A new study is underway that uses the latest imaging software and cloud computing to develop better tools for fast screening of tuberculosis in animal models, and designing new combinations of drug treatments. This two-fold approach could help make positron emission tomography (PET) scans more sensitive, spotting tuberculosis disease signatures earlier, and make human treatments more patient-friendly.
Tuberculosis is sometimes referred to as the ‘forgotten disease’, but according to the World Health Organization, there were 8.8 million cases in 2010, and in 2009 almost 10 million children were orphaned as a result of parental deaths caused by the disease.
Researchers from the Carlos III University of Madrid (UC3M), Spain, along with 18 other partner institutions, are working on the PreDiCT-TB ('Model-based preclinical development of anti-tuberculosis drug combinations') project. The UC3M are the technological partner in this project. They are creating software specifically suited to integrate multiple data sets and create better resolution imaging for visualizing a complete rat or guinea pig lung. Cloud resources will be used to store and analyze the results.
PreDiCT-TB has received €14.8 million ($19 million) of EC FP7 funds from the Innovative Medicines Initiative (IMI), a public-private partnership aimed at removing bottlenecks in R&D that prevent good medicines from being developed. PreDiCT-TB is just one of IMI's wider goals. This year, it launched seven new projects to tackle major health research challenges including tuberculosis, autism, diabetes, drug and vaccine safety.
Today, the possibility of being cured of tuberculosis drops if the disease is ‘multi-drug resistant’ and treatment can take six months. The PreDiCT-TB project hopes to improve this by finding the most effective therapeutic drug combinations.
“The agent causing the disease - M.tuberculosis - is getting more and more resistant. Patients need to be treated with more than one drug at the time, and for a very long time… it’s a vicious circle,” said Elisabetta Vaudano, IMI scientific manager of the PreDiCT-TB project. “PreDiCT-TB will take a comprehensive model-based approach, synthesizing and integrating pre-clinical and clinical information. This is unique in the field.”
More information about the project can be found on the IMI website.
- Adrian Giordani