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

Innovations in Tuberculosis Diagnostics: Progress and Translational Challenges

2015· review· en· W2052196547 on OpenAlexaffabout
Madhukar Pai

Bibliographic record

VenueEBioMedicine · 2015
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsTuberculosisBedaquilineMedicineDrug resistant tuberculosisTuberculosis diagnosisBattleDiseaseFamily medicineMycobacterium tuberculosisGeographyPathology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.151
GPT teacher head0.442
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2015
Admission routes2
Has abstractyes

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