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Title: Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

Abstract

Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, fewmore » MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.« less

Authors:
 [1];  [2]; ORCiD logo [2];  [3]
+ Show Author Affiliations
  1. Univ. of Michigan, Ann Arbor, MI (United States); Purdue Univ., West Lafayette, IN (United States)
  2. Univ. of Michigan, Ann Arbor, MI (United States)
  3. Univ. Autònoma de Barcelona (Spain)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE
OSTI Identifier:
1544032
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 13; Journal Issue: 5; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Science & Technology; Other Topics

Citation Formats

Pienaar, Elsje, Linderman, Jennifer J., Kirschner, Denise E., and Cardona, Pere -Joan. Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. United States: N. p., 2018. Web. doi:10.1371/journal.pone.0196322.
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Pienaar, Elsje, Linderman, Jennifer J., Kirschner, Denise E., & Cardona, Pere -Joan. Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. United States. https://doi.org/10.1371/journal.pone.0196322
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Pienaar, Elsje, Linderman, Jennifer J., Kirschner, Denise E., and Cardona, Pere -Joan. Thu . "Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas". United States. https://doi.org/10.1371/journal.pone.0196322. https://www.osti.gov/servlets/purl/1544032.
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@article{osti_1544032,
title = {Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas},
author = {Pienaar, Elsje and Linderman, Jennifer J. and Kirschner, Denise E. and Cardona, Pere -Joan},
abstractNote = {Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.},
doi = {10.1371/journal.pone.0196322},
journal = {PLoS ONE},
number = 5,
volume = 13,
place = {United States},
year = {Thu May 10 00:00:00 EDT 2018},
month = {Thu May 10 00:00:00 EDT 2018}
}
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https://doi.org/10.1371/journal.pone.0196322
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Figures / Tables:

Fig 1 Fig 1: Antibiotic resistance emergence and selection over bacterial through population scales. Resistance develops upon mutation conferring genetic resistance (A). Resistant bacteria grow (B) within the host, giving rise to hetero-resistance (C), which can spread resistant bacteria to others (D). Improper treatment (E) selects for resistant bacteria, resulting in drug-resistantmore » TB (F), which can spread in a community. (G) The influence of bacterial and drug dynamics on resistance emergence and selection in the context of granulomas remains unclear. Arrows between scales represent a series of potential events, but influences can occur in both directions.« less
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    • DOI: 10.3389/fphar.2020.00333
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