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Adherence to treatment

Developing effective drug regimens is just one part of effective TB treatment. The course of treatment is long, may cause side effects, and non-adherence can lead to new antibiotic resistance.  The same is true of treating latent TB, when individuals have no symptoms. Supporting people in taking their full course of treatment, and understanding reasons for non-adherence, are therefore important.

Example outputs: 

  • All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era. (2020) Stagg, H. R. et al. ERJ Open Res 6(4) https://doi.org/10.1183/23120541.00315-2020
  • IMPACT study on intervening with a manualised package to achieve treatment adherence in people with tuberculosis: protocol paper for a mixed-methods study, including a pilot randomised controlled trial.  (2019) Stagg, H. R.et al.  BMJ Open 9(12): e032760 https://doi.org/10.1136/bmjopen-2019-032760
  • Measuring and reporting treatment adherence: What can we learn by comparing two respiratory conditions? (2020) Tibble, H. et al. Br J Clin Pharmacol https://doi.org/10.1111/bcp.14458
  • Protocol for a systematic review of treatment adherence for HIV, hepatitis C and tuberculosis among homeless populations. (2020) Johnson, L. et al. Syst Rev 9(1): 211 https://doi.org/10.1186/s13643-020-01470-y
  • The IMPACT study - Voices from the Front Line: Presentation for World TB Day 2020 (Slideshare)

Projects: IMPACT, RID-TB

People: Ibrahim Abubakar, Amy Clarke, Marcia Darvell, Rob Horne, Annie Jones, Marc Lipman, Lele Rangaka  

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Ancient TB

TB is an ancient disease, and also immensely significant in our more recent history.  New DNA and lipid technologies allow TB disease to be identified in archaeological samples.

Example outputs: 

  • Oldest evidence of tuberculosis in Argentina: A multidisciplinary investigation in an adult male skeleton from Saujil, Tinogasta, Catamarca (905-1030 CE). (2020) Luna, L. H. et al. Tuberculosis (Edinb) 125: 101995 https://doi.org/10.1016/j.tube.2020.101995
  • Verification of tuberculosis infection among Vac mummies (18th century CE, Hungary) based on lipid biomarker profiling with a new HPLC-HESI-MS approach. (2020) Varadi, O. A. et al. Tuberculosis (Edinb) 126: 102037 https://doi.org/10.1016/j.tube.2020.102037

People: Helen Donoghue

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Arts and humanities

TB has been part of the fabric of life in the UK for many centuries, and therefore has a presence in our culture, which we are keen to explore.

People: John Mullan

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Behavioural research

 

Example outputs:

  • IMPACT study on intervening with a manualised package to achieve treatment adherence in people with tuberculosis: protocol paper for a mixed-methods study, including a pilot randomised controlled trial.  (2019) Stagg, H. R.et al.  BMJ Open 9(12): e032760 https://doi.org/10.1136/bmjopen-2019-032760

Projects: IMPACT, RID-TB

People: Amy Clarke, Rob Horne, Annie Jones

See also: Adherence to treatment

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Biology of host and pathogen

Sub-themes: Computational Genomics; Gene regulation; Immunology; Pharmacology

People: Kristine Arnvig, Francois Balloux, Frank Kloprogge, Camus Nimmo, Gillian Tomlinson, Lucy van Dorp

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Biomarkers

The body is complex, and infection and our immune response to it are complex processes that we only partly understand.  However, we can now collect enormous amounts of information about the activity going on in our bodies, and use computers to identify signatures that are found (for example) in a group of people with known disease, but not in people we know not to be infected.  These signatures can then be used as biomarkers for disease in people of unknown status.  For example, this approach is analysing mRNA expression in the blood for biomarker signatures that identify people who don’t yet have clinical TB, but in whom TB bacteria that were latent/inactive have now become active. In addition, we are investigating TB-specific cytokine profiles that may aid to distinguish between latent and active TB, as well as functional T cell profiling. 

