1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Pharmacology and Toxicology
  4. Volume 61, 2021
  5. Article

Annual Review of Pharmacology and Toxicology

Volume 61, 2021

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Abstract

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Keyword(s): antituberculosis agents, drug development, modeling, pharmacokinetics-pharmacodynamics, simulation, translational science, tuberculosis
Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-030920-011143
2021-01-06
2024-04-28
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/61/1/annurev-pharmtox-030920-011143.html?itemId=/content/journals/10.1146/annurev-pharmtox-030920-011143&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    WHO (World Health Organ.). 2019. Global turberculosis report: executive summary Rep WHO, Geneva:
  2. 2. 
    Jindani A, Harrison TS, Nunn AJ, Phillips PP, Churchyard GJ et al. 2014. High-dose rifapentine with moxifloxacin for pulmonary tuberculosis. N. Engl. J. Med. 371:1599–608
     [Google Scholar]
  3. 3. 
    Merle CS, Fielding K, Sow OB, Gninafon M, Lo MB et al. 2014. A four-month gatifloxacin-containing regimen for treating tuberculosis. N. Engl. J. Med. 371:1588–98
     [Google Scholar]
  4. 4. 
    Gillespie SH, Crook AM, McHugh TD, Mendel CM, Meredith SK et al. 2014. Four-month moxifloxacin-based regimens for drug-sensitive tuberculosis. N. Engl. J. Med. 371:1577–87
     [Google Scholar]
  5. 5. 
    Vernon A, Fielding K, Savic R, Dodd L, Nahid P 2019. The importance of adherence in tuberculosis treatment clinical trials and its relevance in explanatory and pragmatic trials. PLOS Med 16:e1002884
     [Google Scholar]
  6. 6. 
    Eisinger RW, Embry AC, Read SW, Fauci AS 2020. 2019: A banner year for tuberculosis research. J. Infect. Dis. In press
    [Google Scholar]
  7. 7. 
    Tornheim JA, Dooley KE. 2019. The global landscape of tuberculosis therapeutics. Annu. Rev. Med. 70:105–20
     [Google Scholar]
  8. 8. 
    Nuermberger E, Sizemore C, Romero K, Hanna D 2016. Toward an evidence-based nonclinical road map for evaluating the efficacy of new tuberculosis (TB) drug regimens: Proceedings of a Critical Path to TB Drug Regimens-National Institute of Allergy and Infectious Diseases In Vivo Pharmacology Workshop for TB Drug Development. Antimicrob. Agents Chemother. 60:1177–82
     [Google Scholar]
  9. 9. 
    Dooley KE, Hanna D, Mave V, Eisenach K, Savic RM 2019. Advancing the development of new tuberculosis treatment regimens: the essential role of translational and clinical pharmacology and microbiology. PLOS Med 16:e1002842
     [Google Scholar]
  10. 10. 
    Nuermberger EL. 2017. Preclinical efficacy testing of new drug candidates. Microbiol. Spectr. 5:3TBTB2-0034–2017
     [Google Scholar]
  11. 11. 
    Miller R, Ewy W, Corrigan BW, Ouellet D, Hermann D et al. 2005. How modeling and simulation have enhanced decision making in new drug development. J. Pharmacokinet. Pharmacodyn. 32:185–97
     [Google Scholar]
  12. 12. 
    Tuntland T, Ethell B, Kosaka T, Blasco F, Zang RX et al. 2014. Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research. Front. Pharmacol. 5:174
     [Google Scholar]
  13. 13. 
    Dartois V, Barry CE 3rd 2013. A medicinal chemists’ guide to the unique difficulties of lead optimization for tuberculosis. Bioorg. Med. Chem. Lett. 23:4741–50
     [Google Scholar]
  14. 14. 
    Franzblau SG, DeGroote MA, Cho SH, Andries K, Nuermberger E et al. 2012. Comprehensive analysis of methods used for the evaluation of compounds against Mycobacterium tuberculosis. . Tuberculosis 92:453–88
     [Google Scholar]
  15. 15. 
    Lanoix JP, Chaisson RE, Nuermberger EL 2016. Shortening tuberculosis treatment with fluoroquinolones: lost in translation. ? Clin. Infect. Dis. 62:484–90
     [Google Scholar]
  16. 16. 
    Gumbo T, Angulo-Barturen I, Ferrer-Bazaga S 2015. Pharmacokinetic-pharmacodynamic and dose-response relationships of antituberculosis drugs: recommendations and standards for industry and academia. J. Infect. Dis. 211:Suppl. 3S96–106
     [Google Scholar]
  17. 17. 
    Brill MJE, Kristoffersson AN, Zhao C, Nielsen EI, Friberg LE 2018. Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations. Clin. Microbiol. Infect. 24:697–706
     [Google Scholar]
  18. 18. 
