Tuberculosis drugs' distribution and emergence of resistance in patient's lung lesions: A mechanistic model and tool for regimen and dose optimization
Natasha Strydom, Sneha V Gupta, William S Fox, Laura E Via, Hyeeun Bang, Myungsun Lee, Seokyong Eum, TaeSun Shim, Clifton E Barry 3rd, Matthew Zimmerman, Véronique Dartois, Radojka M Savic, Natasha Strydom, Sneha V Gupta, William S Fox, Laura E Via, Hyeeun Bang, Myungsun Lee, Seokyong Eum, TaeSun Shim, Clifton E Barry 3rd, Matthew Zimmerman, Véronique Dartois, Radojka M Savic
Abstract
Background: The sites of mycobacterial infection in the lungs of tuberculosis (TB) patients have complex structures and poor vascularization, which obstructs drug distribution to these hard-to-reach and hard-to-treat disease sites, further leading to suboptimal drug concentrations, resulting in compromised TB treatment response and resistance development. Quantifying lesion-specific drug uptake and pharmacokinetics (PKs) in TB patients is necessary to optimize treatment regimens at all infection sites, to identify patients at risk, to improve existing regimens, and to advance development of novel regimens. Using drug-level data in plasma and from 9 distinct pulmonary lesion types (vascular, avascular, and mixed) obtained from 15 hard-to-treat TB patients who failed TB treatments and therefore underwent lung resection surgery, we quantified the distribution and the penetration of 7 major TB drugs at these sites, and we provide novel tools for treatment optimization.
Methods and findings: A total of 329 plasma- and 1,362 tissue-specific drug concentrations from 9 distinct lung lesion types were obtained according to optimal PK sampling schema from 15 patients (10 men, 5 women, aged 23 to 58) undergoing lung resection surgery (clinical study NCT00816426 performed in South Korea between 9 June 2010 and 24 June 2014). Seven major TB drugs (rifampin [RIF], isoniazid [INH], linezolid [LZD], moxifloxacin [MFX], clofazimine [CFZ], pyrazinamide [PZA], and kanamycin [KAN]) were quantified. We developed and evaluated a site-of-action mechanistic PK model using nonlinear mixed effects methodology. We quantified population- and patient-specific lesion/plasma ratios (RPLs), dynamics, and variability of drug uptake into each lesion for each drug. CFZ and MFX had higher drug exposures in lesions compared to plasma (median RPL 2.37, range across lesions 1.26-22.03); RIF, PZA, and LZD showed moderate yet suboptimal lesion penetration (median RPL 0.61, range 0.21-2.4), while INH and KAN showed poor tissue penetration (median RPL 0.4, range 0.03-0.73). Stochastic PK/pharmacodynamic (PD) simulations were carried out to evaluate current regimen combinations and dosing guidelines in distinct patient strata. Patients receiving standard doses of RIF and INH, who are of the lower range of exposure distribution, spent substantial periods (>12 h/d) below effective concentrations in hard-to-treat lesions, such as caseous lesions and cavities. Standard doses of INH (300 mg) and KAN (1,000 mg) did not reach therapeutic thresholds in most lesions for a majority of the population. Drugs and doses that did reach target exposure in most subjects include 400 mg MFX and 100 mg CFZ. Patients with cavitary lesions, irrespective of drug choice, have an increased likelihood of subtherapeutic concentrations, leading to a higher risk of resistance acquisition while on treatment. A limitation of this study was the small sample size of 15 patients, performed in a unique study population of TB patients who failed treatment and underwent lung resection surgery. These results still need further exploration and validation in larger and more diverse cohorts.
Conclusions: Our results suggest that the ability to reach and maintain therapeutic concentrations is both lesion and drug specific, indicating that stratifying patients based on disease extent, lesion types, and individual drug-susceptibility profiles may eventually be useful for guiding the selection of patient-tailored drug regimens and may lead to improved TB treatment outcomes. We provide a web-based tool to further explore this model and results at http://saviclab.org/tb-lesion/.
Conflict of interest statement
The authors have declared that no competing interests exist.
