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Enregistrement W2052196547 · doi:10.1016/j.ebiom.2015.01.018

Innovations in Tuberculosis Diagnostics: Progress and Translational Challenges

2015· review· en· W2052196547 sur OpenAlexaffabout
Madhukar Pai

Notice bibliographique

RevueEBioMedicine · 2015
Typereview
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésTuberculosisBedaquilineMedicineDrug resistant tuberculosisTuberculosis diagnosisBattleDiseaseFamily medicineMycobacterium tuberculosisGeographyPathology

Résumé

récupéré en direct d'OpenAlex

Despite the long and hard battle against tuberculosis (TB), WHO estimated that 9 million people developed the disease in 2013, and nearly 1.5 million people died of TB (World Health Organization, 2014World Health Organization Global Tuberculosis Report 2014. WHO, Geneva2014: 1-289Google Scholar). To make matters worse, drug-resistance is a growing threat, and 3 out of 9 million TB cases are either not diagnosed, or not notified to TB control programs.But there is some good news from the perspective of new tool introduction. Slowly but surely, the landscape of TB technologies is changing (Pai and Schito, 2015Pai M. Schito M. Tuberculosis diagnostics in 2015: landscape, priorities, needs, and prospects.J. Infect. Dis. 2015; 211: S21-S28Crossref PubMed Scopus (132) Google Scholar). We now have a variety of new TB diagnostics, including rapid molecular tests (e.g. Xpert MTB/RIF, Cepheid Inc., USA) for detection as well as drug susceptibility testing (DST) (UNITAID, 2014UNITAID, 2014. Tuberculosis: Diagnostics Technology and Market Landscape, 3rd edition, in: Secretariat U., ed., WHO, Geneva, pp. 1–42.Google Scholar). We also have new TB drugs (e.g. bedaquiline and delamanid) on the market, and new TB drug regimens are expected within the next 2–3 years. These are major, exciting developments in the fight against a very ancient scourge.This article reviews the current best diagnostic tools available for TB diagnosis and monitoring, and describes the most important gaps, and translational challenges for developing innovative products that can meet the needs (Table 1).Table 1Unmet needs in TB diagnosis and monitoring.Indication for testingCurrently used toolsLimitations of existing toolsDesirable new tools (key references)Translational challenges for new tool development (key references)Triage test to identify individuals with presumed TB who need confirmatory testing1.TB symptoms (e.g. 2 weeks of cough)2.Chest x-rays1.Symptoms lack sensitivity and specificity, especially in HIV-infected populations and children2.Chest x-rays are sensitive, but not specific for TBA simple, low cost triage test for use by first-contact care healthcare providers as a rule-out test, ideally suitable for use by community health workers (Denkinger et al., 2015aDenkinger C.M. Kik S.V. Cirillo D. et al.Defining the needs for next generation assays for tuberculosis.J. Infect. Dis. 2015; 211: S29-S38Crossref PubMed Scopus (98) Google Scholar)Lack of validated biomarkers (Foundation for Innovative New Diagnostics, 2014Foundation for Innovative New Diagnostics, 2014. Strategy for Tuberculosis (and lower respiratory tract infections) 2015–2020. in: FIND Geneva, ed. FIND, Geneva.Google Scholar).Diagnosis of active pulmonary TB1.Sputum smear microscopy2.Nucleic acid amplification tests (NAAT)3.Cultures1.Smear microscopy lacks sensitivity and cannot detect drug resistance.2.NAAT are expensive and not easily deployable at the peripheral level.3.Cultures are expensive and require BSL3 labs, and results take time.A sputum-based replacement test for smear-microscopy; A non-sputum-based biomarker test for all forms of TB, ideally suitable for use at levels below microscopy centers (Denkinger et al., 2015aDenkinger C.M. Kik S.V. Cirillo D. et al.Defining the needs for next generation assays for tuberculosis.J. Infect. Dis. 2015; 211: S29-S38Crossref PubMed Scopus (98) Google Scholar)While several NAATs are being developed for microscopy centers, they will need to be evaluated in field conditions for policy. For the non-sputum TB test, the biggest challenge is the lack of validated biomarkers (UNITAID, 2014UNITAID, 2014. Tuberculosis: Diagnostics Technology and Market Landscape, 3rd edition, in: Secretariat U., ed., WHO, Geneva, pp. 1–42.Google Scholar, Denkinger et al., 2015aDenkinger C.M. Kik S.V. Cirillo D. et al.Defining the needs for next generation assays for tuberculosis.J. Infect. Dis. 2015; 211: S29-S38Crossref PubMed Scopus (98) Google Scholar, Foundation for Innovative New Diagnostics, 2014Foundation for Innovative New Diagnostics, 2014. Strategy for Tuberculosis (and lower respiratory tract infections) 2015–2020. in: FIND Geneva, ed. FIND, Geneva.Google Scholar).Diagnosis of extrapulmonary (EPTB) and childhood TB1.Smear microscopy2.Nucleic acid