One million euros grant for smart antibiotic combinations – tackling resistant infections and antimicrobial resistance
Optimised antibiotic combinations can combat bacteria more effectively while also slowing the development of resistance. Scientists from five European countries are joining forces to identify such combinations and provide tools for others to do the same. The project is led by Professor Coen van Hasselt from Leiden University.
Each year, around 200 people in the Netherlands die from infections caused by antibiotic-resistant bacteria. Across Europe, the death toll rises to 35,000, and globally it is 700,000 – with numbers continuing to climb. These infections occur most often in hospitals, affecting patients with already weakened immune systems. One notorious culprit is MRSA, the so-called “hospital superbug”.
‘Whenever antibiotics are used, there’s always a risk of resistance developing,’ explains Van Hasselt, Professor of Pharmacology at the Leiden Academic Centre for Drug Research (LACDR). ‘Resistant bacteria survive treatment and continue to spread.’ Together with an international team of colleagues, he is working to find effective ways to tackle this problem.
The evolutionary arms race between humans and bacteria
The battle between humans and harmful bacteria is a constant evolutionary arms race. While new antibiotics are still being developed to outpace bacterial resistance, this is becoming increasingly challenging. Antibiotic combinations offer a promising strategy in this fight. ‘Some resistant bacteria can break down certain antibiotics, but this can be countered with a second drug that blocks the protein responsible for that breakdown,’ Van Hasselt explains.
Untapped potential of combination therapy
This kind of combination therapy is already in use, but the potential of combining antibiotics goes far beyond current applications. ‘Some antibiotics are highly effective but come with severe side effects, such as kidney or liver damage,’ says Van Hasselt. ‘Imagine if combining them with another drug allowed us to use much lower doses, while still effectively treating infections.’
‘Imagine needing less of a harmful antibiotic, and therefore reducing side effects, by combining it with another drug.’
Testing in patients isn’t straightforward
While promising results for antibiotic combinations can be achieved in the lab, testing them directly in infected patients is a big leap and rarely happens. As a result, such approaches remain underused by doctors. However, Van Hasselt and his colleagues from Germany, the UK, France, and Denmark have developed a safe way to explore optimal antibiotic combinations. They have secured over 1 million euros in funding from JPIAMR, a European initiative to combat antimicrobial resistance.
A computer model predicts the best therapy
What will the researchers do? ‘We’re combining lab experiments with a sophisticated computer model,’ explains Van Hasselt. ‘The model integrates everything we know about how antibiotics kill bacteria, how resistance develops, and how these drugs behave in the human body – including how they spread and how they are broken down.’ The model will generate predictions for optimal antibiotic combinations, including precise dosing schedules that might not occur to clinicians. ‘For instance, should the drugs be administered simultaneously or alternately? Should they be given in equal doses, or is there a better ratio?’
Predictions are tested in the lab – and then in patients
The researchers will take the model’s predictions back to the lab, testing the most promising combinations there first. ‘Once we have sufficient evidence, we can safely move on to clinical trials,’ says Van Hasselt. Over the project’s three-to-four-year span, the team aims to deliver a handful of ready-to-use combinations for doctors.
But their ambitions extend further: ‘The computer model we’re developing will serve as a toolkit for pharmaceutical developers. It will enable them to design new antibiotics and drug combinations that are pre-emptively tailored to counter resistance.’