Professor Koul is also vice president of Global Public Health Discovery Research at J&J, and played a key role in the discovery and development of Bedaquiline - the only treatment for multidrug-resistant TB with a novel mode of action developed in almost 50 years. Bedaquiline is on WHO’s list of essential medicines both for adult as well as paediatric TB.
In this Q&A, Dr Richard Wall and Dr Sherif Abouelhadid ask Professor Koul about the threat of antimicrobial resistance (AMR); why we need new treatments for TB; and how the new J&J Satellite Centre for Global Health Discovery at LSHTM will help to expedite future drug discovery.
1. Why do we need new treatments against Tuberculosis (TB)?
“In 2020, TB was the second deadliest infectious killer, after COVID-19. TB causes 1.5 million deaths globally and there are more than 10 million TB infections every year. An even bigger challenge is the emergence of multi-drug-resistant TB (MDR-TB). MDR-TB remains a major public health crisis with mortalities in more than 50% of cases and is also emerging as a threat to global health security. In short, we urgently need new TB drugs and vaccines.”
2. The Satellite Centre for Global Health Discovery at LSHTM is a joint venture between J&J and LSHTM, what are the goals of this academia-industry collaboration?
“It is clear that there is a big innovation gap in the field of neglected diseases. In order to address this, J&J have built a new partnership model called the Centres for Global Health Discovery (CGHD) with labs from major public health academic centres across the world, including LSHTM.
The aim is to combine the breadth and depth of J&J resources in translational sciences, including discovery research, with the deep scientific and diseases-specific expertise of these leading institutions to advance early-stage discovery and the translational drug pipeline. To date, the key focus areas for these CGHDs have been TB, AMR and Dengue. Our centre at LSHTM specifically supports the discovery of new therapeutics so as to improve drug treatment options for TB.”
3. What is the link between TB bacteria (M. tuberculosis)’ metabolism and drug discovery?
“Bacterial energy metabolism or associated pathways have proven to be exceptional targets for TB drug discovery. The idea to kill TB bacterium via this mechanism has been very simple: to starve the bacteria of their energy sources (ATP) and shutdown the bacterial power grid (OxPhos) via chemical inhibition. Bedaquiline targets energy metabolism by inhibiting the function of ATP synthase. My research has shown how energy is key for TB survival and how drug molecules can be designed that selectively targeted the bacterium without impacting human cells. This was a serendipitous finding that revolutionized TB drug discovery and gave us new and novel drug targets.”
4. Are there any previous successes in developing drugs based on the vulnerabilities in TB metabolism?
“Metabolism has proven to be an exceptional target for TB drugs. Other than bedaquiline, drugs such as thioridazine and pyrazinamide highlight the potential for targeting the metabolic pathways, like proton motive force, of M. tuberculosis as a strategy for developing new TB treatments. These drugs have been shown to be highly sterilizing.”
5. What is the future of TB drug discovery and how does Artificial intelligence (AI) come to help in this field?
“The future of TB drug discovery involves the continued search for new and innovative treatments to address the challenges of drug resistance and reducing the duration of TB treatment, ideally as a single pill in a fixed-dose combination. This involves the exploration of new targets, novel drug discovery platforms and a clear understanding of disease pathogenesis including early surrogate biomarkers for cure. These challenges are compounded by the fact that developing a new drug costs between $1-2 billion and takes more than 12 years.
However, we have to embrace new tools like AI as it can assist with the early stages of medicinal chemistry optimisation, help us understand the biology of TB bacteria and, coupled to data sciences, can be used to effectively run clinical proof-of-concept studies. Our machine learning models can help integrate vast amounts of biochemical and omics data as well as help us screen better compound libraries against TB. We need to be fast, agile, and highly innovative. I believe, our lab at LSHTM is gearing towards that!”
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