Predicting the Outcomes of New Short-Course Regimens for Multidrug-Resistant Tuberculosis Using Intrahost and Pharmacokinetic-Pharmacodynamic Modeling

Tan N Doan; Pengxing Cao; Theophilus I Emeto; James M McCaw; Emma S McBryde

doi:10.1128/AAC.01487-18

Predicting the Outcomes of New Short-Course Regimens for Multidrug-Resistant Tuberculosis Using Intrahost and Pharmacokinetic-Pharmacodynamic Modeling

Antimicrob Agents Chemother. 2018 Nov 26;62(12):e01487-18. doi: 10.1128/AAC.01487-18. Print 2018 Dec.

Authors

Tan N Doan^{1

2

3}, Pengxing Cao⁴, Theophilus I Emeto^{2

5}, James M McCaw^{6

7

8

9}, Emma S McBryde^{10

2}

Affiliations

¹ Department of Medicine at The Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia [email protected] [email protected].
² Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia.
³ Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
⁴ School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia [email protected] [email protected].
⁵ College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia.
⁶ School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia.
⁷ Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
⁸ Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia.
⁹ Peter Doherty Institute for Infection and Immunity, Royal Melbourne Hospital and University of Melbourne, Melbourne, Victoria, Australia.
¹⁰ Department of Medicine at The Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia.

Abstract

Short-course regimens for multidrug-resistant tuberculosis (MDR-TB) are urgently needed. Limited data suggest that the new drug bedaquiline (BDQ) may have the potential to shorten MDR-TB treatment to less than 6 months when used in conjunction with standard anti-TB drugs. However, the feasibility of BDQ in shortening MDR-TB treatment duration remains to be established. Mathematical modeling provides a platform to investigate different treatment regimens and predict their efficacy. We developed a mathematical model to capture the immune response to TB inside a human host environment. This model was then combined with a pharmacokinetic-pharmacodynamic model to simulate various short-course BDQ-containing regimens. Our modeling suggests that BDQ could reduce MDR-TB treatment duration to just 18 weeks (4 months) while still maintaining a very high treatment success rate (100% for daily BDQ for 2 weeks, or 95% for daily BDQ for 1 week during the intensive phase). The estimated time to bacterial clearance of these regimens ranges from 27 to 33 days. Our findings provide the justification for empirical evaluation of short-course BDQ-containing regimens. If short-course BDQ-containing regimens are found to improve outcomes, then we anticipate clear cost savings and a subsequent improvement in the efficiency of national TB programs.

Keywords: bedaquiline; mathematical modeling; multidrug resistance; short-course regimen; tuberculosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Antitubercular Agents / pharmacokinetics
Antitubercular Agents / pharmacology*
Clofazimine / pharmacokinetics
Clofazimine / pharmacology
Colony Count, Microbial
Computer Simulation
Diarylquinolines / pharmacokinetics
Diarylquinolines / pharmacology*
Dose-Response Relationship, Drug
Drug Dosage Calculations
Drug Resistance, Bacterial / genetics
Drug Therapy, Combination
Ethambutol / pharmacokinetics
Ethambutol / pharmacology
Host-Pathogen Interactions / drug effects*
Host-Pathogen Interactions / immunology
Humans
Immunity, Innate
Isoniazid / pharmacokinetics
Isoniazid / pharmacology
Kanamycin / pharmacokinetics
Kanamycin / pharmacology
Macrophages / drug effects*
Macrophages / immunology
Macrophages / microbiology
Microbial Sensitivity Tests
Models, Statistical*
Moxifloxacin / pharmacokinetics
Moxifloxacin / pharmacology
Mycobacterium tuberculosis / drug effects*
Mycobacterium tuberculosis / genetics
Mycobacterium tuberculosis / growth & development
Mycobacterium tuberculosis / immunology
Ofloxacin / pharmacokinetics
Ofloxacin / pharmacology
Prothionamide / pharmacokinetics
Prothionamide / pharmacology
Pyrazinamide / pharmacokinetics
Pyrazinamide / pharmacology
Time Factors
Tuberculosis, Multidrug-Resistant / drug therapy
Tuberculosis, Multidrug-Resistant / immunology
Tuberculosis, Multidrug-Resistant / microbiology

Substances

Antitubercular Agents
Diarylquinolines
Pyrazinamide
Kanamycin
Prothionamide
bedaquiline
Ethambutol
Ofloxacin
Clofazimine
Moxifloxacin
Isoniazid