    Drusano GL. 2016. From lead optimization to NDA approval for a new antimicrobial: use of pre-clinical effect models and pharmacokinetic/pharmacodynamic mathematical modeling. Bioorg. Med. Chem. 24:6401–8
     [Google Scholar]
  19. 19. 
    Bulitta JB, Hope WW, Eakin AE, Guina T, Tam VH et al. 2019. Generating robust and informative nonclinical in vitro and in vivo bacterial infection model efficacy data to support translation to humans. Antimicrob. Agents Chemother. 63:e02307–18
     [Google Scholar]
  20. 20. 
    Gumbo T, Lenaerts AJ, Hanna D, Romero K, Nuermberger E 2015. Nonclinical models for antituberculosis drug development: a landscape analysis. J. Infect. Dis. 211:Suppl. 3S83–95
     [Google Scholar]
  21. 21. 
    Yasinskaya Y, Sacks L. 2011. Models and approaches for anti-TB drug testing. Expert Rev. Anti. Infect. Ther. 9:823–31
     [Google Scholar]
  22. 22. 
    Gold B, Nathan C. 2017. Targeting phenotypically tolerant Mycobacterium tuberculosis. Microbiol. Spectr 5:1TBTB2–0031-2016
     [Google Scholar]
  23. 23. 
    Ambrose PG, Bhavnani SM, Rubino CM, Louie A, Gumbo T et al. 2007. Pharmacokinetics-pharmacodynamics of antimicrobial therapy: It's not just for mice anymore. Clin. Infect. Dis. 44:79–86
     [Google Scholar]
  24. 24. 
    Gumbo T, Pasipanodya JG, Romero K, Hanna D, Nuermberger E 2015. Forecasting accuracy of the hollow fiber model of tuberculosis for clinical therapeutic outcomes. Clin. Infect. Dis. 61:Suppl. 1S25–31
     [Google Scholar]
  25. 25. 
    Gumbo T, Pasipanodya JG, Nuermberger E, Romero K, Hanna D 2015. Correlations between the hollow fiber model of tuberculosis and therapeutic events in tuberculosis patients: learn and confirm. Clin. Infect. Dis. 61:Suppl. 1S18–24
     [Google Scholar]
  26. 26. 
    Deshpande D, Gumbo T. 2011. Pharmacokinetic/pharmacodynamic-based treatment of disseminated Mycobacterium avium. . Future Microbiol 6:433–39
     [Google Scholar]
  27. 27. 
    Zhang N, Strydom N, Tyagi S, Soni H, Tasneen R et al. 2020. Mechanistic modeling of Mycobacterium tuberculosis infection in murine models for drug and vaccine efficacy studies. Antimicrob. Agents Chemother. 64:3e01727–19
     [Google Scholar]
  28. 28. 
    Bartelink IH, Zhang N, Keizer RJ, Strydom N, Converse PJ et al. 2017. New paradigm for translational modeling to predict long-term tuberculosis treatment response. Clin. Transl. Sci. 10:366–79
     [Google Scholar]
  29. 29. 
    Almeida D, Nuermberger E, Tasneen R, Rosenthal I, Tyagi S et al. 2009. Paradoxical effect of isoniazid on the activity of rifampin-pyrazinamide combination in a mouse model of tuberculosis. Antimicrob. Agents Chemother. 53:4178–84
     [Google Scholar]
  30. 30. 
    Rosenthal IM, Tasneen R, Peloquin CA, Zhang M, Almeida D et al. 2012. Dose-ranging comparison of rifampin and rifapentine in two pathologically distinct murine models of tuberculosis. Antimicrob. Agents Chemother. 56:4331–40
     [Google Scholar]
  31. 31. 
    de Steenwinkel JE, Aarnoutse RE, de Knegt GJ, ten Kate MT, Teulen M et al. 2013. Optimization of the rifampin dosage to improve the therapeutic efficacy in tuberculosis treatment using a murine model. Am. J. Respir. Crit. Care Med. 187:1127–34
     [Google Scholar]
  32. 32. 
    Rouan MC, Lounis N, Gevers T, Dillen L, Gilissen R et al. 2012. Pharmacokinetics and pharmacodynamics of TMC207 and its N-desmethyl metabolite in a murine model of tuberculosis. Antimicrob. Agents Chemother. 56:1444–51
     [Google Scholar]
  33. 33. 
    Lanoix JP, Ioerger T, Ormond A, Kaya F, Sacchettini J et al. 2016. Selective inactivity of pyrazinamide against tuberculosis in C3HeB/FeJ mice is best explained by neutral pH of caseum. Antimicrob. Agents Chemother. 60:735–43
     [Google Scholar]
  34. 34. 
    Cooper AM. 2009. Cell-mediated immune responses in tuberculosis. Annu. Rev. Immunol. 27:393–422
     [Google Scholar]
  35. 35. 
    Park SW, Tasneen R, Converse PJ, Nuermberger EL 2017. Immunodeficiency and intermittent dosing promote acquired rifamycin monoresistance in murine tuberculosis. Antimicrob. Agents Chemother. 61:e01502–17
     [Google Scholar]
  36. 36. 