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References
- WHO | Report of the Technical Consultation on Advances in Clinical Trial Design for Development of New TB Treatments. WHO; World Health Organization; 2018; Available from: (Accessed December 21, 2018)
- Jindani A, Harrison TS, Nunn AJ, Phillips PPJ, Churchyard GJ, Charalambous S, et al. High-Dose Rifapentine with Moxifloxacin for Pulmonary Tuberculosis. N Engl J Med. Massachusetts Medical Society; 2014;371: 1599–1608. 10.1056/NEJMoa1314210
- Gillespie SH, Crook AM, McHugh TD, Mendel CM, Meredith SK, Murray SR, et al. Four-Month Moxifloxacin-Based Regimens for Drug-Sensitive Tuberculosis. N Engl J Med. Massachusetts Medical Society; 2014;371: 1577–1587. 10.1056/NEJMoa1407426
- Merle CS, Fielding K, Sow OB, Gninafon M, Lo MB, Mthiyane T, et al. A Four-Month Gatifloxacin-Containing Regimen for Treating Tuberculosis. N Engl J Med. Massachusetts Medical Society; 2014;371: 1588–1598. 10.1056/NEJMoa1315817
- Barry CE, Boshoff HI, Dartois V, Dick T, Ehrt S, Flynn J, et al. The spectrum of latent tuberculosis: rethinking the biology and intervention strategies. Nat Rev Microbiol. Nature Publishing Group; 2009;7: 845 10.1038/nrmicro2236
- Dartois V. The path of anti-tuberculosis drugs: from blood to lesions to mycobacterial cells. Nat Rev Microbiol. Nature Publishing Group; 2014;12: 159–67. 10.1038/nrmicro3200
- Prideaux B, Via LE, Zimmerman MD, Eum S, Sarathy J, O’Brien P, et al. The association between sterilizing activity and drug distribution into tuberculosis lesions. Nat Med. 2015;21: 1223–7. 10.1038/nm.3937
- Pienaar E, Dartois V, Linderman JJ, Kirschner DE. In silico evaluation and exploration of antibiotic tuberculosis treatment regimens. BMC Syst Biol. 2015;9: 79 10.1186/s12918-015-0221-8
- Rifat D, Prideaux B, Savic RM, Urbanowski ME, Parsons TL, Luna B, et al. Pharmacokinetics of rifapentine and rifampin in a rabbit model of tuberculosis and correlation with clinical trial data. Sci Transl Med. American Association for the Advancement of Science; 2018;10: eaai7786 10.1126/scitranslmed.aai7786
- Savic R, Weiner M, MacKenzie W, Engle M, Whitworth W, Johnson J, et al. Defining the optimal dose of rifapentine for pulmonary tuberculosis: Exposure-response relations from two phase II clinical trials. Clin Pharmacol Ther. Wiley-Blackwell; 2017;102: 321–331. 10.1002/cpt.634
- Mould DR, Upton RN. Basic concepts in population modeling, simulation, and model-based drug development-part 2: introduction to pharmacokinetic modeling methods. CPT pharmacometrics Syst Pharmacol. 2013;2: e38 10.1038/psp.2013.14
- Savic RM, Jonker DM, Kerbusch T, Karlsson MO. Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies. J Pharmacokinet Pharmacodyn. 2007;34: 711–26. 10.1007/s10928-007-9066-0
- Kjellsson MC, Via LE, Goh A, Weiner D, Low KM, Kern S, et al. Pharmacokinetic evaluation of the penetration of antituberculosis agents in rabbit pulmonary lesions. Antimicrob Agents Chemother. 2012;56: 446–457. 10.1128/AAC.05208-11
- Guiastrennec B, Hooker AC, Olofsson A, Ueckert S, Keizer R, Karlsson MO. xpose: Diagnostics for Pharmacometric Models [Internet]. 2017. Available from: (Accessed March 18, 2019)
- Gumbo T. New susceptibility breakpoints for first-line antituberculosis drugs based on antimicrobial pharmacokinetic/pharmacodynamic science and population pharmacokinetic variability. Antimicrob Agents Chemother. American Society for Microbiology; 2010;54: 1484–91. 10.1128/AAC.01474-09
- EUCAST. EUCAST. In: EUCAST [Internet]. 2015. Available from: (Accessed March 18, 2019)
