Skip to main content

Advertisement

SpringerLink
Log in

Computational pharmacology of rifampin in mice: an application to dose optimization with conflicting objectives in tuberculosis treatment

  • Original Paper
  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

Dose selection for rifampin in the treatment of active pulmonary tuberculosis (TB) illustrates some of the challenges for dose optimization within multidrug therapies. Rifampin-based anti-TB regimens are often combined with antiretroviral therapies to treat human immunodeficiency virus (HIV) coinfection. The potent cytochrome P450 (CYP) enzyme inducing properties of rifampin give rise to significant drug-drug interactions, the minimization of which by limiting the dose, conflicts with the maximization of bacterial killing by increasing the dose. Such multiple and conflicting objectives lead to a set of trade-off optimal solutions for dose optimization rather than a single best solution. Here, we combine pharmacokinetic/pharmacodynamic (PK/PD) modeling with multiobjective optimization to quantitatively explore trade-offs between therapeutic and adverse effects of optimal dosing for the example of rifampin in TB-infected mice. The PK/PD model describes rifampin concentrations in plasma and liver following oral administration together with hepatic CYP enzyme induction and bacterial killing kinetics. We include optimization objectives descriptive of antimicrobial efficacy, CYP-mediated drug-drug interactions, and drug exposure-dependent toxicity. Results show non-conventional dosing scenarios that allow for increased efficacy relative to uniform dosing without increasing drug-drug interactions. Additionally, we find currently employed dosages for rifampin to be nearly optimal with respect to trade-offs between efficacy and toxicity. While limited by the accuracy and applicability of the PK/PD model, these results provide an avenue for experimental investigation of complex dose optimization problems. This method can be extended to include additional drugs and optimization objectives, and may provide a useful tool for individualized medicine.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Centers for Disease Control and Prevention (2003). Treatment of tuberculosis, American Thoracic Society, CDC, and Infectious Disease Society of America. MMWR Recomm Rep 52(RR-11):1–77

  2. Constans P, Baron A, Parrot R, Coury C (1972) A study of 200 cases of active, recent pulmonary tuberculosis treated with rifampin-isoniazid. A follow-up history of one and one-half to three years. Chest 61(6):539–549

    CAS  PubMed  Google Scholar 

  3. Raleigh JW (1972) Rifampin: clinical experience with a new anti-tuberculosis drug. Trans Am Clin Climatol Assoc 83:104–112

    CAS  PubMed Central  PubMed  Google Scholar 

  4. van Ingen J, Aarnoutse RE, Donald PR, Diacon AH, Dawson R, Gillespie SH, Boeree MJ (2011) Why do we use 600 mg of rifampicin in tuberculosis treatment? Clin Infect Dis 52(9):e194–199

    Article  PubMed  Google Scholar 

  5. Steingart KR, Jotblad S, Robsky K, Deck D, Hopewell PC, Huang D, Nahid P (2011) Higher-dose rifampin for the treatment of pulmonary tuberculosis: a systematic review. Int J Tuberc Lung Dis 15(3):305–316

    CAS  PubMed  Google Scholar 

  6. Harvard University Faculty of Medicine (2014) Trial of High-Dose Rifampin in Patients With TB (HIRIF), In: ClinicalTrials.gov [Internet], Bethesda (MD): National Library of Medicine (US). Available from: http://clinicaltrials.gov/ct2/show/NCT01408914Z, nLM Identifier: NCT01408914

  7. Radboud University (2013) Pharmacokinetics and pharmacodynamics of high versus standard dose rifampicin in patients with pulmonary tuberculosis (High RIF), In: ClinicalTrials.gov [Internet], Bethesda (MD): National Library of Medicine (US). Available from: http://clinicaltrials.gov/ct2/show/results/NCT00760149, nLM Identifier: NCT00760149

  8. Radboud University (2014) Safety, tolerability, extended early bactericidal activity and PK of higher doses rifampicin in adults with pulmonary TB (HR1) In: ClinicalTrials.gov [Internet], Bethesda (MD): National Library of Medicine (US). Available from: http://clinicaltrials.gov/ct2/show/results/NCT00760149, nLM Identifier: NCT00760149

  9. Marais BJ, Lonnroth K, Lawn SD, Migliori GB, Mwaba P, Glaziou P, Bates M, Colagiuri R, Zijenah L, Swaminathan S, Memish ZA, Pletschette M, Hoelscher M, Abubakar I, Hasan R, Zafar A, Pantaleo G, Craig G, Kim P, Maeurer M, Schito M, Zumla A (2013) Tuberculosis comorbidity with communicable and non-communicable diseases: integrating health services and control efforts. Lancet Infect Dis 13(5):436–448

    Article  PubMed  Google Scholar 

  10. Dooley KE, Flexner C, Andrade AS (2008) Drug interactions involving combination antiretroviral therapy and other anti-infective agents: repercussions for resource-limited countries. J Infect Dis 198(7):948–961

    Article  CAS  PubMed  Google Scholar 

  11. Dooley KE, Chaisson RE (2009) Tuberculosis and diabetes mellitus: convergence of two epidemics. Lancet Infect Dis 9(12):737–746