    Xu J, Li SY, Almeida DV, Tasneen R, Barnes-Boyle K et al. 2019. Contribution of pretomanid to novel regimens containing bedaquiline with either linezolid or moxifloxacin and pyrazinamide in murine models of tuberculosis. Antimicrob. Agents Chemother. 63:5e00021–19
     [Google Scholar]
  37. 37. 
    Irwin SM, Driver E, Lyon E, Schrupp C, Ryan G et al. 2015. Presence of multiple lesion types with vastly different microenvironments in C3HeB/FeJ mice following aerosol infection with Mycobacterium tuberculosis. Dis. Model. Mech 8:591–602
     [Google Scholar]
  38. 38. 
    Lanoix JP, Lenaerts AJ, Nuermberger EL 2015. Heterogeneous disease progression and treatment response in a C3HeB/FeJ mouse model of tuberculosis. Dis. Model. Mech. 8:603–10
     [Google Scholar]
  39. 39. 
    Gengenbacher M, Duque-Correa MA, Kaiser P, Schuerer S, Lazar D et al. 2017. NOS2-deficient mice with hypoxic necrotizing lung lesions predict outcomes of tuberculosis chemotherapy in humans. Sci. Rep. 7:8853
     [Google Scholar]
  40. 40. 
    Robertson GT, Ektnitphong VA, Scherman MS, McNeil MB, Dennison D et al. 2019. Efficacy and improved resistance potential of a cofactor-independent InhA inhibitor of Mycobacterium tuberculosis in the C3HeB/FeJ mouse model. Antimicrob. Agents Chemother. 63:4e02071–18
     [Google Scholar]
  41. 41. 
    Subbian S, Tsenova L, Yang G, O'Brien P, Parsons S et al. 2011. Chronic pulmonary cavitary tuberculosis in rabbits: a failed host immune response. Open Biol 1:110016
     [Google Scholar]
  42. 42. 
    Via LE, Schimel D, Weiner DM, Dartois V, Dayao E et al. 2012. Infection dynamics and response to chemotherapy in a rabbit model of tuberculosis using [(1)(8)F]2-fluoro-deoxy-d-glucose positron emission tomography and computed tomography. Antimicrob. Agents Chemother. 56:4391–402
     [Google Scholar]
  43. 43. 
    Blanc L, Sarathy JP, Alvarez Cabrera N, O'Brien P, Dias-Freedman I et al. 2018. Impact of immunopathology on the antituberculous activity of pyrazinamide. J. Exp. Med. 215:1975–86
     [Google Scholar]
  44. 44. 
    Prideaux B, Lenaerts A, Dartois V 2018. Imaging and spatially resolved quantification of drug distribution in tissues by mass spectrometry. Curr. Opin. Chem. Biol. 44:93–100
     [Google Scholar]
  45. 45. 
    Zimmerman M, Blanc L, Chen PY, Dartois V, Prideaux B 2018. Spatial quantification of drugs in pulmonary tuberculosis lesions by laser capture microdissection liquid chromatography mass spectrometry (LCM-LC/MS). J. Vis. Exp. 134:e57402
     [Google Scholar]
  46. 46. 
    Sarathy J, Blanc L, Alvarez-Cabrera N, O'Brien P, Dias-Freedman I et al. 2019. Fluoroquinolone efficacy against tuberculosis is driven by penetration into lesions and activity against resident bacterial populations. Antimicrob. Agents Chemother. 63:5e02516–18
     [Google Scholar]
  47. 47. 
    Kjellsson MC, Via LE, Goh A, Weiner D, Low KM et al. 2012. Pharmacokinetic evaluation of the penetration of antituberculosis agents in rabbit pulmonary lesions. Antimicrob. Agents Chemother. 56:446–57
     [Google Scholar]
  48. 48. 
    Via LE, Savic R, Weiner DM, Zimmerman MD, Prideaux B et al. 2015. Host-mediated bioactivation of pyrazinamide: implications for efficacy, resistance, and therapeutic alternatives. ACS Infect. Dis. 1:203–14
     [Google Scholar]
  49. 49. 
    Prideaux B, Dartois V, Staab D, Weiner DM, Goh A et al. 2011. High-sensitivity MALDI-MRM-MS imaging of moxifloxacin distribution in tuberculosis-infected rabbit lungs and granulomatous lesions. Anal. Chem. 83:2112–18
     [Google Scholar]
  50. 50. 
    Prideaux B, ElNaggar MS, Zimmerman M, Wiseman JM, Li X, Dartois V 2015. Mass spectrometry imaging of levofloxacin distribution in TB-infected pulmonary lesions by MALDI-MSI and continuous liquid microjunction surface sampling. Int. J. Mass Spectrom. 377:699–708
     [Google Scholar]
  51. 51. 
