万博体育官网

??Aiming to advance tuberculosis (TB) treatment standards from the current practice of “one-size-fits-all" to precision-guided individualised therapy, the PredictTB research team led by Prof Gerhard Walzl (Stellenbosch 万博体育官网 in South Africa) and Prof Clifton Barry III (US National Institutes of Health) set out in 2017 to investigate a set of criteria enabling improved treatment predictability and identification of patients eligible for treatment shortening. 

While the current TB standard therapy lasts six months, up to 80% of all TB patients are cured after four months. However, scientists do not know beforehand which patients belong to that group. Hence, gaining a better understanding of individual TB treatment response allowing for more personalised therapies and potentially shortened treatment duration is a critical step towards reducing drug resistance and disease burden in developing countries.

Extensive data generation paving the way for future relapse-specific TB biomarker discovery

In the past 5 ? years the PredictTB group, consisting of experts from Africa, Asia, Europe, and the United States, tested novel, patient-specific radiographic and microbiological biomarkers for early treatment stopping in a large proof-of-concept study in South Africa and China with close to 700 patients. Looking at an innovative combination of positron emission tomography (PET) and computed tomography (CT) scans paired with rapid molecular diagnostic tests, PredictTB has generated a wealth of clinical, imaging, and microbiological data.

“Although the early stopping criteria were shown not to be effective in achieving a safe reduction period of standard TB treatment from six to four months, the PredictTB study provides a wealth of information on PET/CT imaging and it is one of the largest studies ever conducted with PET/CTs on TB treatment with such a long follow-up and well-defined clinical outcomes," summarises Walzl. He continues: “These imaging parameters may help us in the future to fine-tune and optimise early stopping criteria. Plus, this data will also contribute to gaining a better understanding of the factors that lead to failed or curative treatment strategies."

In addition, the study provides a lot of well-characterised patient samples. Together with available samples from other relapse studies, these samples will pave the way for large-scale, relapse-specific biomarker discovery experiments including gene expression, proteomics, and metabolomics measures. These future studies will help design the next generation of candidate biomarkers to be used in clinical trials aiming to streamline the evaluation of new drugs and will also contribute to improved treatment shortening approaches in the future.

The collected PredictTB datasets and samples will be made accessible to the wider research community and external investigators upon request (see further information on the PredictTB website) and after secondary aims of the study have been addressed by the consortium.

Alongside the scientific work plan, PredictTB also engaged in a variety of capacity building and training activities to support knowledge-sharing and create perspectives for emerging African scientists. For instance, these activities included a series of workshops with more than 200 delegates over the past five years, the training of PhD students, the establishment of a mentorship program and personal development plans. In addition, the group launched the PredictTB Learning Board, a tailor-made online platform containing curated material for career and skill development that can be used by mentors and mentees in the PredictTB mentoring scheme and other consortium members involved in capacity building.

For further information visit: www.predict-tb.com.

Funding Acknowledgement