    Article  PubMed Central  PubMed  Google Scholar 

  12. Sousa M, Pozniak A, Boffito M (2008) Pharmacokinetics and pharmacodynamics of drug interactions involving rifampicin, rifabutin and antimalarial drugs. J Antimicrob Chemother 62(5):872–878

    Article  CAS  PubMed  Google Scholar 

  13. Baciewicz AM, Chrisman CR, Finch CK, Self TH (2013) Update on rifampin, rifabutin, and rifapentine drug interactions. Curr Med Res Opin 29(1):1–12

    Article  CAS  PubMed  Google Scholar 

  14. Centers for Disease Control and Prevention (2013) Managing drug interactions in the treatment of HIV-related tuberculosis. [online]. 2013. Available from: www.cdc.gov/tb/TB_HIV_Drugs/default.htm

  15. Tostmann A, Boeree MJ, Aarnoutse RE, de Lange WC, van der Ven AJ, Dekhuijzen R (2008) Antituberculosis drug-induced hepatotoxicity: concise up-to-date review. J Gastroenterol Hepatol 23(2):192–202

    Article  CAS  PubMed  Google Scholar 

  16. Satyaraddi A, Velpandian T, Sharma SK, Vishnubhatla S, Sharma A, Sirohiwal A, Makharia GK, Sinha S, Biswas A, Singh S (2014) Correlation of plasma anti-tuberculosis drug levels with subsequent development of hepatotoxicity. Int J Tuberc Lung Dis 18(2):188–195

    Article  CAS  PubMed  Google Scholar 

  17. Kwara A, Flanigan TP, Carter EJ (2005) Highly active antiretroviral therapy (HAART) in adults with tuberculosis: current status. Int J Tuberc Lung Dis 9(3):248–257

    CAS  PubMed  Google Scholar 

  18. Collette Y, Siarry P (2003) Multiobjective optimization: principles and case studies. Decision engineering. Springer, Berlin

    Google Scholar 

  19. Marler R, Arora J (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26(6):369–395

    Article  Google Scholar 

  20. Deb K et al (2001) Multi-objective optimization using evolutionary algorithms, vol 2012. John Wiley & Sons, Chichester

    Google Scholar 

  21. Raybon JJ, Pray D, Morgan DG, Zoeckler M, Zheng M, Sinz M, Kim S (2011) Pharmacokinetic-pharmacodynamic modeling of rifampicin-mediated Cyp3a11 induction in steroid and xenobiotic X receptor humanized mice. J Pharmacol Exp Ther 337(1):75–82

    Article  CAS  PubMed  Google Scholar 

  22. Vaddady PK, Lee RE, Meibohm B (2010) In vitro pharmacokinetic/pharmacodynamic models in anti-infective drug development: focus on TB. Future Med Chem 2(8):1355–1369

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Charles River Laboratories Inc (2006) Charles River Laboratories Research Models & Services. Available from: http://www.criver.com

  24. Jayaram R, Gaonkar S, Kaur P, Suresh BL, Mahesh BN, Jayashree R, Nandi V, Bharat S, Shandil RK, Kantharaj E, Balasubramanian V (2003) Pharmacokinetics-pharmacodynamics of rifampin in an aerosol infection model of tuberculosis. Antimicrob Agents Chemother 47(7):2118–2124

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Bauer B, Reynolds M (2008) Recovering data from scanned graphs: performance of Frantz’s g3data software. Behav Res Methods 40(3):858–868

    Article  PubMed  Google Scholar 

  26. Frantz J (2012) G3data, Version 1.5.2, Software, Available from: https://github.com/pn2200/g3data.git

  27. Gilks WR, Best N, Tan K (1995) Adaptive rejection Metropolis sampling within Gibbs sampling. Appl Stat 44:455–472

    Article  Google Scholar 

  28. Zitzler E, Laumanns M, Bleuler S (2004) A tutorial on evolutionary multiobjective optimization. In: Gandibleux X (ed) Metaheuristics for multiobjective optimisation. Springer, Berlin, pp 3–37

    Chapter  Google Scholar 

  29. Bois FY, Maszle DR (1997) MCSim: a Monte Carlo simulation program. J Stat Softw 2(i09):1–60

    Google Scholar 

  30. Hindmarsh AC (1983) ODEPACK, A Systematized Collection of ODE Solvers, R.S Stepleman et al. (eds.), North-Holland, Amsterdam, (vol. 1 of), pp. 55–64. IMACS transactions on scientific computation 1:55–64

  31. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  32. R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org/

  33. Plummer M, Best N, Cowles K, Vines K (2006) CODA: Convergence diagnosis and output analysis for MCMC. R news 6(1):7–11

    Google Scholar 

  34. Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian data analysis, 2nd edn. Chapman and Hall/CRC, Boca Raton

    Google Scholar 

  35. De Groote MA, Gilliland JC, Wells CL, Brooks EJ, Woolhiser LK, Gruppo V, Peloquin CA, Orme IM, Lenaerts AJ (2011) Comparative studies evaluating mouse models used for efficacy testing of experimental drugs against Mycobacterium tuberculosis. Antimicrob Agents Chemother 55(3):1237–1247