    Blanc L, Daudelin IB, Podell BK, Chen PY, Zimmerman M et al. 2018. High-resolution mapping of fluoroquinolones in TB rabbit lesions reveals specific distribution in immune cell types. eLife 7:e41115
     [Google Scholar]
  52. 52. 
    Sarathy JP, Via LE, Weiner D, Blanc L, Boshoff H et al. 2018. Extreme drug tolerance of Mycobacterium tuberculosis in caseum. Antimicrob. Agents Chemother. 62:2e02266–17
     [Google Scholar]
  53. 53. 
    White AG, Maiello P, Coleman MT, Tomko JA, Frye LJ et al. 2017. Analysis of 18FDG PET/CT imaging as a tool for studying Mycobacterium tuberculosis infection and treatment in non-human primates. J. Vis. Exp. 127:e56375
     [Google Scholar]
  54. 54. 
    Martin CJ, Cadena AM, Leung VW, Lin PL, Maiello P et al. 2017. Digitally barcoding Mycobacterium tuberculosis reveals in vivo infection dynamics in the macaque model of tuberculosis. mBio 8:3e00312–17
     [Google Scholar]
  55. 55. 
    Lin PL, Ford CB, Coleman MT, Myers AJ, Gawande R et al. 2014. Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing. Nat. Med. 20:75–79
     [Google Scholar]
  56. 56. 
    Lin PL, Dartois V, Johnston PJ, Janssen C, Via L et al. 2012. Metronidazole prevents reactivation of latent Mycobacterium tuberculosis infection in macaques. PNAS 109:14188–93
     [Google Scholar]
  57. 57. 
    Coleman MT, Chen RY, Lee M, Lin PL, Dodd LE et al. 2014. PET/CT imaging reveals a therapeutic response to oxazolidinones in macaques and humans with tuberculosis. Sci. Transl. Med. 6:265ra167
     [Google Scholar]
  58. 58. 
    Via LE, England K, Weiner DM, Schimel D, Zimmerman MD et al. 2015. A sterilizing tuberculosis treatment regimen is associated with faster clearance of bacteria in cavitary lesions in marmosets. Antimicrob. Agents Chemother. 59:4181–89
     [Google Scholar]
  59. 59. 
    Via LE, Weiner DM, Schimel D, Lin PL, Dayao E et al. 2013. Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus). Infect. Immun. 81:2909–19
     [Google Scholar]
  60. 60. 
    Benson N. 2019. Quantitative systems pharmacology and empirical models: friends or foes. ? CPT Pharmacometrics Syst. Pharmacol. 8:135–37
     [Google Scholar]
  61. 61. 
    Shah P, Kendall F, Khozin S, Goosen R, Hu J et al. 2019. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit. Med. 2:69
     [Google Scholar]
  62. 62. 
    Silva A, Lee BY, Clemens DL, Kee T, Ding X et al. 2016. Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model. PNAS 113:E2172–79
     [Google Scholar]
  63. 63. 
    Al-Shyoukh I, Yu F, Feng J, Yan K, Dubinett S et al. 2011. Systematic quantitative characterization of cellular responses induced by multiple signals. BMC Syst. Biol. 5:88
     [Google Scholar]
  64. 64. 
    Clemens DL, Lee BY, Silva A, Dillon BJ, Maslesa-Galic S et al. 2019. Artificial intelligence enabled parabolic response surface platform identifies ultra-rapid near-universal TB drug treatment regimens comprising approved drugs. PLOS ONE 14:e0215607
     [Google Scholar]
  65. 65. 
    Williams K, Minkowski A, Amoabeng O, Peloquin CA, Taylor D et al. 2012. Sterilizing activities of novel combinations lacking first- and second-line drugs in a murine model of tuberculosis. Antimicrob. Agents Chemother. 56:3114–20
     [Google Scholar]
  66. 66. 
    Danhof M, de Jongh J, De Lange EC, Della Pasqua O, Ploeger BA, Voskuyl RA 2007. Mechanism-based pharmacokinetic-pharmacodynamic modeling: biophase distribution, receptor theory, and dynamical systems analysis. Annu. Rev. Pharmacol. Toxicol. 47:357–400
     [Google Scholar]
  67. 67. 
    Danhof M, de Lange EC, Della Pasqua OE, Ploeger BA, Voskuyl RA 2008. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends Pharmacol. Sci. 29:186–91
     [Google Scholar]
  68. 68. 
    Rathi C, Lee RE, Meibohm B 2016. Translational PK/PD of anti-infective therapeutics. Drug Discov. Today Technol. 21–22:41–49
     [Google Scholar]
  69. 69. 
    Lenaerts A, Barry CE 3rd, Dartois V 2015. Heterogeneity in tuberculosis pathology, microenvironments and therapeutic responses. Immunol. Rev. 264:288–307
     [Google Scholar]
  70. 70. 
