1932

Abstract

Imperfect medication adherence remains the biggest predictor of treatment failure for patients with tuberculosis. Missed doses during treatment lead to relapse, tuberculosis resistance, and further spread of disease. Understanding individual patient phenotypes, population pharmacokinetics, resistance development, drug distribution to tuberculosis lesions, and pharmacodynamics at the site of infection is necessary to fully measure the impact of adherence on patient outcomes. To decrease the impact of expected variabilityin drug intake on tuberculosis outcomes, an improvement in patient adherence and new forgiving regimens that protect against missed doses are needed. In this review, we summarize emerging technologies to improve medication adherence in clinical practice and provide suggestions on how digital adherence technologies can be incorporated in clinical trials and practice and the drug development pipeline that will lead to more forgiving regimens and benefit patients suffering from tuberculosis.

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2022-01-06
2024-04-19
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Literature Cited

  1. 1. 
    Haberer JE, Sabin L, Amico KR, Orrell C, Galárraga O et al. 2017. Improving antiretroviral therapy adherence in resource-limited settings at scale: a discussion of interventions and recommendations. J. Int. AIDS Soc. 20:121371
    [Google Scholar]
  2. 2. 
    Vrijens B, De Geest S, Hughes DA, Przemyslaw K, Demonceau J et al. 2012. A new taxonomy for describing and defining adherence to medications. Br. J. Clin. Pharmacol. 73:5691–705
    [Google Scholar]
  3. 3. 
    De Geest S, Zullig LL, Dunbar-Jacob J, Hughes D, Wilson IB, Vrijens B. 2019. Improving medication adherence research reporting: ESPACOMP Medication Adherence Reporting Guideline (EMERGE). Eur. J. Cardiovasc. Nurs. 18:4258–59
    [Google Scholar]
  4. 4. 
    Osterberg LG, Urquhart J, Blaschke TF. 2010. Understanding forgiveness: minding and mining the gaps between pharmacokinetics and therapeutics. Clin. Pharmacol. Ther. 88:4457–59
    [Google Scholar]
  5. 5. 
    Imperial MZ, Nahid P, Phillips PPJ, Davies GR, Fielding K et al. 2018. A patient-level pooled analysis of treatment-shortening regimens for drug-susceptible pulmonary tuberculosis. Nat. Med. 24:111708–15
    [Google Scholar]
  6. 6. 
    Stagg HR, Lewis JJ, Liu X, Huan S, Jiang S et al. 2020. Temporal factors and missed doses of tuberculosis treatment. A causal associations approach to analyses of digital adherence data. Ann. Am. Thorac. Soc. 17:4438–49
    [Google Scholar]
  7. 7. 
    WHO (World Health Organ.) 2019. WHO consolidated guidelines on drug-resistant tuberculosis treatment Guidel., WHO Geneva, Switz:.
  8. 8. 
    Murray JF, Schraufnagel DE, Hopewell PC. 2015. Treatment of tuberculosis. A historical perspective. Ann. Am. Thorac. Soc. 12:121749–59
    [Google Scholar]
  9. 9. 
    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 16:12e1002884
    [Google Scholar]
  10. 10. 
    Mahishale V, Patil B, Lolly M, Eti A, Khan S 2015. Prevalence of smoking and its impact on treatment outcomes in newly diagnosed pulmonary tuberculosis patients: a hospital-based prospective study. Chonnam. Med. J. 51:286–90
    [Google Scholar]
  11. 11. 
    Baker MA, Harries AD, Jeon CY, Hart JE, Kapur A et al. 2011. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC Med 9:181
    [Google Scholar]
  12. 12. 
    Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Alffenaar JM 2018. Risk factors of multidrug-resistant tuberculosis: a global systematic review and meta-analysis. J. Infect. 77:6469–78
    [Google Scholar]
  13. 13. 
    Rockwood N, Abdullahi LH, Wilkinson RJ, Meintjes G. 2015. Risk factors for acquired rifamycin and isoniazid resistance: a systematic review and meta-analysis. PLOS ONE 10:9e0139017
    [Google Scholar]
  14. 14. 
    Alipanah N, Jarlsberg L, Miller C, Linh NN, Falzon D et al. 2018. Adherence interventions and outcomes of tuberculosis treatment: a systematic review and meta-analysis of trials and observational studies. PLOS Med 15:7e1002595
    [Google Scholar]
  15. 15. 
    Moonan PK, Quitugua TN, Pogoda JM, Woo G, Drewyer G et al. 2011. Does directly observed therapy (DOT) reduce drug resistant tuberculosis?. BMC Public Health 11:19
    [Google Scholar]
  16. 16. 
    Srivastava S, Pasipanodya JG, Meek C, Leff R, Gumbo T 2011. Multidrug-resistant tuberculosis not due to noncompliance but to between-patient pharmacokinetic variability. J. Infect. Dis. 204:121951–59
    [Google Scholar]
  17. 17. 