    Article  PubMed Central  PubMed  Google Scholar 

  36. Chang KC, Leung CC, Grosset J, Yew WW (2011) Treatment of tuberculosis and optimal dosing schedules. Thorax 66(11):997–1007

    Article  PubMed  Google Scholar 

  37. Mouton JW, Ambrose PG, Canton R, Drusano GL, Harbarth S, MacGowan A, Theuretzbacher U, Turnidge J (2011) Conserving antibiotics for the future: new ways to use old and new drugs from a pharmacokinetic and pharmacodynamic perspective. Drug Resist Updat 14(2):107–117

    Article  CAS  PubMed  Google Scholar 

  38. Drusano GL, Ambrose PG, Bhavnani SM, Bertino JS, Nafziger AN, Louie A (2007) Back to the future: using aminoglycosides again and how to dose them optimally. Clin Infect Dis 45(6):753–760

    Article  CAS  PubMed  Google Scholar 

  39. Ambrose PG, Bhavnani SM, Rubino CM, Louie A, Gumbo T, Forrest A, Drusano GL (2007) Pharmacokinetics-pharmacodynamics of antimicrobial therapy: it’s not just for mice anymore. Clin Infect Dis 44(1):79–86

    Article  CAS  PubMed  Google Scholar 

  40. Steuernagel O, Polani D (2010) Multiobjective optimization applied to the eradication of persistent pathogens. IEEE Trans Evol Comput 14(5):759–765

    Article  Google Scholar 

  41. Heris SMK, Khaloozadeh H (2011) Open-and closed-loop multiobjective optimal strategies for HIV therapy using NSGA-II. IEEE Trans Biomed Eng 58(6):1678–1685

    Article  PubMed  Google Scholar 

  42. Petrovski A, McCall J (2001) Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. In: Deb K, Goel T (eds) Evolutionary multi-criterion optimization. Springer, Berlin, pp 531–545

    Chapter  Google Scholar 

  43. Deb K, Mitra K, Dewri R, Majumdar S (2004) Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation. Chemical engineering science 59(20):4261–4277

    Article  CAS  Google Scholar 

  44. Dooley KE, Nuermberger EL, Diacon AH (2013) Pipeline of drugs for related diseases: tuberculosis. Curr Opin HIV AIDS 8(6):579–585

    Article  CAS  PubMed  Google Scholar 

  45. Martínez E, Collazos J, Mayo J (1999) Hypersensitivity reactions to rifampin: pathogenetic mechanisms, clinical manifestations, management strategies, and review of the anaphylactic-like reactions. Medicine 78(6):361–369

    Article  PubMed  Google Scholar 

  46. Zhang Y, Yew WW, Barer MR (2012) Targeting persisters for tuberculosis control. Antimicrob Agents Chemother 56(5):2223–2230

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Rosenthal IM, Tasneen R, Peloquin CA, Zhang M, Almeida D, Mdluli KE, Karakousis PC, Grosset JH, Nuermberger EL (2012) Dose-ranging comparison of rifampin and rifapentine in two pathologically distinct murine models of tuberculosis. Antimicrob Agents Chemother 56(8):4331–4340

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Katsube T, Yano Y, Wajima T, Yamano Y, Takano M (2010) Pharmacokinetic/pharmacodynamic modeling and simulation to determine effective dosage regimens for doripenem. J Pharm Sci 99(5):2483–2491

    CAS  PubMed  Google Scholar 

  49. Lesko LJ, Schmidt S (2012) Individualization of drug therapy: history, present state, and opportunities for the future. Clin Pharmacol Ther 92(4):458–466

    CAS  PubMed  Google Scholar 

  50. Zumla AI, Gillespie SH, Hoelscher M, Philips PP, Cole ST, Abubakar I, McHugh TD, Schito M, Maeurer M, Nunn AJ (2014) New antituberculosis drugs, regimens, and adjunct therapies: needs, advances, and future prospects. Lancet Infect Dis 14(4):327–340

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The author thanks Mary Ann De Groote and Scott Irwin (Colorado State University (CSU)) for helpful discussions, Raymond Yang and Anne Lenaerts (CSU) for helpful suggestions and review of the manuscript, Ole Steuernagel and Daniel Polani (University of Hertfordshire) for helpful suggestions regarding NSGA II, and Kenneth “KJ” Sullivan for helpful discussions on genetic algorithms. This work was supported by National Institutes of Health Grant Number K25AI089945.

Author information

Authors and Affiliations

  1. Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA

    Michael A. Lyons

Authors
  1. Michael A. Lyons
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael A. Lyons.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lyons, M.A. Computational pharmacology of rifampin in mice: an application to dose optimization with conflicting objectives in tuberculosis treatment. J Pharmacokinet Pharmacodyn 41, 613–623 (2014). https://doi.org/10.1007/s10928-014-9380-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-014-9380-2

Keywords

Search

Navigation