    Drusano GL. 2004. Antimicrobial pharmacodynamics: critical interactions of ‘bug and drug’. Nat. Rev. Microbiol. 2:289–300
     [Google Scholar]
  71. 71. 
    Kirschner D, Pienaar E, Marino S, Linderman JJ 2017. A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment. Curr. Opin. Syst. Biol. 3:170–85
     [Google Scholar]
  72. 72. 
    Lyons MA, Lenaerts AJ. 2015. Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model. J. Pharmacokinet. Pharmacodyn. 42:375–89
     [Google Scholar]
  73. 73. 
    Pienaar E, Cilfone NA, Lin PL, Dartois V, Mattila JT et al. 2015. A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment. J. Theor. Biol. 367:166–79
     [Google Scholar]
  74. 74. 
    Boshoff HI, Lun DS. 2010. Systems biology approaches to understanding mycobacterial survival mechanisms. Drug Discov. Today Dis. Mech. 7:e75–82
     [Google Scholar]
  75. 75. 
    Marino S, Gideon HP, Gong C, Mankad S, McCrone JT et al. 2016. Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome. PLOS Comput. Biol. 12:e1004804
     [Google Scholar]
  76. 76. 
    Young D, Stark J, Kirschner D 2008. Systems biology of persistent infection: tuberculosis as a case study. Nat. Rev. Microbiol. 6:520–28
     [Google Scholar]
  77. 77. 
    Sershen CL, Plimpton SJ, May EE 2016. Oxygen modulates the effectiveness of granuloma mediated host response to Mycobacterium tuberculosis: a multiscale computational biology approach. Front. Cell Infect. Microbiol. 6:6
     [Google Scholar]
  78. 78. 
    Cicchese JM, Evans S, Hult C, Joslyn LR, Wessler T et al. 2018. Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology. Immunol. Rev. 285:147–67
     [Google Scholar]
  79. 79. 
    Marino S, Hult C, Wolberg P, Linderman JJ, Kirschner DE 2018. The role of dimensionality in understanding granuloma formation. Computation 6:458
     [Google Scholar]
  80. 80. 
    Pienaar E, Sarathy J, Prideaux B, Dietzold J, Dartois V et al. 2017. Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach. PLOS Comput. Biol. 13:e1005650
     [Google Scholar]
  81. 81. 
    Cilfone NA, Perry CR, Kirschner DE, Linderman JJ 2013. Multi-scale modeling predicts a balance of tumor necrosis factor-α and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection. PLOS ONE 8:e68680
     [Google Scholar]
  82. 82. 
    Bowness R, Chaplain MAJ, Powathil GG, Gillespie SH 2018. Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: insights from a hybrid multiscale cellular automaton model. J. Theor. Biol. 446:87–100
     [Google Scholar]
  83. 83. 
    Pitcher MJ, Bowness R, Dobson S, Gillespie SH 2018. A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection. Appl. Netw. Sci. 3:33
     [Google Scholar]
  84. 84. 
    Huh Y, Smith DE, Feng MR 2011. Interspecies scaling and prediction of human clearance: comparison of small- and macro-molecule drugs. Xenobiotica 41:972–87
     [Google Scholar]
  85. 85. 
    Jones H, Rowland-Yeo K. 2013. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst. Pharmacol. 2:e63
     [Google Scholar]
  86. 86. 
    Dartois V. 2014. The path of anti-tuberculosis drugs: from blood to lesions to mycobacterial cells. Nat. Rev. Microbiol. 12:159–67
     [Google Scholar]
  87. 87. 
    Datta M, Via LE, Kamoun WS, Liu C, Chen W et al. 2015. Anti-vascular endothelial growth factor treatment normalizes tuberculosis granuloma vasculature and improves small molecule delivery. PNAS 112:1827–32
     [Google Scholar]
  88. 88. 
    Smith DA, Rowland M. 2019. Intracellular and intraorgan concentrations of small molecule drugs: theory, uncertainties in infectious diseases and oncology, and promise. Drug Metab. Dispos. 47:665–72
     [Google Scholar]
  89. 89. 
    Sarathy JP, Zuccotto F, Hsinpin H, Sandberg L, Via LE et al. 2016. Prediction of drug penetration in tuberculosis lesions. ACS Infect. Dis. 2:552–63
     [Google Scholar]
  90. 90. 
    Strydom N, Gupta SV, Fox WS, Via LE, Bang H et al. 2019. Tuberculosis drugs’ distribution and emergence of resistance in patient's lung lesions: a mechanistic model and tool for regimen and dose optimization. PLOS Med 16:e1002773
     [Google Scholar]
  91. 91. 
    Irwin SM, Gruppo V, Brooks E, Gilliland J, Scherman M et al. 2014. Limited activity of clofazimine as a single drug in a mouse model of tuberculosis exhibiting caseous necrotic granulomas. Antimicrob. Agents Chemother. 58:4026–34
     [Google Scholar]
  92. 92. 