    Pasipanodya JG, Srivastava S, Gumbo T. 2012. Meta-analysis of clinical studies supports the pharmacokinetic variability hypothesis for acquired drug resistance and failure of antituberculosis therapy. Clin. Infect. Dis. 55:2169–77
    [Google Scholar]
  18. 18. 
    Smythe W, Khandelwal A, Merle C, Rustomjee R, Gninafon M et al. 2012. A semimechanistic pharmacokinetic-enzyme turnover model for rifampin autoinduction in adult tuberculosis patients. Antimicrob. Agents Chemother. 56:42091–98
    [Google Scholar]
  19. 19. 
    Hibma JE, Radtke KK, Dorman SE, Jindani A, Dooley KE et al. 2020. Rifapentine population pharmacokinetics and dosing recommendations for latent tuberculosis infection. Am. J. Respir. Crit. Care Med. 202:6866–77
    [Google Scholar]
  20. 20. 
    Savic RM, Lu Y, Bliven-Sizemore E, Weiner M, Nuermberger E et al. 2014. Population pharmacokinetics of rifapentine and desacetyl rifapentine in healthy volunteers: nonlinearities in clearance and bioavailability. Antimicrob. Agents Chemother. 58:63035–42
    [Google Scholar]
  21. 21. 
    Salinger DH, Subramoney V, Everitt D, Nedelman JR. 2019. Population pharmacokinetics of the antituberculosis agent pretomanid. Antimicrob. Agents Chemother. 63:10e00907-19
    [Google Scholar]
  22. 22. 
    Svensson E, Dosne A-G, Karlsson M. 2016. Population pharmacokinetics of bedaquiline and metabolite M2 in patients with drug-resistant tuberculosis: the effect of time-varying weight and albumin. CPT Pharmacomet. Syst. Pharmacol. 5:12682–91
    [Google Scholar]
  23. 23. 
    Karumbi J, Garner P. 2015. Directly observed therapy for treating tuberculosis. Cochrane Database Syst. Rev. 2015:5CD003343
    [Google Scholar]
  24. 24. 
    Pasipanodya JG, Gumbo T. 2013. A meta-analysis of self-administered versus directly observed therapy effect on microbiologic failure, relapse, and acquired drug resistance in tuberculosis patients. Clin. Infect. Dis. 57:21–31
    [Google Scholar]
  25. 25. 
    Tian JH, Lu ZX, Bachmann MO, Song FJ. 2014. Effectiveness of directly observed treatment of tuberculosis: a systematic review of controlled studies. Int. J. Tuberc. Lung Dis. 18:1092–98
    [Google Scholar]
  26. 26. 
    Yellappa V, Lefèvre P, Barraglioli T, Narayanan D, Van der Stuyft P. 2016. Coping with tuberculosis and directly observed treatment: a qualitative study among patients from South India. BMC Health Serv. Res. 16:283
    [Google Scholar]
  27. 27. 
    Wynne A, Richter S, Banura L, Kipp W. 2008. Challenges in tuberculosis care in Western Uganda: health care worker and patient perspectives. Int. J. Afr. Nurs. Sci. 1:6–10
    [Google Scholar]
  28. 28. 
    Sagbakken M, Frich JC, Bjune G. 2008. Barriers and enablers in the management of tuberculosis treatment in Addis Ababa, Ethiopia: a qualitative study. BMC Public Health 8:11
    [Google Scholar]
  29. 29. 
    Lei X, Huang K, Liu Q, Jie YF, Tang SL. 2016. Are tuberculosis patients adherent to prescribed treatments in China? Results from a prospective cohort study. Infect. Dis. Poverty 5:38
    [Google Scholar]
  30. 30. 
    Blaschke TF, Osterberg L, Vrijens B, Urquhart J. 2011. Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories. Annu. Rev. Pharmacol. Toxicol. 52:275–301
    [Google Scholar]
  31. 31. 
    Demonceau J, Ruppar T, Kristanto P, Hughes DA, Fargher E et al. 2013. Identification and assessment of adherence-enhancing interventions in studies assessing medication adherence through electronically compiled drug dosing histories: a systematic literature review and meta-analysis. Drugs 73:6545–62
    [Google Scholar]
  32. 32. 
    Sabin LL, DeSilva MB, Hamer DH, Xu K, Zhang J et al. 2010. Using electronic drug monitor feedback to improve adherence to antiretroviral therapy among HIV-positive patients in China. AIDS Behav 14:3580–89
    [Google Scholar]
  33. 33. 
    Subbaraman R, de Mondesert L, Musiimenta A, Pai M, Mayer K et al. 2018. Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities. BMJ Glob. Health 3:e001018
    [Google Scholar]
  34. 34. 
    Liu X, Lewis JJ, Zhang H, Lu W, Zhang S et al. 2015. Effectiveness of electronic reminders to improve medication adherence in tuberculosis patients: a cluster-randomised trial. . PLOS Med 12:9e1001876
    [Google Scholar]
  35. 35. 
    Thomas BE, Kumar JV, Onongaya C, Bhatt SN, Galivanche A et al. 2020. Explaining differences in the acceptability of 99DOTS, a cell phone-based strategy for monitoring adherence to tuberculosis medications: qualitative study of patients and health care providers. JMIR mHealth uHealth 8:7e16634
    [Google Scholar]
  36. 36. 