Example outputs:

  • Blood transcriptional biomarkers for active pulmonary tuberculosis in a high-burden setting: a prospective, observational, diagnostic accuracy study. (2020) Turner, C. T. et al. Lancet Respir Med 8(4): 407-419 https://doi.org/10.1016/S2213-2600(19)30469-2
  • Blood transcriptomic biomarkers for tuberculosis screening: time to redefine our target populations? (2021) Gupta, R.K. and M. Noursadeghi. Lancet Glob Health https://doi.org/10.1016/S2214-109X(21)00088-7
  • Blood transcriptomic stratification of short-term risk in contacts of tuberculosis. (2020) Roe, J. et al. Clin Infect Dis 70(5): 731-737 https://doi.org/10.1093/cid/ciz252
  • Concise whole blood transcriptional signatures for incipient tuberculosis: a systematic review and patient-level pooled meta-analysis. (2020) Gupta, R. K. et al. Lancet Respir Med 8(4): 395-406 https://doi.org/10.1016/S2213-2600(19)30282-6
  • Mycobacteria-Specific Mono- and Polyfunctional CD4+ T Cell Profiles in Children With Latent and Active Tuberculosis: A Prospective Proof-of-Concept Study.  (2019) Tebruegge, M. et al.  Front Immunol 10: 431 https://doi.org/10.3389/fimmu.2019.00431.

People: Ibrahim Abubakar, Rishi Gupta, Isobella Honeyborne, Maddy Noursadeghi,  Marc Tebruegge

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Capacity development & training

Most TB is found in the Global South, where overall scientific infrastructure and support is most insecure for multiple historical and economic reasons.  We strongly support the idea that these countries should be enabled to develop high quality science with highly trained workforces.  We are both part of large programmes explicitly aiming to improve capacity development, and we also bring capacity development into other work wherever we can. As a university, we see the importance of academic and practical education, and have expertise in delivering it.  This includes supporting individuals to visit UCL for varying periods of time, carrying out workshops in London and abroad, training laboratory workers in diagnostic laboratories, and being part of bigger capacity development programmes.

Example outputs:

Projects: PanACEA; UK-Korean partnership for a TB cohort

People: Frank Kloprogge, Tim McHugh, Ali Zumla 

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Clinical management

Although standard treatments are published for patients with TB, working with individual patients to apply those treatments is a long process that is often far from smooth.  TB as a disease is different in every patient in terms of where the infection is active, whether the bacteria are resistant to any antibiotics, how the patient responds to drugs they are given, their personal situation, and other complicating conditions or factors.  Treatment is long, and many of these factors may change, while stopping the treatment early may lead to new drug resistance developing. All this happens in the context of a changing NHS and Social Services with limited resources. Clinical management is therefore never routine, and developing processes that are flexible and robust enough is challenging.

Example outputs:

People: Helen Booth, Hanif Esmail, Marc Lipman, Maddy Noursadeghi, Jacqui White

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Clinical trials

The way we can improve treatment for TB, and be confident that it will be safe and effective, is through stringent clinical trials.  For TB, which is a disease that can be slow to develop and to treat, where the drug resistance patterns are changing, and manifests most in the poorest parts of the world, these trials are particularly challenging. They involve a large team of people with different skills over a long period of time, so are costly, and those we run have to be carefully selected.  The MRC Clinical Trials Unit at UCL – which formed originally as the MRC Tuberculosis Research Unit in 1948 has an unprecedented track record in TB trials.  As well as their leadership, management,  statistical and analysis expertise, they work with others at UCL who carry out TB microbiology and train and monitor participating laboratories, and an army of people throughout the world who recruit and work with the trial participants.  UCL also works with other trial sponsors such as the Global TB Alliance, and the University of Stellenbosch.

Sub-themes: Clinical trial design

Example outputs:

Projects: SimpliciTB, STREAM 2.0, TB-CHAMP, TB-PRACTECAL, ZeNix; (MRC-CTU TB project page)

People: Suzanne AndersonRobindra Basu Roy, Angela Crook, Hanif Esmail, Diana GibbRuth Goodall, Tim McHughSarah Meredith, Andrew Nunn, Lele RangakaAnna Turkova, Conor Tweed 

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Computational Genomics

Studying the spread of M. tuberculosis strains can identify local outbreaks, identify sub-strains with particular properties, inform on genotypic predictors of disease pathology, tell us how drug resistance develops and spreads, and inform us about human history. Computational genomics relies on the ability to differentiate isolates based on their evolutionary relatedness, typically employing the fields of phylogenetics and population genomics.  These days, this is mostly done through Whole Genome Sequencing.