- Lavielle M. mlxR: Simulation of Longitudinal Data [Internet]. 2017. Available from: (Accessed March 18, 2019)
- R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria; 2017. Available from: (Accessed March 18, 2019)
- RStudio Team. RStudio: Integrated Development Environment for R [Internet]. Boston, MA; 2015. Available from: (Accessed March 18, 2019)
- Chang W, Cheng J, Allaire JJ, Xie Y, McPherson J. shiny: Web Application Framework for R [Internet]. 2017. Available from: (Accessed March 18, 2019)
- Werely CJ, Donald PR, van Helden PD. NAT2 polymorphisms and their influence on the pharmacology and toxicity of isoniazid in TB patients. Per Med. Future Medicine Ltd London, UK; 2007;4: 123–131. 10.2217/17410541.4.2.123
- Sarathy JP, Via LE, Weiner D, Blanc L, Boshoff H, Eugenin EA, et al. Extreme Drug Tolerance of Mycobacterium tuberculosis in Caseum. Antimicrob Agents Chemother. American Society for Microbiology; 2018;62: e02266–17. 10.1128/AAC.02266-17
- Lanoix J-P, Ioerger T, Ormond A, Kaya F, Sacchettini J, Dartois V, et al. Selective Inactivity of Pyrazinamide against Tuberculosis in C3HeB/FeJ Mice Is Best Explained by Neutral pH of Caseum. Antimicrob Agents Chemother. American Society for Microbiology (ASM); 2016;60: 735–43. 10.1128/AAC.01370-15
- Irwin SM, Gruppo V, Brooks E, Gilliland J, Scherman M, Reichlen MJ, et al. Limited Activity of Clofazimine as a Single Drug in a Mouse Model of Tuberculosis Exhibiting Caseous Necrotic Granulomas. Antimicrob Agents Chemother. 2014;58: 4026–4034. 10.1128/AAC.02565-14
- Jayaram R, Shandil RK, Gaonkar S, Kaur P, Suresh BL, Mahesh BN, et al. Isoniazid pharmacokinetics-pharmacodynamics in an aerosol infection model of tuberculosis. Antimicrob Agents Chemother. 2004;48: 2951–2957. 10.1128/AAC.48.8.2951-2957.2004
- Lakshminarayana SB, Huat TB, Ho PC, Manjunatha UH, Dartois V, Dick T, et al. Comprehensive physicochemical, pharmacokinetic and activity profiling of anti-TB agents. J Antimicrob Chemother. 2015;70: 857–67. 10.1093/jac/dku457
- Kashuba AD, Nafziger AN, Drusano GL, Bertino JS. Optimizing aminoglycoside therapy for nosocomial pneumonia caused by gram-negative bacteria. Antimicrob Agents Chemother. American Society for Microbiology; 1999;43: 623–9. Available from: (Accessed March 18, 2019)
- Boak LM, Rayner CR, Grayson ML, Paterson DL, Spelman D, Khumra S, et al. Clinical Population Pharmacokinetics and Toxicodynamics of Linezolid. Antimicrob Agents Chemother. 2014;58: 2334–2343. 10.1128/AAC.01885-13
- Sarathy JP, Via LE, Weiner D, Blanc L, Boshoff H, Eugenin EA, et al. Extreme Drug Tolerance of Mycobacterium tuberculosis in Caseum. Antimicrob Agents Chemother. American Society for Microbiology; 2018;62: e02266–17. 10.1128/AAC.02266-17
- Heinrichs MT, Vashakidze S, Nikolaishvili K, Sabulua I, Tukvadze N, Bablishvili N, et al. Moxifloxacin target site concentrations in patients with pulmonary TB utilizing microdialysis: a clinical pharmacokinetic study. J Antimicrob Chemother. 2018;73: 477–483. 10.1093/jac/dkx421
- Jindani A, Harrison TS, Nunn AJ, Phillips PPJ, Churchyard GJ, Charalambous S, et al. High-Dose Rifapentine with Moxifloxacin for Pulmonary Tuberculosis. N Engl J Med. Massachusetts Medical Society; 2014;371: 1599–1608. 10.1056/NEJMoa1314210
- Gillespie SH, Crook AM, McHugh TD, Mendel CM, Meredith SK, Murray SR, et al. Four-Month Moxifloxacin-Based Regimens for Drug-Sensitive Tuberculosis. N Engl J Med. Massachusetts Medical Society; 2014;371: 1577–1587. 10.1056/NEJMoa1407426