    Thomas BE, Kumar JV, Periyasamy M, Khandewale AS, Mercy JH et al. 2021. Acceptability of the Medication Event Reminder Monitor for Promoting Adherence to Multidrug-Resistant Tuberculosis Therapy in Two Indian Cities: Qualitative Study of Patients and Health Care Providers. J. Med. Internet Res. 23:6e23294
    [Google Scholar]
  37. 37. 
    Papineni S. 2020. Digital adherence technologies for TB: translating surveillance to care Master's thesis, Harvard Med. School Cambridge, MA:
  38. 38. 
    Stop TB Partnersh 2020. Smart medication container KIT Tech. Inf. Note Stop TB Partnersh. Geneva, Switz: http://stoptb.org/assets/documents/gdf/drugsupply/TIN_SMCK.pdf
  39. 39. 
    Stop TB Partnersh 2020. Smart medication container included in Stop TB Partnership's Global Drug Facility product catalog Press Release Novemb. 12. http://www.stoptb.org/news/stories/2020/ns20_031.html
  40. 40. 
    Matiru R, Ryan T. 2007. The global drug facility: a unique, holistic and pioneering approach to drug procurement and management. Bull. World Health Organ. 85:5348–53
    [Google Scholar]
  41. 41. 
    Chen RY, Via LE, Dodd LE, Walzl G, Malherbe ST et al. 2017. Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial. Gates Open Res 1:9
    [Google Scholar]
  42. 42. 
    Villarino ME, Scott NA, Weis SE, Weiner M, Conde MB et al. 2015. Treatment for preventing tuberculosis in children and adolescents: a randomized clinical trial of a 3-month, 12-dose regimen of a combination of rifapentine and isoniazid. JAMA Pediatr 169:3247–55
    [Google Scholar]
  43. 43. 
    Chappell LC, Brocklehurst P, Green ME, Hunter R, Hardy P et al. 2019. Planned early delivery or expectant management for late preterm pre-eclampsia (PHOENIX): a randomised controlled trial. Lancet 394:102041181–90
    [Google Scholar]
  44. 44. 
    Guillebaud J. 1987. The forgotten pill—and the paramount importance of the pill-free week. Br. J. Fam. Plan. 12:35–43
    [Google Scholar]
  45. 45. 
    Dartois V. 2011. Drug forgiveness and interpatient pharmacokinetic variability in tuberculosis. J. Infect. Dis. 204:121827–29
    [Google Scholar]
  46. 46. 
    Fors J, Strydom N, Fox WS, Keizer RJ, Savic RM. 2020. Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis. PLOS Comput. Biol. 16:8e10088107
    [Google Scholar]
  47. 47. 
    Ernest JP, Strydom N, Wang Q, Zhang N, Nuermberger E et al. 2021. Development of new tuberculosis drugs: translation to regimen composition for drug-sensitive and multidrug-resistant tuberculosis. Annu. Rev. Pharmacol. Toxicol. 61:495–516
    [Google Scholar]
  48. 48. 
    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]
  49. 49. 
    Esteves PJ, Abrantes J, Baldauf H, Mohamed L, Chen Y et al. 2018. The wide utility of rabbits as models of human diseases. Exp. Mol. Med. 50:51–10
    [Google Scholar]
  50. 50. 
    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 16:4e1002773
    [Google Scholar]
  51. 51. 
    Boissel J-P, Nony P. 2002. Using pharmacokinetic-pharmacodynamic relationships to predict the effect of poor compliance. Clin. Pharmacokinet. 41:11–6
    [Google Scholar]
  52. 52. 
    Nathan C, Barry CE 2015. TB drug development: immunology at the table. Immunol. Rev. 264:1308–18
    [Google Scholar]
  53. 53. 
    Sarathy JP, Zuccotto F, Hsinpin H, Sandberg L, Via LE et al. 2016. Prediction of drug penetration in tuberculosis lesions. ACS Infect. Dis. 2:8552–63
    [Google Scholar]
  54. 54. 
    Irwin SM, Prideaux B, Lyon ER, Zimmerman MD, Brooks EJ et al. 2016. Bedaquiline and pyrazinamide treatment responses are affected by pulmonary lesion heterogeneity in Mycobacterium tuberculosis infected C3HeB/FeJ mice. ACS Infect. Dis. 2:4251–67
    [Google Scholar]
  55. 55. 
    Vrijens B, Goetghebeur E. 2004. Electronic monitoring of variation in drug intakes can reduce bias and improve precision in pharmacokinetic/pharmacodynamic population. Stat. Med. 23:4531–44
    [Google Scholar]
  56. 56. 
    Azizi M, Sanghvi K, Saxena M, Gosse P, Reilly JP et al. 2021. Ultrasound renal denervation for hypertension resistant to a triple medication pill (RADIANCE-HTN TRIO): a randomised, multicentre, single-blind, sham-controlled trial. Lancet 397:2476–86
    [Google Scholar]
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