Example outputs:

  • Dynamics of within-host Mycobacterium tuberculosis diversity and heteroresistance during treatment. (2020) Nimmo, C. et al. EBioMedicine 55: 102747 https://doi.org/10.1016/j.ebiom.2020.102747
  • Population-level emergence of bedaquiline and clofazimine resistance-associated variants among patients with drug-resistant tuberculosis in southern Africa: a phenotypic and phylogenetic analysis. (2020) Nimmo, C. et al. Lancet Microbe 1(4): e165-e174 https://doi.org/10.1016/S2666-5247(20)30031-8

Projects:

People: Francois Balloux, Camus Nimmo, Lucy van Dorp

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Diagnostics

Despite many technical advances in diagnostics in the last two decades diagnosing TB is often challenging, especially in children. Existing tests have suboptimal sensitivity, which means that many TB patients have false-negative results. Diagnosing TB in children has additional challenges, as collecting adequate samples is often difficult and most children have paucibacillary disease (meaning that few mycobacteria are present in their clinical samples, making it hard to detect them). We are conducting studies on existing immune-based TB tests, including the tuberculin skin test and interferon-gamma release assays, and are working towards designing novel immunological TB tests that perform better across all age groups.

Example outputs:

  • Diagnostic accuracy of QuantiFERON-TB Gold Plus assays in children and adolescents with tuberculosis disease. (2020) Soler-Garcia, A. et al.  J Pediatr 223: 212-215 e211 https://doi.org/10.1016/j.jpeds.2020.02.025
  • Tuberculosis disease in children and adolescents on therapy with antitumor necrosis factor-a agents: A collaborative, multicenter Paediatric Tuberculosis Network European Trials Group (ptbnet) study. (2020) Noguera-Julian, A. et al. Clin Infect Dis 71(10): 2561-2569 https://doi.org/10.1093/cid/ciz1138

Projects:

  • SimpliciTB
  • TB-PRACTECAL
  • ZeNix
  • Comparison of Cepheid Xpert MTB/XDR and GenoScreen Deeplex Myc-TB for MDR and XDR M. tuberculosis (Giovanni Satta).  

People: Tim McHugh, Giovanni SattaMarc Tebruegge

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Digital health

Example outputs:

  • Knowledge, attitudes, and behaviors on utilizing mobile health technology for TB in Indonesia: A qualitative pilot study. (2020) Aisyah, D. N. et al. Front Public Health 8: 531514 https://doi.org/10.3389/fpubh.2020.531514
  • Management and control of tuberculosis control in socially complex groups: a research programme including three RCTs. (2020) Story, A., et al. Programme Grants for Applied Research 8(9) https://doi.org/http://doi.org/10.3310/pgfar08090

Projects:

  • TB Mentor app for clinical decision support

People: Hanif Esmail, Andrew Hayward, Patty Kostkova, Al Story 

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Drug development

After the success of early drugs against TB, starting in the 1940s with streptomycin, there was a long period where no new antibiotics were developed, due to lower priority, cost and time to develop these, and lack of biological understanding. The rise of antibiotic resistance has shown this to be short-sighted, and at last there is a renewed pipeline of drugs, some of which have moved into clinical use.  We are looking for novel drugs, coming both from the biology, and also from direction of large collections of chemical derivatives.

Example outputs:

  • Carprofen elicits pleiotropic mechanisms of bactericidal action with the potential to reverse antimicrobial drug resistance in tuberculosis. (2020) Maitra, A. et al. J Antimicrob Chemother 75(11): 3194-3201 https://doi.org/10.1093/jac/dkaa307
  • Ertapenem and Faropenem against Mycobacterium tuberculosis: in vitro testing and comparison by macro and microdilution. (2020) Gonzalo, X. et al. BMC Microbiol 20(1): 271 https://doi.org/10.1186/s12866-020-01954-w
  • Improving the potency of N-Aryl-2,5-dimethylpyrroles against multidrug-resistant and intracellular mycobacteria. (2020) Touitou, M. et al. ACS Med Chem Lett 11(5): 638-644 https://doi.org/10.1021/acsmedchemlett.9b00515
  • Polymersomes eradicating intracellular bacteria. (2020) Fenaroli, F. et al. ACS Nano 14(7): 8287-8298 https://doi.org/10.1021/acsnano.0c01870
  • Role of whole-genome sequencing in characterizing the mechanism of action of para-aminosalicylic acid and its resistance. (2020) Satta, G. et al. Antimicrob Agents Chemother 64(9) https://doi.org/10.1128/AAC.00675-20

People: Dimitris Evangelopoulos, Tim McHugh, Mat Todd

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Drug-resistant TB

Antibiotics are one of the cornerstones of the modern world, curing once-fatal diseases, and this is true for TB.  Yet resistance to these antibiotics develops  - in the case of M. tuberculosis, through the bacteria acquiring mutations in their chromosomes one at a time.  Reducing the risk of this happening is one major reason that TB is always treated with combination drug therapy. With TB the problem is worsened by the long period of treatment and side effects of drugs, which can lead to patients not completing their full courses. It is an aspect of TB that affects almost everything else: diagnosis, clinical management, and drug development to name a few.

