TB is an airborne, infectious disease caused by the bacterium Mycobacterium tuberculosis. It usually affects the lungs, and symptoms include coughing, weight loss, night sweats, fever, and fatigue. Although it is both preventable and curable, it remains a leading cause of disease and death by infection, particularly in developing countries. TB treatment is tough; patients must take four antibiotics for at least six months. The length and complexity of this regimen mean that many patients do not take their treatment properly, or stop taking the drugs before the bacteria have been completely eradicated from the body. Thus poor compliance may well be driving a rise in multidrug-resistant (MDR) strains of TB that simply do not respond to the main first-line antibiotics. If the treatment regimen for standard TB is tough, the regimen for MDR TB is even tougher, taking upwards of two years and involving drugs that often cause severe side effects such as liver, skin and hearing problems. In addition, recent years have seen growing reports of extensively drug resistant (XDR) TB, which does not respond to a number of the core first and second line antibiotics.
There is therefore an urgent need to develop a more potent, yet patient-friendly, combination of drugs to tackle TB. However, very few new TB drugs have been developed in recent decades. Furthermore, to prevent TB from developing resistance, it must be treated with a combination of multiple drugs. Until now, new drug candidates were developed and added to the existing regimen one by one. As it takes at least six years to change one drug in the regimen by either substitution or addition, approving a new four-drug regimen through successive trials would take a quarter of a century. Shortening this period is a top priority for the fight against TB, but current clinical trial methodologies make it very difficult to evaluate the optimal doses and combinations of drugs.
PreDiCTing the best treatment regimens
We need a way to facilitate the complex decisions around which doses and combinations of new drugs should enter clinical trials, and that’s where the PreDiCT-TB project will focus its combined resources. PreDiCT-TB aims to develop an integrated set of laboratory-based models that will provide much-needed data to indicate the most appropriate doses and combinations of drugs for patients. In addition, the project will generate a comprehensive database of patient data from previous and on-going clinical trials for use as a reference for evaluating the performance of combination anti-TB drug regimens in these newly developed laboratory models. Ultimately, they aim to enable researchers to be able to use the information generated by the novel models to design better clinical trials involving TB patients. Therefore PreDiCT-TB brings together internationally-respected TB scientists and physicians with expertise in the biology, immunology and imaging of the disease, as well as those specialising in the behaviour of drugs in the body (pharmacokinetics), their interactions with one another (pharmacodynamics), and clinical trials.
A boost for TB patients
Today’s long, complex TB treatment regimen is simply not patient-friendly enough and potentially raises the risk of patients developing (and passing on to others) drug-resistant forms of the disease. By speeding up the development of better and shorter treatment regimens, PreDiCT-TB should dramatically increase the likelihood of patients completing the course of treatment successfully in future years.
New leads for the industry
By assessing combinations of new candidate drugs and optimising clinical trial design, PreDiCT-TB is set to revolutionise the speed and effectiveness of drug discovery and development in the field of TB.
Making good on a promise
Tackling TB is a high priority for governments worldwide; the international Stop TB Partnership has set the goal of eliminating TB as a global public health problem by 2050. The results of PreDiCT-TB are set to give a new impetus to efforts to deliver novel treatments against this deadly disease.