Skip to main content

Advertisement

Log in

Modeling basic aspects of bacterial resistance of Mycobacterium tuberculosis to antibiotics

  • Published:
Ricerche di Matematica Aims and scope Submit manuscript

Abstract

In this work we study the effect of antibiotics treatment and the immune system in the development of Mycobacterium tuberculosis infection through the formulation and analysis of a nonlinear system of ordinary differential equations. The results are given in terms of forward and backward bifurcation and they reveal the importance of combining a suitable treatment with a good stimulation of the immune system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Billington, O.J., Mchgh, T.D., Gullespie, S.H.: Physiological cost of rifampicin resistance induced in vitro in Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 43, 1866–1869 (1999)

    Google Scholar 

  2. Borrell, S., Gagneux, S.: Infectiousness, reproductive fitness and evolution of drug-resistant Mycobacterium tuberculosis. Int. J. Tuberc. Lung Dis. 13(12), 1456–1466 (2009)

    Google Scholar 

  3. Byrd, T.F., Green, G.M., Fowlston, S.E., Lyons, C.R.: Differential growth characteristics and streptomycin susceptibility of virulent and avirulent Mycobacterium tuberculosis strains in a novel fibroblast-mycobacterium microcolony assay. Infect. Immun. 66, 5132–5139 (1998)

    Google Scholar 

  4. Carpenter, S.M., Nunes-Alves, C., Booty, M.G., Way, S.S., Behar, S.M.: A higher activation threshold of memory CD\(8^+\) T cells has a fitness cost that is modified by TCR affinity during tuberculosis. PLoS Pathog. 12(1), 1–31 (2016). https://doi.org/10.1371/journal.ppat.1005380

    Article  Google Scholar 

  5. Cohen, T., Murray, M.: Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness. Nat. Med. 10(10), 1117–1121 (2004)

    Article  Google Scholar 

  6. Cohen, T., Sommers, B., Murray, M.: The effect of drug resistance on the fitness of Mycobacterium tuberculosis. Lancet. Infect. Dis 3, 13–21 (2003)

    Article  Google Scholar 

  7. Cooper, A.M.: Cell mediated immune responses in tuberculosis. Annu. Rev. Immunol. 27, 393–422 (2009)

    Article  Google Scholar 

  8. Dye, C., Williams, B.G.: Criteria for the control of drug-resistant tuberculosis. Proc. Natl. Acad. Sci. USA 97, 8180–8185 (2000)

    Article  Google Scholar 

  9. Dye, C., Espinal, M.A.: Will tuberculosis become resistant to all antibiotics? Proc. R. Soc. Lond. B 268, 45–52 (1462)

  10. Dye, Ch.: Doomsday postponed? Preventing and reversing epidemics of drug-resistant tuberculosis. Nat. Rev. Microbiol. 7, 81–87 (2009)

    Article  Google Scholar 

  11. Flynn, J.L., Chan, J.: Immunology of tuberculosis. Annu. Rev. Immunol. 19, 93–129 (2001)

    Article  Google Scholar 

  12. Gumbo, T., Louie, A., Deziel, M.R., Parsons, L.M., Salfinger, M., Drusano, G.L.: Selection of a moxifloxacin dose that suppresses drug resistance in Mycobacterium tuberculosis, by use of an in vitro pharmacodynamic infection model and mathematical modeling. J. Infect. Dis. 190(9), 1642–1651 (2004)

    Article  Google Scholar 

  13. Hoal-Van Helden, E.G., Hon, D., Lewis, L.A., Beyers, N., Van Helden, P.D.: Mycobacterial growth in human macrophages: variation according to donor, inoculum and bacterial strain. Cell Biol. Int. 25, 71–81 (2001)

    Article  Google Scholar 

  14. Hu, Y., Pertinez, H., Ortega-Muro, F., Alameda-Martin, L., Liu, Y., Schipani, A., Davies, G., Coates, A.: Investigation of elimination rate, persistent subpopulation removal, and relapse rates of Mycobacterium tuberculosis by using combinations of first-line drugs in a modified cornell mouse model. Antimicrob. Agents Chemother. 60(8), 4778–4785 (2016)

