PreDiCT-TB

Model-based preclinical development of anti-tuberculosis drug combinations
Model-based preclinical development of anti-tuberculosis drug combinations

FACTS & FIGURES

Start Date
End Date
Call
IMI1 - Call 3
Grant agreement number
115337

Type of Action: 
RIA (Research and Innovation Action)

Contributions
IMI Funding
14 778 855
EFPIA in kind
9 296 106
Other
4 478 125
Total Cost
28 553 086

Summary

Tuberculosis is still a huge global public health issue and the single biggest killer amongst infectious diseases, particularly in developing countries. The treatment usually involves a combination of antibiotics, which poses a problem for companies that are used to developing one drug a time. Additionally, the current laboratory models used to predict the potential of new drug candidates have not been reliable, leading to a high failure rate in clinical trials. PreDiCT-TB project scientists developed a series of tools, which drug developers can use to increase the probability of them choosing the right drug-like compounds and getting them through clinical trials successfully. With that, the project laid the foundations for faster and better development of new TB medicines for the benefit of patients.

Tuberculosis (TB) is the single biggest killer among infectious diseases, particularly in developing countries. It infects over 10 million people worldwide every year and kills 1.7 million. Treatment takes at least six months, it usually involves a combination of 4-7 drugs, and many patients struggle to take their antibiotics properly, fuelling the rise of drug-resistant strains of the disease. Despite all this, few new drugs for TB have been developed and registered since the 1970s.

By bringing together academic researchers with expertise in the biology, immunology and imaging of the disease, as well as industry scientists specialising in drug development, IMI’s PreDiCT-TB project set out to speed up the search for new, more effective combinations of treatments to tackle this deadly disease. In particular, the project aimed to evaluate and improve the effectiveness of preclinical laboratory models in predicting the success of drug candidates in clinical trials, which has been a major bottleneck in TB drug development.

Comparison of preclinical and clinical data

In order to achieve that, the project created a large database of preclinical data with many different preclinical models, from simple experiments in broth through to animal models – the largest and most diverse preclinical dataset available in TB to date. The project team also undertook a comprehensive review of published data from clinical trials, and created a large database containing 31 clinical trials with more than 15 000 participants.

Furthermore, PreDiCT-TB scientists performed the first systematic quantitative comparisons of preclinical and clinical results in order to evaluate how effective different preclinical models are in predicting the success of drug candidates in clinical trials. The researchers took a mathematical modelling approach to integrating that information, putting it together in a way that can be used by drug developers to predict what will happen in clinical trials. Altogether, the project produced a series of tools, which drug developers can use to increase the probability of them choosing the right drug-like compounds and getting them through clinical trials successfully.

New biomarkers for diagnosing TB

The project also developed new biomarkers, which enable scientists to understand how the TB drug candidates work in preclinical and clinical trials. Currently scientists measure the effect of potential new treatments either by culturing TB bacteria from a patient-derived specimen, or by using DNA-based diagnostic tests. Growing a culture is very slow, taking up to three months before a result is available. DNA tests on the other hand are fast, but they remain positive even after the bacteria is dead, making them unsuitable for tracking the effectiveness of potential new treatments.

A new method fine-tuned by the PreDiCT-TB project is based on measuring ribosomal RNA. The RNA is easy to detect even when there are very few organisms present, and unlike the DNA, it does not survive long after antibiotics kill the bug. The method, called the mycobacterial load assay (MBLA), offers a much faster way to diagnose tuberculosis and has the potential to improve both the speed and effectiveness of preclinical and clinical TB trials.

Reducing the use of animals in research

PreDiCT-TB scientists have also done a lot of work to improve the design of preclinical studies and decrease the number of animals used when possible. Additionally, the project evaluated the effectiveness of different animal species in predicting the success of potential TB treatments. In some cases, this resulted in moving to the use of lower phylogenetic species such as zebrafish.

For example, one of the SMEs in the project developed a zebrafish high-throughput screening model. The model enables them to screen very large numbers of TB drugs, and combinations of drugs, very efficiently. The tool may become very valuable in early phases of clinical development.

For the benefit of industry, academia and SMEs

The industry benefited from the new resources and tools developed within the project, which they could not have developed on their own. They also benefited from the network of TB scientists, which will continue to exist beyond the lifetime of the project.