- Drusano GL, Sgambati N, Eichas A, Brown DL, Kulawy R, Louie A. The Combination of Rifampin plus Moxifloxacin Is Synergistic for Suppression of Resistance but Antagonistic for Cell Kill of Mycobacterium tuberculosis as Determined in a Hollow-Fiber Infection Model. MBio. 2010;1: e00139-10–e00139-10. 10.1128/mBio.00139-10
- Dooley K, Flexner C, Hackman J, Peloquin CA, Nuermberger E, Chaisson RE, et al. Repeated Administration of High-Dose Intermittent Rifapentine Reduces Rifapentine and Moxifloxacin Plasma Concentrations. Antimicrob Agents Chemother. 2008;52: 4037–4042. 10.1128/AAC.00554-08
- Reuter A, Tisile P, von Delft D, Cox H, Cox V, Ditiu L, et al. The devil we know: is the use of injectable agents for the treatment of MDR-TB justified? Int J Tuberc Lung Dis. 2017;21: 1114–1126. 10.5588/ijtld.17.0468
- WHO | Rapid Communication: Key changes to treatment of multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB). WHO; World Health Organization; 2018; Available from: (Accessed March 18, 2019)
- Conradie F, Diacon AH, Everitt D, Mendel CM, van Niekerk C, Howell P, et al. Sustained high rate of successful treatment outcomes: interim results of 75 patients in the Nix-TB clinical study of pretomanid, bedaquiline and linezolid. Oral presentation at 49th World Conference on lung health of the International Union against tuberculosis and lung disease (The Union) October 25, 2018, The Hague, The Netherlands. The Hague; 2018. p. S69. Available from: (Accessed March 18, 2019)
- Colangeli R, Jedrey H, Kim S, Connell R, Ma S, Chippada Venkata UD, et al. Bacterial Factors That Predict Relapse after Tuberculosis Therapy. N Engl J Med. 2018;379: 823–833. 10.1056/NEJMoa1715849
- Imperial MZ, Nahid P, Phillips PPJ, Davies GR, Fielding K, Hanna D, et al. A patient-level pooled analysis of treatment-shortening regimens for drug-susceptible pulmonary tuberculosis. Nat Med. Nature Publishing Group; 2018;24: 1708–1715. 10.1038/s41591-018-0224-2
- Huang Q, Yin Y, Kuai S, Yan Y, Liu J, Zhang Y, et al. The value of initial cavitation to predict re-treatment with pulmonary tuberculosis. Eur J Med Res. BioMed Central; 2016;21: 20 10.1186/s40001-016-0214-0
- Kaplan G, Post FA, Moreira AL, Wainwright H, Kreiswirth BN, Tanverdi M, et al. Mycobacterium tuberculosis growth at the cavity surface: a microenvironment with failed immunity. Infect Immun. 2003;71: 7099–108. Available from: (Accessed March 18, 2019) 10.1128/IAI.71.12.7099-7108.2003
- Kempker RR, Rabin AS, Nikolaishvili K, Kalandadze I, Gogishvili S, Blumberg HM, et al. Additional drug resistance in Mycobacterium tuberculosis isolates from resected cavities among patients with multidrug-resistant or extensively drug-resistant pulmonary tuberculosis. Clin Infect Dis. 2012;54: e51–4. 10.1093/cid/cir904
- Blanc L, Daudelin IB, Podell BK, Chen P-Y, Zimmerman M, Martinot AJ, et al. High-resolution mapping of fluoroquinolones in TB rabbit lesions reveals specific distribution in immune cell types. Elife. 2018;7 10.7554/eLife.41115
- Irwin SM, Prideaux B, Lyon ER, Zimmerman MD, Brooks EJ, Schrupp CA, et al. Bedaquiline and Pyrazinamide Treatment Responses Are Affected by Pulmonary Lesion Heterogeneity in Mycobacterium tuberculosis Infected C3HeB/FeJ Mice. ACS Infect Dis. 2016;2: 251–267. 10.1021/acsinfecdis.5b00127
- Sarathy JP, Zuccotto F, Hsinpin H, Sandberg L, Via LE, Marriner GA, et al. Prediction of Drug Penetration in Tuberculosis Lesions. ACS Infect Dis. NIH Public Access; 2016;2: 552–63. 10.1021/acsinfecdis.6b00051
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