    Article  Google Scholar 

  15. Ibargüen-Mondragón, E., Esteva, L., Chávez-Galán, L.: A mathematical model for cellular immunology of tuberculosis. J. Math. Biosci. Eng. 8, 976–986 (2011)

    MathSciNet  MATH  Google Scholar 

  16. Ibargüen-Mondragón, E., Esteva, L.: On the interactions of sensitive and resistant Mycobacterium tuberculosis to antibiotics. Math. Biosci. 246(1), 84–93 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Ibargüen-Mondragón, E., Esteva, L.: A mathematical model on Mycobacterium tuberculosis dynamics into the granuloma. Rev. Colomb. Mat. 46(1), 39–65 (2012)

    MATH  Google Scholar 

  18. Ibargüen-Mondragón, E., Mosquera, S., Cerón, M., Burbano-Rosero, E.M., Hidalgo-Bonilla, S.P., Esteva, L., Romero-Leitón, J.P.: Mathematical modelling on bacterial resistance to multiple antibiotics caused by spontaneous mutations. BioSystems 117, 60–67 (2014)

    Article  Google Scholar 

  19. Ibargüen-Mondragón, E., Esteva, L.: On CTL response against Mycobacterium tuberculosis. Appl. Math. Sci. 8(48), 2383–2389 (2014)

    Google Scholar 

  20. Ibargüen-Mondragón, E., Romero-Leitón, J.P., Esteva, L., Burbano-Rosero, E.M.: Mathematical modeling of bacterial resistance to antibiotics by mutations and plasmids. J. Biol. Syst. 24(1), 1–18 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  21. Li, Q., Whalen, C.C., Albert, J.M., Larkin, R., Zukowsy, L., Cave, M.D., Silver, R.F.: Differences in rate and variability of intracellular growth of a panel of Mycobacterium tuberculosis clinical isolates within monocyte model. Infect. Immun. 70, 6489–6493 (2002)

    Article  Google Scholar 

  22. Luciani, P., Sisson, S.A., Jiang, H., Francis, A.R., Tanaka, M.M.: The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis. PNAS 106(34), 14711–14715 (2009)

    Article  Google Scholar 

  23. Marino, S., Hogue, I.B., Ray, C.J., Kirschner, D.E.: A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008)

    Article  MathSciNet  Google Scholar 

  24. Melnyk, A.H., Wong, A., Kassen, R.: The fitness costs of antibiotic resistance mutations. Evol. Appl. 8, 273–283 (2015)

    Article  Google Scholar 

  25. Mitchinson, G.A.: Mechanism of drug action in short course chemotherapy. Bull. Int. Union Tuberc. 65, 30–40 (1985)

    Google Scholar 

  26. Naidoo, C.C., Pillay, M.: Increased in vitro fitness of multi- and extensively drug-resistant F15/ LAM4/KZN strains of Mycobacterium tuberculosis. Clin. Microbiol. Infect. 20(6), 361–369 (2014)

    Article  Google Scholar 

  27. Nowak, M.A., May, R.M.: Virus Dynamics. Oxford University Press, New York (2000)

    MATH  Google Scholar 

  28. Ozcaglar, C., Shabbeer, A., Vandenberg, S.L., Yener, B., Bennett, K.P., Zhang, Y., Dhandayuthapani, S., Deretic, V.: Epidemiological models of Mycobacterium tuberculosis complex infections. Math. Biosci. 236(2), 77–96 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  29. Palomino, J.C., Leao, S., Ritacco, V.: Tuberculosis 2007; From Basic Science to Patient Care. Tuberculosistextbook.com, 1st edn, Brazil (2007)

  30. Romero, J., Ibargüen, E., Esteva, L.: Un modelo matemático sobre bacterias sensibles y resistentes a antibióticos. Matemáticas: enseanza universitaria 20(1), 55–73 (2011)