    Via LE, Lin PL, Ray SM, Carrillo J, Allen SS et al. 2008. Tuberculous granulomas are hypoxic in guinea pigs, rabbits, and nonhuman primates. Infect. Immun. 76:2333–40
     [Google Scholar]
  93. 93. 
    Ganchua SKC, Cadena AM, Maiello P, Gideon HP, Myers AJ et al. 2018. Lymph nodes are sites of prolonged bacterial persistence during Mycobacterium tuberculosis infection in macaques. PLOS Pathog 14:e1007337
     [Google Scholar]
  94. 94. 
    Kempker RR, Heinrichs MT, Nikolaishvili K, Sabulua I, Bablishvili N et al. 2017. Lung tissue concentrations of pyrazinamide among patients with drug-resistant pulmonary tuberculosis. Antimicrob. Agents Chemother. 61:6e00226–17
     [Google Scholar]
  95. 95. 
    Fallahi-Sichani M, El-Kebir M, Marino S, Kirschner DE, Linderman JJ 2011. Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation. J. Immunol. 186:3472–83
     [Google Scholar]
  96. 96. 
    Ganguli S, Gammack D, Kirschner DE 2005. A metapopulation model of granuloma formation in the lung during infection with mycobacterium tuberculosis. Math. Biosci. Eng. 2:535–60
     [Google Scholar]
  97. 97. 
    Marino S, El-Kebir M, Kirschner D 2011. A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis. J. Theor. Biol. 280:50–62
     [Google Scholar]
  98. 98. 
    Eftimie R, Gillard JJ, Cantrell DA 2016. Mathematical models for immunology: current state of the art and future research directions. Bull. Math. Biol. 78:2091–134
     [Google Scholar]
  99. 99. 
    Campion JJ, Chung P, McNamara PJ, Titlow WB, Evans ME 2005. Pharmacodynamic modeling of the evolution of levofloxacin resistance in Staphylococcus aureus. Antimicrob. . Agents Chemother 49:2189–99
     [Google Scholar]
  100. 100. 
    Mouton JW, Vinks AA. 2005. Pharmacokinetic/pharmacodynamic modelling of antibacterials in vitro and in vivo using bacterial growth and kill kinetics: the minimum inhibitory concentration versus stationary concentration. Clin. Pharmacokinet. 44:201–10
     [Google Scholar]
  101. 101. 
    Tam VH, Schilling AN, Nikolaou M 2005. Modelling time-kill studies to discern the pharmacodynamics of meropenem. J. Antimicrob. Chemother. 55:699–706
     [Google Scholar]
  102. 102. 
    Nielsen EI, Friberg LE. 2013. Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Pharmacol. Rev. 65:1053–90
     [Google Scholar]
  103. 103. 
    Drusano GL, Fregeau C, Liu W, Brown DL, Louie A 2010. Impact of burden on granulocyte clearance of bacteria in a mouse thigh infection model. Antimicrob. Agents Chemother. 54:4368–72
     [Google Scholar]
  104. 104. 
    Drusano GL, Liu W, Kulawy R, Louie A 2011. Impact of granulocytes on the antimicrobial effect of tedizolid in a mouse thigh infection model. Antimicrob. Agents Chemother. 55:5300–5
     [Google Scholar]
  105. 105. 
    Drusano GL, Vanscoy B, Liu W, Fikes S, Brown D, Louie A 2011. Saturability of granulocyte kill of Pseudomonas aeruginosa in a murine model of pneumonia. Antimicrob. Agents Chemother. 55:2693–95
     [Google Scholar]
  106. 106. 
    Clewe O, Aulin L, Hu Y, Coates AR, Simonsson US 2016. A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro. J. Antimicrob. Chemother. 71:964–74
     [Google Scholar]
  107. 107. 
    Wicha SG, Clewe O, Svensson RJ, Gillespie SH, Hu Y et al. 2018. Forecasting clinical dose-response from preclinical studies in tuberculosis research: translational predictions with rifampicin. Clin. Pharmacol. Ther. 104:1208–18
     [Google Scholar]
  108. 108. 
    Foucquier J, Guedj M. 2015. Analysis of drug combinations: current methodological landscape. Pharmacol. Res. Perspect. 3:e00149
     [Google Scholar]
  109. 109. 
    Bliss CI. 1939. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26:585–615
     [Google Scholar]
  110. 110. 
    Greco WR, Bravo G, Parsons JC 1995. The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47:331–85
     [Google Scholar]
  111. 111. 
    Geary N. 2013. Understanding synergy. Am. J. Physiol. Endocrinol. Metab. 304:E237–53
     [Google Scholar]
  112. 112. 
    Berenbaum MC. 1978. A method for testing for synergy with any number of agents. J. Infect. Dis. 137:122–30
     [Google Scholar]
  113. 113. 
    Loewe S. 1953. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3:285–90
     [Google Scholar]
  114. 114. 