People and sub-themes:

Example outputs:

Projects: SimpliciTB, STREAM 2.0, TB-CHAMP, TB-PRACTECAL, ZeNix

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Gene regulation

Although we have known the genetic structure of Mycobacterium tuberculosis for over 20 years, much of its biology relates to how and when genes are switched on and off, but our understanding of the underlying mechanisms remains incomplete.  The development of high-throughput sequencing (HTS) techniques, has revealed the abundance and importance of regulatory RNAs such as small RNAs and so-called riboswitches in bacterial gene expression control, and we can now monitor expression of all genes in an effort to understand their role in different activity states and in different locations within the host.

Example outputs:

People: Kristine Arnvig

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Host-directed therapies

Classically we think of treating bacterial disease with antibiotics – molecules that kill or damage the bacteria, and ideally don’t affect the patient at all. A complementary approach is to use molecules that modulate the host’s immune response. An effective immune response aims to kill pathogen but not its own cells. Pathology caused by infectious agents can either be a direct effect of the pathogen, or arise indirectly from an inappropriate immune response that causes damage. Host-directed therapies, therefore, can both be developed to stimulate the immune response, and dampen autoimmune damage depending on what is needed.

Example outputs:

Projects:

People: Ali Zumla

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Immunology

Example outputs:

Projects:

People: David Lowe, Gillian Tomlinson

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Latent and sub-clinical TB

When people are infected with M. tuberculosis, if not cleared by the immune response, the bacteria will most often stay in the body – often the lungs – in a quiescent state.  At some point, which could be soon after infection or many years later, it can reactivate to cause disease.  Only 10% of infections move to disease, so most infection is latent.  Identifying people with latent infection, and those where the bacteria are starting to reactivate but not yet clinically apparent, is an important part of controlling disease. However this is not only technically difficult, but it also raises issues of what is appropriate to do, and often requires engaging with particular at-risk communities.

Example outputs: 

  • Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings. (2020) Gupta, R. K. et al. Nat Med 26(12): 1941-1949 https://doi.org/10.1038/s41591-020-1076-0
  • Exaggerated IL-17A activity in human in vivo recall responses discriminates active tuberculosis from latent infection and cured disease. (2021) Pollara, G. et al. Sci Transl Med 13(592) https://doi.org/10.1126/scitranslmed.abg7673
  • The relationship between social risk factors and latent tuberculosis infection among individuals residing in England: a cross-sectional study. (2020) Lule, S. A. et al. BMJ Glob Health 5(12) https://doi.org/10.1136/bmjgh-2020-003550
  • Subclinical tuberculosis disease - a review and analysis of prevalence surveys to inform definitions, burden, associations and screening methodology. (2020) Frascella, B. et al. Clin Infect Dis https://doi.org/10.1093/cid/ciaa1402

Projects: RID-TB

People: Ibrahim Abubakar, Hanif Esmail, Rishi Gupta, Marc Lipman, Maddy Noursadeghi, Gabriele Pollara, Lele Rangaka, Ali Zumla 

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Migrant TB / TB in mobile populations

Like most infectious diseases, TB is more prevalent in populations who have fewer resources, less access to good and stable healthcare.  Furthermore, the long periods needed for treatment means that mobile populations of all sorts are less likely to enter a treatment programme, or are liable to default, while migrants are more likely to come from countries where TB is endemic.  Yet lack of adequate treatment not only affects those individuals, but their communities and the wider public. We have been working to support different mobile populations, such as migrants, the homeless, and people in prison.

Example outputs:

Projects:

People: Ibrahim AbubakarRob Aldridge, Andrew Hayward, Lele Rangaka, Al Story 

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Non-tuberculous mycobacterial disease (NTMs)

Although TB, caused by Mycobacterium tuberculosis and other highly related organisms in what is called the M. tuberculosis complex, may be considered the most important disease caused by mycobacteria, other mycobacteria do cause significant human disease. These are grouped essentially as ‘everything that is not tuberculosis or leprosy: non-tuberculous mycobacteria (NTMs).  These mycobacteria, such as M. abscessus  and M. avium, survive in the environment or other animals, and disease in humans is usually opportunistic.  However these infections are increasingly common and are seen in people who are generally not thought of as being at risk of such infections. NTM infections can be debilitating, and hard to diagnose and treat. Our work looks at both at how these bacteria (including M. abscessus, M. avium, M. marinum and M. ulcerans) cause disease, and how infections can be effectively managed in patients.