    MATH  Google Scholar 

  31. Romero, J., Ibargüen, E.: Sobre la resistencia bacteriana hacia antibióticos de acción bactericida y bacteriostática. Rev. Integr. 32(1), 101–116 (2014)

    MathSciNet  Google Scholar 

  32. Saltelli, A., Ratto, M., Tarantola, S., Campolongo, F.: Sensitivity analysis for chemical models. Chem. Rev. 105, 2811–2828 (2005)

    Article  MATH  Google Scholar 

  33. Shimao, T.: Drug resistance in tuberculosis control. Tubercle 68, 5–18 (1987)

    Article  Google Scholar 

  34. Sotto, A., Lavigne, J.P.: A mathematical model to guide antibiotic treatment strategies. BMC Med. Comment. 10, 1–3 (2012)

    Article  Google Scholar 

  35. Sud, D., Bigbee, C., Flynn, J.L., Kirschner, D.: Contribution of CD8+ T cells to control of Mycobacterium tuberculosis infection. J Immunol. 176(7), 4296–314 (2006)

    Article  Google Scholar 

  36. Sun, H.R., Lu, X., Ruan, S.: Qualitative analysis of models with different treatment protocols to prevent antibiotic resistance. Math. Biosci. 227, 56–67 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  37. Thieme, H.: Convergence results and a Poincaré–Bendixon trichotomy for asymptotically autonomous differential equations. J. Math. Biol. 30, 755–763 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  38. Vademecum.com Colombia. (s.f.): Recovered 18 Nov 2013. www.vademecum.com/co/

  39. Werngren, J., Hoffner, S.E.: Drug-susceptible Mycobacterium tuberculosis Beijing genotype does not develop mutation-conferred resistance to rifampin at an elevated rate. J. Clin. Microbiol. 41, 1520–1524 (2003)

    Article  Google Scholar 

  40. Wiesch, P.A., Kouyos, R., Engelstdter, J., Regoes, R.: Population biological principles of drug-resistance evolution in infectious diseases. Lancet Infect. Dis. 11(3), 236–247 (2011)

    Article  Google Scholar 

  41. Wigginton, E.J., Kirschner, D.: A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis. Immunology 166, 1951–1967 (2001)

    Article  Google Scholar 

  42. World Health Organization (WHO): Global tuberculosis report 2016. http://www.who.int/tb/publications/global_report/gtbr2016_executive_summary.pdf. Accessed 25 Jan 2017

  43. Zhang, Y.: Mechanisms of drug resistance in Mycobacterium tuberculosis. Int. J. Tuberc. Lung Dis. 13(11), 1320–1330 (2009)

    Google Scholar 

  44. Zhang, M., Gong, J., Yang, Z., Samtem, B., Cave, M.D., Barnes, P.F.: Enhanced capacity of a widespread strain of Mycobacterium tuberculosis to grow in human monocytes. J. Infect. Dis. 179, 1213–1217 (1999)

    Article  Google Scholar 

  45. Zhang, Y., Dhandayuthapani, S., Deretic, V.: Molecular basis for the exquisite sensitivity of Mycobacterium tuberculosis to isoniazid. Proc. Natl. Acad. Sci. USA 93(23), 13212–13216 (1996)

    Article  Google Scholar 

Download references

Acknowledgements

L. Esteva was supported by Grant IN113716 from PAPIIT-UNAM. E. Ibarguen acknowledges support from Project No. 182-01/11/2016 (VIPRI-UDENAR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. Esteva.

Additional information

This article belongs to the Special Issue: Demographic and temporal heterogeneity in infectious disease epidemiology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Esteva, L., Ibargüen-Mondragón, E. Modeling basic aspects of bacterial resistance of Mycobacterium tuberculosis to antibiotics. Ricerche mat 67, 69–88 (2018). https://doi.org/10.1007/s11587-017-0347-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11587-017-0347-7

Keywords

Mathematics Subject Classification

Navigation