Molecular Diversity

, Volume 15, Issue 2, pp 417–426 | Cite as

Quantitative structure–activity relationship study of antitubercular fluoroquinolones

Full-length paper


Quantitative structure–activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (μg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure–activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.


Tuberculosis Fluoroquinolones DNA gyrase QSAR CP ANN 



Adenosine triphosphate


Minimal inhibitory concentration


Structure–activity relationship


Quantitative structure–activity relationship


Comprehensive descriptors for structural and statistical analysis


Multiple linear regression


Neural networks


Artificial neural networks


Counter propagation artificial neural networks


Kohonen artificial neural networks


Self organizing maps


Cross validation leave-one-out


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Supplementary material

11030_2010_9238_MOESM1_ESM.doc (354 kb)
ESM 1 (DOC 354 kb)
11030_2010_9238_MOESM2_ESM.doc (810 kb)
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11030_2010_9238_MOESM4_ESM.xls (28 kb)
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11030_2010_9238_MOESM5_ESM.xls (19 kb)
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11030_2010_9238_MOESM6_ESM.doc (120 kb)
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  1. 1.
    Kayukova LA, Praliev KD (2000) Main directions in the search for new antituberculous drugs. Pharm Chem J 34: 11–18. doi: 10.1007/BF02524551 CrossRefGoogle Scholar
  2. 2.
    Dye C, Scheele S, Dolin P, Pathania V, Raviglione MC (1999) Consensus statement. Global burden of tuberculosis estimated incidence, prevalence, and mortality by country. JAMA 282: 677–686PubMedCrossRefGoogle Scholar
  3. 3.
    Frieden TR, Sterling TR, Munsiff SS, Watt CJ, Dye C (2003) Tuberculosis. Lancet 362: 887–889. doi: 10.1016/S0140-6736(03)14333-4 PubMedCrossRefGoogle Scholar
  4. 4.
    Janin YL (2007) Antituberculosis drugs: ten years of research. Bioorg Med Chem 15: 2479–2513. doi: 10.1016/j.bmc.2007.01.030 PubMedCrossRefGoogle Scholar
  5. 5.
    Derouin F (2001) Anti-toxoplasmosis drugs. Curr Opin Invest Drugs 2: 1368–1374Google Scholar
  6. 6.
    Drlica K, Zhao X (1997) DNA gyrase, topoisomerase IV, and the 4-quinolones. Microbiol Mol Biol Rev 61: 377–392PubMedGoogle Scholar
  7. 7.
    Owens RC Jr, Ambrose PG (2000) Clinical use of the fluoroquinolones. Med Clin North Am 84: 1447–1469PubMedCrossRefGoogle Scholar
  8. 8.
    Hooper DC (2001) Mechanisms of action of antimicrobials: focus on fluoroquinolones. Clin Infect Dis 32: S9–S15PubMedCrossRefGoogle Scholar
  9. 9.
    Hooper DC (2001) Emerging mechanisms of fluoroquinolone resistance. Emerg Infect Dis 7: 337–341PubMedCrossRefGoogle Scholar
  10. 10.
    Ruiz J (2003) Mechanisms of resistance to quinolones: target alterations, decreased accumulation and DNA gyrase protection. J Antimicrob Chemother 51: 1109–1117. doi: 10.1093/jac/dkg222 PubMedCrossRefGoogle Scholar
  11. 11.
    Anquetin G, Greiner J, Vierling P (2005) Quinolone-based drugs against Toxoplasma gondii and Plasmodium spp. Curr Drug Targ: Infect Disord 5: 227–245CrossRefGoogle Scholar
  12. 12.
    Block, JH, Beale, JM (eds) (2004) Whilson and Gisvold’s textbook of organic medicinal and pharmaceutical chemistry. Lippincott Williams & Wilkins, Philadelphia, pp 247–252Google Scholar
  13. 13.
    Maxwell A (1997) DNA gyrase as a drug target. Trends Microbiol 5: 102–109PubMedCrossRefGoogle Scholar
  14. 14.
    Champoux JJ (2001) DNA topoisomerases: structure, function, and mechanism. Annu Rev Biochem 70: 369–413PubMedCrossRefGoogle Scholar
  15. 15.
    Peng H, Marians KJ (1993) Escherichia-coli topoisomerase-iv-purification, characterization, subunit structure, and subunit interactions. J Biol Chem 268: 24481–24490PubMedGoogle Scholar
  16. 16.
    Reece RJ, Maxwell A (1991) DNA gyrase—structure and function. Crit Rev Biochem Mol 26: 335–375CrossRefGoogle Scholar
  17. 17.
    Levine C, Hiasa H, Marians KJ (1998) DNA gyrase and topoisomerase IV: biochemical activities, physiological roles during chromosome replication, and drug sensitivities. Biochim Biophys Acta 1400: 29–43PubMedGoogle Scholar
  18. 18.
    Oblak M, Kotnik M, Solmajer T (2007) Discovery and development of ATPase inhibitors of DNA gyrase as antibacterial agents. Curr Med Chem 14: 2033–2047PubMedCrossRefGoogle Scholar
  19. 19.
    Owners RC, Ambrose PG (2005) Antimicrobial safety: focus on fluoroquinolones. Clin Infect Dis 41: S144–S157CrossRefGoogle Scholar
  20. 20.