amplification tests3.Cultures1.Children and patients with EPTB often do not produce sputum. Invasive samples are usually necessary. Smear microscopy lacks sensitivity and cannot detect drug resistance.2.NAAT are expensive and not easily deployable at the peripheral level. Sensitivity in EPTB samples is lower than sputum.3.Cultures are expensive and require BSL3 labs, and results take time.A non-sputum-based biomarker test for all forms of TB, ideally suitable for use at levels below microscopy centers (Denkinger et al., 2015aDenkinger C.M. Kik S.V. Cirillo D. et al.Defining the needs for next generation assays for tuberculosis.J. Infect. Dis. 2015; 211: S29-S38Crossref PubMed Scopus (98) Google Scholar)For the non-sputum TB test, the biggest challenge is the lack of validated biomarkers (Foundation for Innovative New Diagnostics, 2014Foundation for Innovative New Diagnostics, 2014. Strategy for Tuberculosis (and lower respiratory tract infections) 2015–2020. in: FIND Geneva, ed. FIND, Geneva.Google Scholar).Drug susceptibility testing1.Nucleic acid amplification tests2.Cultures1.Current NAATs cannot reliably detect all mutations and sensitivity for drugs other than rifampicin is poor.2.Cultures are expensive and require BSL3 labs, and results take time.A new molecular DST for use at a microscopy center level, which can evaluate for resistance to rifampin, fluoroquinolones, isoniazid and pyrazinamide and enable the selection of the best drug regimen (Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar).Lack of good data on the correlation of mutations with phenotypic results and clinical outcomes and the association with cross-resistance (Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar, Solomon et al., 2015Solomon H. Yamaguchi K.D. Cirillo D. et al.Integration of published information into a resistance-associated mutation database for mycobacterium tuberculosis.J. Infect. Dis. 2015; 211: S50-S57Crossref PubMed Scopus (28) Google Scholar). There is also a need to align emerging TB drug regimens with companion diagnostics (Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar).Diagnosis of latent TB infection (LTBI)1.Tuberculin skin test (TST)2.Interferon-gamma release assays (IGRA)Neither TST nor IGRA can separate latent infection from active disease. Neither test can accurately identify those at highest risk of progression to active disease.A test that can resolve the spectrum of TB, and identify the subset of latently infected individuals who are at highest risk of progressing to active disease, and will benefit from preventive therapy (Pai et al., 2014Pai M. Denkinger C.M. Kik S.V. et al.Gamma interferon release assays for detection of Mycobacterium tuberculosis infection.Clin. Microbiol. Rev. 2014; 27: 3-20Crossref PubMed Scopus (519) Google Scholar, Barry et al., 2009Barry III, C.E. Boshoff H.I. Dartois V. et al.The spectrum of latent tuberculosis: rethinking the biology and intervention strategies.Nat. Rev. Microbiol. 2009; 7: 845-855Crossref PubMed Scopus (972) Google Scholar).Lack of validated biomarkers (Pai et al., 2014Pai M. Denkinger C.M. Kik S.V. et al.Gamma interferon release assays for detection of Mycobacterium tuberculosis infection.Clin. Microbiol. Rev. 2014; 27: 3-20Crossref PubMed Scopus (519) Google Scholar, Barry et al., 2009Barry III, C.E. Boshoff H.I. Dartois V. et al.The spectrum of latent tuberculosis: rethinking the biology and intervention strategies.Nat. Rev. Microbiol. 2009; 7: 845-855Crossref PubMed Scopus (972) Google Scholar).Test of cure (treatment monitoring)1.Serial smear microscopy2.Serial cultures1.Smears lack sensitivity, and cannot distinguish between live and dead bacilli.2.Serial cultures are expensive and time-consuming.An accurate test for cure that can be used to make changes in management (e.g. changes in regimens, or DST) (Wallis et al., 2010Wallis R.S. Pai M. Menzies D. et al.Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice.Lancet. 2010; 375: 1920-1937Summary Full Text Full Text PDF PubMed Scopus (367) Google Scholar).Lack of validated biomarkers (Wallis et al., 2010Wallis R.S. Pai M. Menzies D. et al.Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice.Lancet. 