Example outputs:

  • Cross-transmission is not the source of new Mycobacterium abscessus infections in a multicenter cohort of cystic fibrosis patients. (2020) Doyle, R. M. et al. Clin Infect Dis 70(9): 1855-1864 https://doi.org/10.1093/cid/ciz526
  • Current and future management of non-tuberculous mycobacterial pulmonary disease (NTM-PD) in the UK. (2020) Lipman, M. et al. BMJ Open Respir Res 7(1) https://doi.org/10.1136/bmjresp-2020-000591
  • Engineered bacteriophages for treatment of a patient with a disseminated drug-resistant Mycobacterium abscessus. (2019) Dedrick, R. M. et al. Nat Med 25(5): 730-733 https://doi.org/10.1038/s41591-019-0437-z
  • Mycobacterium ulcerans-specific immune response after immunisation with bacillus Calmette-Guerin (BCG) vaccine. (2021) Pittet, L. F. et al. Vaccine 39(4): 652-657 https://doi.org/10.1016/j.vaccine.2020.11.045
  • Interferon-gamma release assays differentiate between Mycobacterium avium complex and tuberculous lymphadenitis in children. Tebruegge M et al. Journal of Pediatrics 2021 (in press).

Projects:

  • European Non-tuberculouS Mycobacterial Lymphadenitis in childrEn (ENSeMBLE) study (Marc Tebruegge)
  • Evolution of mycobacterial drug resistance (Naomi Fuller, Tim McHugh)
  • M. abscessus rapid diagnosis of resistance with whole genome sequencing (Giovanni Satta, Garth Dixon)
  • M. abscessus new treatment options, including bacteriophages (Giovanni Satta)
  • Post-transcriptional regulation in M. abscessus (Kristine Arnvig)
  • The Hollow-Fibre Model of M. abscessus disease to test new antibiotics and combination therapy (Steve Morris-Jones, Giovanni Satta)

People: Ibrahim Abubakar, Kristine Arnvig, Helen BoothHanif Esmail, Naomi Fuller, Frank Kloprogge, David Lowe, Marc Lipman, Tim McHugh, Rob Miller, Steve Morris-Jones, Giovanni Satta, Helen SpencerMarc Tebruegge, Gillian Tomlinson, Jacqui White

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Pharmacology

Determining the best combinations and levels of drugs for TB treatment to effectively kill bacteria, and minimise side effects, is challenging. By investigating the relationship between how much drug is used and how effective they are  (the pharmacokinetic-pharmacodynamic relationships), we can optimise the combinations of drugs that we use to treat TB. We use two approaches: the first is the hollow-fibre laboratory model, in which we investigate drug effects of treatment combinations against M. tuberculosis in a controlled way by mimicking antibiotic profiles to human conditions. A second approach is to evaluate antibiotic effects of drug combinations in the context of the whole body and immune system using data from patients.

Example outputs:

  • Exploring a combined biomarker for tuberculosis treatment response: protocol for a prospective observational cohort study. (2021) Kloprogge, F et al.. BMJ Open 11(7): e052885 https://doi.org/10.1136/bmjopen-2021-052885
  • Longitudinal pharmacokinetic-pharmacodynamic biomarkers correlate with treatment outcome in drug-sensitive pulmonary tuberculosis: A population pharmacokinetic-pharmacodynamic analysis. (2020) Kloprogge, F., et al. Open Forum Infect Dis 7(7): ofaa218 https://doi.org/10.1093/ofid/ofaa218
  • Can phenotypic data complement our understanding of antimycobacterial effects for drug combinations?  (2019) Kloprogge, F. et al.  J Antimicrob Chemother 74(12): 3530-3536 https://doi.org/10.1093/jac/dkz369
  • Mimicking in-vivo exposures to drug combinations in-vitro: anti-tuberculosis drugs in lung lesions and the hollow fiber model of infection.  (2019) Kloprogge, F. et al. Sci Rep 9(1): 13228 https://doi.org/10.1038/s41598-019-49556-5.

Projects: Dose rationale for antibiotic combination therapy in infectious diseases

People: Frank Kloprogge, Arundhati Maitra, Zahra Sadouki

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Whole genome sequencing

Sequencing the entire genome of M. tuberculosis has in recent years changed from a major enterprise, to being quick and a fraction of the previous cost.  Knowing the genome sequence allows the spread of the bacteria to be studied, the success of clinical trials to be measured, and genetic changes that lead to antibiotic resistance to be identified.

Example outputs:

Projects:

People: Francois Balloux, Louis Grandjean, Tim McHugh, Giovanni Satta, Lucy van Dorp

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