Mar;6(3):372-83.

Regular Issue Subscription Original Research
Volume 01
Issue 01
Received January 2, 2024
Accepted January 8, 2024
Published February 1, 2024