    Domagala JM (1994) Structure-activity and structure-side-effect relationships for the quinolone antibacterials. J Antimic Chemother 33: 685–706CrossRefGoogle Scholar
  21. 21.
    NIAID-Division of AIDS, HIV/OI/TB Therapeutics Database.
  22. 22.
    Talete srl, DRAGON for Windows (Software for Molecular Descriptor Calculations), version 5.4, 2006.
  23. 23.
    Katritzky AR, Lobanov VS, Karelson M (1994) CODESSA, Reference Manual, GainsvilleGoogle Scholar
  24. 24.
    Katritzky AR, Lobanov VS, Karelson M (1995) CODESSA, Training Manual, GainsvilleGoogle Scholar
  25. 25.
    Minovski N (2009) DragCOD v1.0, laboratory for chemometrics. National Institute of Chemistry, LjubljanaGoogle Scholar
  26. 26.
    Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design. Wiley-VCH, Weinheim, p 380Google Scholar
  27. 27.
    Vracko M (2005) Kohonen artificial neural network and counter propagation neural network in molecular structure-toxicity studies. Curr Comp-Aid Drug Des 1: 73–78CrossRefGoogle Scholar
  28. 28.
    Vracko M, Bandelj V, Barbieri P, Benfenati E, Chaudhry Q, Cronin M, Devillers J, Gallegos A, Gini G, Gramatica P, Helma C, Mazzatorta P, Neagu D, Netzeva T, Pavan M, Patlewicz G, Randic M, Tsakovska I, Worth A (2006) Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study. SAR QSAR Environ Res 17: 265–284. doi: 10.1080/10659360600787650 PubMedCrossRefGoogle Scholar
  29. 29.
    Simon V, Gasteiger J, Zupan J (1993) A combined application of two different neural network types for prediction of chemical reactivity. J Am Chem Soc 115: 9148–9159CrossRefGoogle Scholar
  30. 30.
    Leonard JT, Roy K (2006) On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci 25: 235–251. doi: 10.1002/qsar.200510161 CrossRefGoogle Scholar
  31. 31.
    Valkova I, Vračko M, Basak SC (2004) Modeling of structure-mutagenicity relationships: counter propagation neural network approach using calculated structural descriptors. Anal Chim Acta 509: 179–186. doi: 10.1016/j.aca.2003.12.035 CrossRefGoogle Scholar
  32. 32.
    Kotnik M, Oblak M, Humljan J, Gobec S, Urleb U, Solmajer T (2004) Quantitative structure–activity relationships of Streptococcus pneumoniae MurD transition state analogue inhibitors. QSAR Comb Sci 23: 399–405. doi: 10.1002/qsar.200430875 CrossRefGoogle Scholar
  33. 33.
    Zupan J, Novic M, Gasteiger J (1995) Neural networks with counter propagation learning strategy used for modeling. Chemometr Intell Lab Systems 27: 175–187CrossRefGoogle Scholar
  34. 34.
    Novic M (2008) Artificial neural networks. In: Livingstone DS (eds) Methods and protocols. Humana Press, Totowa, pp 45–60Google Scholar
  35. 35.
    Mitscher LA (2005) Bacterial topolsomerase inhibitors: quinolone and pyridone antibacterial agents. Chem Rev 105: 559–592. doi: 10.1021/cr030101q PubMedCrossRefGoogle Scholar
  36. 36.
    Jacobs MR (2004) Fluoroquinolones as chemotherapeutics against mycobacterial infections. Curr Pharm Des 10: 3213–3220PubMedCrossRefGoogle Scholar
  37. 37.
    Mitscher LA, Ma ZK (2003) Structure-activity relationships of quinolones. In: Ronald AR, Low DE (eds) Fluoroquinolone antibiotics. Birkhaeuser, Basel, pp 11–48Google Scholar
  38. 38.
    Ledoussal B, Almstead J-IK, Gray JL, Hu EX, Roychoudhury S (2003) Discovery, structure-activity relationships and unique properties of non-fluorinated quinolones (NFQs). Curr Med Chem: Agents 2: 13–25. doi: 10.2174/1568012033354531 CrossRefGoogle Scholar
  39. 39.
    Peterson LR (2001) Quinolone molecular structure-activity relationships: What we have learned about improving antimicrobial activity. Clin Infect Dis 33: S180–S186PubMedCrossRefGoogle Scholar
  40. 40.
    Topliss JG, Edwards RP (1979) Chance factors in studies of quantitative structure-activity-relationships. J Med Chem 22: 1238–1244PubMedCrossRefGoogle Scholar
  41. 41.
    Smith JT (1984) Mutational resistance to 4-quinolone antibacterial agents. Eur J Clin Microbiol 3: 347–350PubMedCrossRefGoogle Scholar
  42. 42.
    Topliss JG (1983) In: Topliss JG (ed) Quantitative structure-activity relationships of drugs. Academic Press, New York, pp 497–504Google Scholar
  43. 43.
    Zupan J (2003) Neural networks. In: Gasteiger J (eds) Handbook of cheminformatics. Wiley-VCH, Weinheim, pp 1167–1215CrossRefGoogle Scholar
  44. 44.
    Vračko M, Mills D, Basak SC (2004) Structure-mutagenicity modelling using counter propagation neural networks. Environ Toxicol Pharmacol 16: 25–36. doi: 10.1016/j.etap.2003.09.004 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Nikola Minovski
    • 1
  • Marjan Vračko
    • 1
  • Tom Šolmajer
    • 1
  1. 1.National Institute of ChemistryLjubljanaSlovenia

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