    Wood K, Nishida S, Sontag ED, Cluzel P 2012. Mechanism-independent method for predicting response to multidrug combinations in bacteria. PNAS 109:12254–59
     [Google Scholar]
  115. 115. 
    Zimmer A, Katzir I, Dekel E, Mayo AE, Alon U 2016. Prediction of multidimensional drug dose responses based on measurements of drug pairs. PNAS 113:10442–47
     [Google Scholar]
  116. 116. 
    Wicha SG, Chen C, Clewe O, Simonsson USH 2017. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nat. Commun. 8:2129
     [Google Scholar]
  117. 117. 
    Ariens EJ, Van Rossum JM, Simonis AM 1957. Affinity, intrinsic activity and drug interactions. Pharmacol. Rev. 9:218–36
     [Google Scholar]
  118. 118. 
    Clewe O, Wicha SG, de Vogel CP, de Steenwinkel JEM, Simonsson USH 2018. A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations. J. Antimicrob. Chemother. 73:437–47
     [Google Scholar]
  119. 119. 
    Chen C, Wicha SG, de Knegt GJ, Ortega F, Alameda L et al. 2017. Assessing pharmacodynamic interactions in mice using the multistate tuberculosis pharmacometric and general pharmacodynamic interaction models. CPT Pharmacometrics Syst. Pharmacol. 6:787–97
     [Google Scholar]
  120. 120. 
    Cokol M, Kuru N, Bicak E, Larkins-Ford J, Aldridge BB 2017. Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis. Sci. . Adv 3:10e1701881
     [Google Scholar]
  121. 121. 
    Zhang Y, Yew WW. 2015. Mechanisms of drug resistance in Mycobacterium tuberculosis: update 2015. Int. J. Tuberc. Lung. Dis. 19:1276–89
     [Google Scholar]
  122. 122. 
    Veziris N, Bernard C, Guglielmetti L, Le Du D, Marigot-Outtandy D et al. 2017. Rapid emergence of Mycobacterium tuberculosis bedaquiline resistance: lessons to avoid repeating past errors. Eur. Respir. J. 49:31601719
     [Google Scholar]
  123. 123. 
    Fujiwara M, Kawasaki M, Hariguchi N, Liu Y, Matsumoto M 2018. Mechanisms of resistance to delamanid, a drug for Mycobacterium tuberculosis. . Tuberculosis 108:186–94
     [Google Scholar]
  124. 124. 
    David HL. 1970. Probability distribution of drug-resistant mutants in unselected populations of Mycobacterium tuberculosis. Appl. Microbiol 20:810–14
     [Google Scholar]
  125. 125. 
    Zhang Y, Yew WW, Barer MR 2012. Targeting persisters for tuberculosis control. Antimicrob. Agents Chemother. 56:2223–30
     [Google Scholar]
  126. 126. 
    Zhang Y. 2014. Persisters, persistent infections and the Yin-Yang model. Emerg. Microbes Infect. 3:e3
     [Google Scholar]
  127. 127. 
    Greenfield BK, Shaked S, Marrs CF, Nelson P, Raxter I et al. 2018. Modeling the emergence of antibiotic resistance in the environment: an analytical solution for the minimum selection concentration. Antimicrob. Agents Chemother. 62:3e01686–17
     [Google Scholar]
  128. 128. 
    Zhao X, Drlica K. 2002. Restricting the selection of antibiotic-resistant mutant bacteria: measurement and potential use of the mutant selection window. J. Infect. Dis. 185:561–65
     [Google Scholar]
  129. 129. 
    Drlica K, Zhao X. 2007. Mutant selection window hypothesis updated. Clin. Infect. Dis. 44:681–88
     [Google Scholar]
  130. 130. 
    Elliott AM, Berning SE, Iseman MD, Peloquin CA 1995. Failure of drug penetration and acquisition of drug resistance in chronic tuberculous empyema. Tuber. Lung Dis. 76:463–67
     [Google Scholar]
  131. 131. 
    Dheda K, Lenders L, Magombedze G, Srivastava S, Raj P et al. 2018. Drug-penetration gradients associated with acquired drug resistance in patients with tuberculosis. Am. J. Respir. Crit. Care Med. 198:1208–19
     [Google Scholar]
  132. 132. 
    Mouton JW, Vinks AA, Punt NC 1997. Pharmacokinetic-pharmacodynamic modeling of activity of ceftazidime during continuous and intermittent infusion. Antimicrob. Agents Chemother. 41:733–38
     [Google Scholar]
  133. 133. 
    Meagher AK, Forrest A, Dalhoff A, Stass H, Schentag JJ 2004. Novel pharmacokinetic-pharmacodynamic model for prediction of outcomes with an extended-release formulation of ciprofloxacin. Antimicrob. Agents Chemother. 48:2061–68
     [Google Scholar]
  134. 134. 