2010; 375: 1920-1937Summary Full Text Full Text PDF PubMed Scopus (367) Google Scholar). Open table in a new tab As shown in the Table, there are critical unmet needs that range from a simple, triage test for use in the community, to DST tools that can detect a range of mutations for several important drugs that will make up future drug regimens (Denkinger et al., 2015aDenkinger C.M. Kik S.V. Cirillo D. et al.Defining the needs for next generation assays for tuberculosis.J. Infect. Dis. 2015; 211: S29-S38Crossref PubMed Scopus (98) Google Scholar, Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar). For the next-generation DST tools, a big translational challenge is the paucity of good data on the correlation of mutations with phenotypic DST results and clinical outcomes and the association with cross-resistance (Solomon et al., 2015Solomon H. Yamaguchi K.D. Cirillo D. et al.Integration of published information into a resistance-associated mutation database for mycobacterium tuberculosis.J. Infect. Dis. 2015; 211: S50-S57Crossref PubMed Scopus (28) Google Scholar). This is particularly important to make sure that we have companion diagnostics for emerging TB drug regimens (Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar). The translational challenges associated with DST are reviewed elsewhere (Solomon et al., 2015Solomon H. Yamaguchi K.D. Cirillo D. et al.Integration of published information into a resistance-associated mutation database for mycobacterium tuberculosis.J. Infect. Dis. 2015; 211: S50-S57Crossref PubMed Scopus (28) Google Scholar).For the development of rapid triage tests, non-sputum based tests for active TB, highly predictive LTBI tests, and an accurate test for cure, we need validated biomarkers. Although considerable efforts are being made to identify biomarkers that can meet some of these needs, progress has been slow, and the translational challenges have been reviewed elsewhere (Wallis et al., 2010Wallis R.S. Pai M. Menzies D. et al.Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice.Lancet. 2010; 375: 1920-1937Summary Full Text Full Text PDF PubMed Scopus (367) Google Scholar).Increased investments are necessary to support biomarker discovery, validation, and translation into clinical tools. Unfortunately, a recent analysis of the TB R&D funding landscape by Treatment Action Group showed a big gap between investment needed and actual expenditure on R&D. Donors, governments, and members of the Stop TB Partnership will need to device creative strategies to plug this gap.While the TB diagnostics R&D space has managed to attract over 50 companies and product developers, they will require technical and funding support to overcome the translational challenges shown in Table 1. Organizations such as Foundation for Innovative New Diagnostics (FIND), Geneva, Bill and Melinda Gates Foundation, World Health Organization, UNITAID, Global Laboratory Initiative, Stop TB Partnership's New Diagnostics Working Group, Critical Path Institute, PATH, McGill International TB Centre, and several academic partners have worked together to produce several reports that are of great relevance, including a technology and market landscape report, a needs assessment study, a consensus report on target product profiles of highest priority, a series of market analyses, and a series of articles which outline the characteristics of the next-generation assays, and translational challenges for product development. All of these are available on a website (www.tbfaqs.org) created to provide answers to the most frequently asked questions by TB product developers. Hopefully, these collective efforts will result in a more robust pipeline of tools that can overcome the translational challenges, and push the agenda towards the goal of TB elimination.DisclosuresThe author has no financial or industry conflicts to disclose. He serves as a consultant to the Bill and Melinda Gates Foundation, and on the scientific advisory committee of the Foundation for Innovative New Diagnostics (FIND), Geneva. Despite the long and hard battle against tuberculosis (TB), WHO estimated that 9 million people developed the disease in 2013, and nearly 1.5 million people died of TB (World Health Organization, 2014World Health Organization Global Tuberculosis Report 2014. WHO, Geneva2014: 1-289Google Scholar). To make matters worse, drug-resistance is a growing threat, and 3 out of 9 million TB cases are either not diagnosed, or not notified to TB control programs. But there is some good news from the perspective of new tool introduction. Slowly but surely, the landscape of TB technologies is changing (Pai and Schito, 2015Pai M. Schito M. Tuberculosis diagnostics in 2015: landscape, priorities, needs, and prospects.J. Infect. Dis. 2015; 211: S21-S28Crossref PubMed Scopus (132) Google Scholar). We now have a variety of new TB diagnostics, including rapid molecular tests (e.g. Xpert MTB/RIF, Cepheid Inc., USA) for detection as well as drug susceptibility testing (DST) (UNITAID, 2014UNITAID, 2014. Tuberculosis: Diagnostics Technology and Market Landscape, 3rd edition, in: Secretariat U., ed., WHO, Geneva, pp. 1–42.Google Scholar). We also have new TB drugs (e.g. bedaquiline and delamanid) on the market, and new TB drug regimens are expected within the next 2–3 years. These are major, exciting developments in the fight against a very ancient scourge. This article reviews the current best diagnostic tools available for TB diagnosis and monitoring, and