Completed Infection & Immunity Lungs & Breathing

Tuberculosis Treatment Trials

Funder MRC (part of UKRI)
Recipient organisation University College London Source-published name: University College London
Funding £3.7M
Period Mar 2015 — Dec 2021

In plain English

AI plain-English summary

Tuberculosis patients who feel better after two months are abandoning their six-month treatment, fuelling drug resistance and spreading disease. The Medical Research Council’s Clinical Trials Unit is running a series of international trials to find shorter, simpler regimens that patients will actually complete. The core problem is that current treatment for drug-sensitive TB works but takes too long. Many patients stop early, leading to relapse and transmission. For multi-drug resistant TB (MDR-TB), existing regimens are toxic, expensive, and often fail. Children have been largely excluded from TB treatment trials, leaving a critical evidence gap. If these trials succeed, a four-month or even shorter regimen could replace the standard six-month course, dramatically improving completion rates. For MDR-TB, a new regimen built around the first new TB drug licensed in 50 years—developed from a promising Bangladesh protocol—could replace the current toxic, year-long ordeal. This would directly reduce transmission in high-burden settings like sub-Saharan Africa, where TB cases surged after the HIV epidemic. Trials in children with limited disease, drug-resistant TB, and TB meningitis are planned or underway, aiming to close the paediatric evidence gap. The impact is not on infrastructure but on human survival and public health logistics in the world’s poorest regions.

View original technical description
Tuberculosis remains a major problem in many countries; in sub-Saharan Africa numbers of cases reported have increased dramatically following the advent of the HIV-AIDS epidemic. Treatment regimens for drug-sensitive tuberculosis which are highly effective under controlled trial conditions have been available for many years but need to be given for at least 6 months. Many patients fail to complete treatment since they feel much better after 2-3 months treatment. Shorter and simpler effective regimens are needed. The CTU recently completed two international multi-centre trials assessing the possibility of treatment shortening and/or simplifying treatment. A trial assessing ultra-short treatment has been funded and is planned to commence in the near future. There is an urgent need to find effective treatments for multi-drug resistant tuberculosis (MDR-TB), current regimens are given for long durations, are toxic, expensive and results are generally poor. A large multi-centre trial is currently in progress which is assessing a promising regimen which has been developed in Bangladesh; the trial includes two regimens containing the first new tuberculosis drug to be licensed for about 50 years. There have been very few trials of tuberculosis treatment in children, one such trial in children with limited disease is expected to start soon and trials in drug-resistant tuberculosis and TB-meningitis are planned.

View the original record at the funder ↗

Researchers

Andrew Nunn (Co-Investigator)Angela Crook (Principal Investigator)Ibrahim Abubakar (Co-Investigator)Patrick Phillips (Principal Investigator)Sarah Meredith (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

MICA: randomized trial of therapy shortening for minimal TB with new WHO-recommended doses and FDC drugs in African/Indian HIV+/HIV- children
New Drugs for MDR-TB
Two-month Regimens Using Novel Combinations to Augment Treatment Effectiveness for drug-sensitive Tuberculosis: the "TRUNCATE-TB" trial
A randomized trial to evaluate the toxicity and efficacy of 1200mg and 1800mg rifampicin daily for 4 months in the treatment of pulmonary tuberculosis
Short enhanced anti-tuberculosis and anti-thrombosis treatment for children with tuberculous meningitis

Original classification

Intramural

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.