The academic community likewise benefitted from contacts with the pharmaceutical industry and SMEs, as well as from access to industry data and compounds. Thanks to this project, they learned a great deal about the drug development process and gained an understanding of how their models and skills could be used to enable better and faster drug development.

The SMEs in the project also benefitted from the network and resources within the project. Furthermore, contacts with the pharmaceutical industry enabled them to learn about the needs of their customers and gave them the necessary support and insights to develop and optimise important preclinical tools, such as the zebrafish high-throughput screening model.

What’s next

All of the key resources generated by the project are, or soon will be, publicly available. The databases of preclinical data and clinical data – both of which are a key resource for the wider TB drug development community – have permanent homes that will be funded for at least the next decade.

Some of the technologies developed within the project, such as the MBLA, have been made available to any external scientific group at a cost. Finally, a lot of the code base for many of the modelling and simulation tools that have been developed will be soon available through publicly accessible websites.

Read the interview with project coordinators

Achievements & News

‘This has been a critical investment for European researchers’ – an interview with the PreDiCT-TB project coordinators
October 2018

Tuberculosis (TB) is still a huge global public health issue and the single biggest killer amongst infectious diseases, particularly in developing countries. The treatment usually involves a combination of antibiotics, which poses a problem for companies that are used to developing one drug a time. Additionally, the current laboratory models used to predict the potential of new drug candidates have not been reliable, leading to a high failure rate in clinical trials. PreDiCT-TB project scientists developed a series of tools, which drug developers can use to increase the probability of them choosing the right drug-like compounds and getting them through clinical trials successfully. ###With that, the project laid the foundations for faster and better development of new TB medicines for the benefit of patients. In an interview with the IMI Programme Office, EFPIA project coordinator Justin Green of GSK and academic coordinator Gerry Davies of the University of Liverpool, explain how PreDiCT-TB transformed the tuberculosis field and could speed up the development of new treatments for the benefit of patients. ‘[Our project] changed people’s attitude towards drug development in TB,’ said Davies. ‘Some of the techniques that we brought in are simply best practices in modern drug development and we have applied them to a field in which perhaps that’s been lacking for decades. We can certainly say that some of the tools that we developed – some of the statistical modelling tools and some of the technologies in the lab – have the potential to be transformative.’

New PreDiCT-TB test to speed up tuberculosis drug development
December 2016

Current methods of diagnosing tuberculosis (TB) and measuring the effect of potential new treatments in clinical and pre-clinical trials depend on either culturing TB bacteria from a patient-derived specimen, or DNA-based diagnostic tests. Growing a culture is very slow, taking up to three months before a result is available.### DNA tests on the other hand are fast, but they remain positive even after the bacteria is dead, making them unsuitable for tracking the effectiveness of potential new treatments. A new method fine-tuned by the PreDiCT-TB project offers a much faster way to diagnose tuberculosis and has the potential to improve both the speed and effectiveness of preclinical and clinical TB trials. The method is based on measuring ribosomal RNA, which is part of the structure of the organisms’ protein synthesis machinery. The RNA is easy to detect even when there are very few organisms present, and unlike the DNA, it does not survive long after the bug is killed by antibiotics. The method gives an answer in as little as four hours, and might also be able to identify a population of more persistent, antibiotic-resistant bacteria. Although the test was initially developed prior to PreDiCT-TB, the work done within the consortium enabled the creation of a test that is easier to use and is more reliable. 'PreDiCT-TB has increased the speed with which we have been able to apply this test to defined regimens and the consortium has allowed us to draw on the expertise from across the community,' said Justin Green, PreDiCT-TB project coordinator. ‘We believe this test may contribute to the speeding up of the evaluation of new drugs. It also has the potential to improve clinical management of hard to treat patients.’ PreDiCT-TB aims to speed up the search for new, more effective combinations of treatments to tackle TB, and is one of the world’s only initiatives focused on tackling pre-clinical research barriers to the discovery and development of new drug combinations for this deadly disease.