describes the most important gaps, and translational challenges for developing innovative products that can meet the needs (Table 1). As shown in the Table, there are critical unmet needs that range from a simple, triage test for use in the community, to DST tools that can detect a range of mutations for several important drugs that will make up future drug regimens (Denkinger et al., 2015aDenkinger C.M. Kik S.V. Cirillo D. et al.Defining the needs for next generation assays for tuberculosis.J. Infect. Dis. 2015; 211: S29-S38Crossref PubMed Scopus (98) Google Scholar, Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar). For the next-generation DST tools, a big translational challenge is the paucity of good data on the correlation of mutations with phenotypic DST results and clinical outcomes and the association with cross-resistance (Solomon et al., 2015Solomon H. Yamaguchi K.D. Cirillo D. et al.Integration of published information into a resistance-associated mutation database for mycobacterium tuberculosis.J. Infect. Dis. 2015; 211: S50-S57Crossref PubMed Scopus (28) Google Scholar). This is particularly important to make sure that we have companion diagnostics for emerging TB drug regimens (Denkinger et al., 2015bDenkinger C.M. Dolinger D. Schito M. et al.Molecular drug susceptibility testing — defining assay characteristics for the use at the level of the microscopy center.J. Infect. Dis. 2015; 211: S39-S49Crossref PubMed Scopus (26) Google Scholar). The translational challenges associated with DST are reviewed elsewhere (Solomon et al., 2015Solomon H. Yamaguchi K.D. Cirillo D. et al.Integration of published information into a resistance-associated mutation database for mycobacterium tuberculosis.J. Infect. Dis. 2015; 211: S50-S57Crossref PubMed Scopus (28) Google Scholar). For the development of rapid triage tests, non-sputum based tests for active TB, highly predictive LTBI tests, and an accurate test for cure, we need validated biomarkers. Although considerable efforts are being made to identify biomarkers that can meet some of these needs, progress has been slow, and the translational challenges have been reviewed elsewhere (Wallis et al., 2010Wallis R.S. Pai M. Menzies D. et al.Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice.Lancet. 2010; 375: 1920-1937Summary Full Text Full Text PDF PubMed Scopus (367) Google Scholar). Increased investments are necessary to support biomarker discovery, validation, and translation into clinical tools. Unfortunately, a recent analysis of the TB R&D funding landscape by Treatment Action Group showed a big gap between investment needed and actual expenditure on R&D. Donors, governments, and members of the Stop TB Partnership will need to device creative strategies to plug this gap. While the TB diagnostics R&D space has managed to attract over 50 companies and product developers, they will require technical and funding support to overcome the translational challenges shown in Table 1. Organizations such as Foundation for Innovative New Diagnostics (FIND), Geneva, Bill and Melinda Gates Foundation, World Health Organization, UNITAID, Global Laboratory Initiative, Stop TB Partnership's New Diagnostics Working Group, Critical Path Institute, PATH, McGill International TB Centre, and several academic partners have worked together to produce several reports that are of great relevance, including a technology and market landscape report, a needs assessment study, a consensus report on target product profiles of highest priority, a series of market analyses, and a series of articles which outline the characteristics of the next-generation assays, and translational challenges for product development. All of these are available on a website (www.tbfaqs.org) created to provide answers to the most frequently asked questions by TB product developers. Hopefully, these collective efforts will result in a more robust pipeline of tools that can overcome the translational challenges, and push the agenda towards the goal of TB elimination. DisclosuresThe author has no financial or industry conflicts to disclose. He serves as a consultant to the Bill and Melinda Gates Foundation, and on the scientific advisory committee of the Foundation for Innovative New Diagnostics (FIND), Geneva. The author has no financial or industry conflicts to disclose. He serves as a consultant to the Bill and Melinda Gates Foundation, and on the scientific advisory committee of the Foundation for Innovative New Diagnostics (FIND), Geneva.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,978
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,151
Tête enseignante GPT0,442
Écart entre enseignants0,291 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2015
Routes d'admission2
Résumé présentoui

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