    Tam VH, Louie A, Deziel MR, Liu W, Leary R, Drusano GL 2005. Bacterial-population responses to drug-selective pressure: examination of garenoxacin's effect on Pseudomonas aeruginosa. J. Infect. Dis 192:420–28
     [Google Scholar]
  135. 135. 
    Li RC, Nix DE, Schentag JJ 1994. Pharmacodynamic modeling of bacterial kinetics: β-lactam antibiotics against Escherichia coli. . J. Pharm. Sci 83:970–75
     [Google Scholar]
  136. 136. 
    Wu B, Derendorf H. 2010. Pharmacokinetic/pharmacodynamic model-based combination therapy approach to target antibiotic resistant populations emerged from ciprofloxacin exposure. Pharmazie 65:417–20
     [Google Scholar]
  137. 137. 
    Pienaar E, Linderman JJ, Kirschner DE 2018. Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. PLOS ONE 13:e0196322
     [Google Scholar]
  138. 138. 
    Mohamed AF, Nielsen EI, Cars O, Friberg LE 2012. Pharmacokinetic-pharmacodynamic model for gentamicin and its adaptive resistance with predictions of dosing schedules in newborn infants. Antimicrob. Agents Chemother. 56:179–88
     [Google Scholar]
  139. 139. 
    Nielsen EI, Viberg A, Lowdin E, Cars O, Karlsson MO, Sandstrom M 2007. Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments. Antimicrob. Agents Chemother. 51:128–36
     [Google Scholar]
  140. 140. 
    Colijn C, Cohen T, Ganesh A, Murray M 2011. Spontaneous emergence of multiple drug resistance in tuberculosis before and during therapy. PLOS ONE 6:e18327
     [Google Scholar]
  141. 141. 
    Reed JL, Basu D, Butzler MA, McFall SM 2017. XtracTB Assay, a Mycobacterium tuberculosis molecular screening test with sensitivity approaching culture. Sci. Rep. 7:3653
     [Google Scholar]
  142. 142. 
    Kramnik I, Beamer G. 2016. Mouse models of human TB pathology: roles in the analysis of necrosis and the development of host-directed therapies. Semin. Immunopathol. 38:221–37
     [Google Scholar]
  143. 143. 
    Diacon AH, Maritz JS, Venter A, van Helden PD, Dawson R, Donald PR 2012. Time to liquid culture positivity can substitute for colony counting on agar plates in early bactericidal activity studies of antituberculosis agents. Clin. Microbiol. Infect. 18:711–17
     [Google Scholar]
  144. 144. 
    Bark CM, Okwera A, Joloba ML, Thiel BA, Nakibali JG et al. 2011. Time to detection of Mycobacterium tuberculosis as an alternative to quantitative cultures. Tuberculosis 91:257–59
     [Google Scholar]
  145. 145. 
    Bowness R, Boeree MJ, Aarnoutse R, Dawson R, Diacon A et al. 2015. The relationship between Mycobacterium tuberculosis MGIT time to positivity and cfu in sputum samples demonstrates changing bacterial phenotypes potentially reflecting the impact of chemotherapy on critical sub-populations. J. Antimicrob. Chemother. 70:448–55
     [Google Scholar]
  146. 146. 
    de Knegt GJ, Dickinson L, Pertinez H, Evangelopoulos D, McHugh TD et al. 2017. Assessment of treatment response by colony forming units, time to culture positivity and the molecular bacterial load assay compared in a mouse tuberculosis model. Tuberculosis 105:113–18
     [Google Scholar]
  147. 147. 
    Bizzi G. 1965. Use of a new antitussive preparation (1-N-phenyl-4-N-(2,3-dihydroxypropyl-diethylene-diamine) in pediatric practice. Gazz. Med. Ital. 124:240–44
     [Google Scholar]
  148. 148. 
    Basit A, Ahmad N, Khan AH, Javaid A, Syed Sulaiman SA et al. 2014. Predictors of two months culture conversion in multidrug-resistant tuberculosis: findings from a retrospective cohort study. PLOS ONE 9:e93206
     [Google Scholar]
  149. 149. 
    Singla R, Sarin R, Khalid UK, Mathuria K, Singla N et al. 2009. Seven-year DOTS-Plus pilot experience in India: results, constraints and issues. Int. J. Tuberc. Lung. Dis. 13:976–81
     [Google Scholar]
  150. 150. 
    Bojorquez-Chapela I, Backer CE, Orejel I, Lopez A, Diaz-Quinonez A et al. 2013. Drug resistance in Mexico: results from the National Survey on Drug-Resistant Tuberculosis. Int. J. Tuberc. Lung. Dis. 17:514–19
     [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-030920-011143
Loading
Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis
Annual Review of Pharmacology and Toxicology 61, 495 (2021); https://doi.org/10.1146/annurev-pharmtox-030920-011143
/content/journals/10.1146/annurev-pharmtox-030920-011143
/content/journals/10.1146/annurev-pharmtox-030920-011143
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error