Predict-TB marks world TB day
March 2016

On 24 March, World TB Day, IMI’s tuberculosis (TB) project PreDiCT-TB highlighted how it is contributing to efforts to ‘unite to end TB’ – the motto of this year’s events. According to the World Health Organization (WHO), in 2014, 9.6 million people fell ill with TB and 1.5 million died; most of these cases were in low and middle income countries.### Treatment still requires six months or more of combinations of antibiotics to ensure a complete cure and more effective drugs are urgently needed to shorten treatment. The goal of the PreDiCT-TB consortium is to find the most rapid and reliable ways of identifying the most potent combinations of new drugs and hasten their arrival in the clinic. On World TB Day, PreDiCT-TB published new materials, including articles, videos and images explaining the project’s activities and progress.

PreDiCT-TB marks World TB Day
March 2015

Tuberculosis (TB) project PreDiCT-TB marked World TB Day on 24 March with a series of articles, videos and tweets on its activities. TB is an infectious bacterial disease. Although it is both preventable and treatable, in 2013, 9 million people fell ill with TB and 1.5 million died.### The goal of PreDiCT-TB is to find the most rapid and reliable ways of identifying the most potent combinations of new drugs and hasten their arrival in the clinic. Material released on World TB Day included:

  • An article on the University of St Andrews’s work in the project. The team looks at ways of detecting TB bacteria that are naturally resistant to current treatment and using this information to understand and predict the effect of current and future treatment regimens.
  • Videos from the team at the University of Liverpool, including an overview of the project and a discussion on historical clinical trial data.
  • A day in the life of Sanofi’s Lucie Eckenberg-Friedlander, who heads up the project’s work package on data management.
  • An article on the team at École Polytechnique Fédérale de Lausanne (EPFL), which is developing tools to assess the efficacy of TB drugs.
  • An interview with the project’s academic coordinator, Dr Gerry Davies of the University of Liverpool.

IMI and C-Path tuberculosis projects sign Memorandum of Understanding
March 2013

IMI’s PreDiCT-TB and C-Path’s CPTR projects have signed a Memorandum of Understanding to coordinate their work in developing new and effective treatment regimens for tuberculosis (TB).### TB is a leading cause of death worldwide, with nearly 9 million new cases and 1.4 million deaths reported every year. A major challenge for those tackling the disease is the length of TB treatment – even standard cases require at least six months of treatment with a multi-drug regimen, while drug-resistant strains of the disease can take two years to treat. Both PreDiCT-TB and CPTR are working to speed up the development of shorter, more patient-friendly treatment regimens. By working together, the teams will be able to combine their forces to take on the challenges in this area more effectively.

Participants Show participants on map

EFPIA companies
  • Glaxosmithkline Investigacion Y Desarrollo SL, Tres Cantos, Spain
  • Janssen Pharmaceutica Nv, Beerse, Belgium
  • Sanofi-Aventis Recherche & Developpement, Chilly Mazarin, France
Universities, research organisations, public bodies, non-profit groups
  • Department of Health, Leeds, United Kingdom
  • Ecole Polytechnique Federale De Lausanne, Lausanne, Switzerland
  • Erasmus Universitair Medisch Centrum Rotterdam, Rotterdam, Netherlands
  • Institut Pasteur, Paris, France
  • Liverpool School of Tropical Medicine, Liverpool, United Kingdom
  • Max-Planck-Gesellschaft Zur Forderung Der Wissenschaften Ev, Munich, Germany
  • St George’s Hospital Medical School (trading as St George's University of London), London, United Kingdom
  • Stichting Vumc, Amsterdam, Netherlands
  • The University Court of the University of St Andrews, St Andrews, Fife, United Kingdom
  • The University Of Liverpool, Liverpool, United Kingdom
  • Universidad Carlos III de Madrid, Getafe (Madrid), Spain
  • University College London, London, United Kingdom
  • University of Leicester, Leicester, United Kingdom
  • University of Sussex, Brighton, United Kingdom
  • Uppsala Universitet, Uppsala, Sweden
Small and medium-sized enterprises (SMEs)
  • Microsens Medtech Ltd, London, United Kingdom
  • Zf-Screens BV, Leiden, Netherlands

CONTACT

Project coordinator
Justin Green
GLAXOSMITHKLINE INVESTIGACION Y DESARROLLO SL
United Kingdom
+44 20 89902092
justin.a.green[at]gsk.com
Managing entity
Gerry Davies
University of Liverpool
United Kingdom
+44 151 4280422
gerrydavies[at]doctors.org.uk