Bacteria have dangerously evolved to thwart many drugs designed to kill them. As a result, a growing antibiotic resistance crisis is responsible for more than 700,000 deaths each year, becoming one of the world’s most pressing health issues.
Since the development of new antibiotics to treat infections has stalled, many patients are now receiving multi-drug treatments in the hope that their joint therapeutic effects may prevent the development of further resistance. Yet, many risks and unknowns are involved in these multi-drug treatments.
Giving a drug to a patient often causes bacteria to develop resistance against it. Fortunately, some of these resistant mutants become more sensitive to a second drug, allowing doctors to successfully treat the infection. However, doctors cannot always be sure when and if evolution will follow this happy course. Worse still, resistance to the initial drug can backfire and cause a to augment in resistance against the second drug, leaving doctors with no other treatment options.
Scientists at the University of California, San Diego have now developed a way that can help doctors calculate the odds of incidental results for different drug pairs and thus increase the chances of a successful treatment. As described in the review eLifegraduate student Sarah Ardell and assistant professor Sergey Kryazhimskiy have developed a mathematical model that can calculate the risk of resistance evolution for various drug pairs.
“The problem with using multiple drugs to treat bacteria is that we just don’t know what mutations are available to bacteria,” said Kryazhimskiy, from the ecology, behavior and evolution section. of the Biological Sciences Division. “In many situations, bacteria can gain access to mutations that make them resistant to both drugs as well as mutations that make them resistant to the first drug but susceptible to the second. In such situations, it is very difficult to predict in which direction the population will evolve.The model that we have developed allows us to make these predictions.
In developing the model, Ardell and Kryazhimskiy used a new concept called “JDFE”, which stands for “Joint Distribution of Fitness Effects (of New Mutations)”. The JDFE characterizes the different types of mutations available to bacteria and allows researchers to categorize drug pairs into those that facilitate or hinder multi-drug resistance.
After reviewing the mutational data available for the bacterium Escherichia coli, researchers have found many resistance mutations against various commonly used antibiotics that lead to collateral sensitivity (a beneficial outcome) or collateral resistance (a detrimental outcome) with other drugs. They say their new model could help better predict resistance outcomes, which means a win for infected patients, although it’s not foolproof given the inevitable randomness of evolution.
Ardell said she was surprised to learn that antibiotic resistance cannot be viewed as a simple deterministic process. The more she learned, the clearer it became that different bacterial populations develop resistance in different ways, even under controlled laboratory conditions. The same experiments conducted by different labs often produce conflicting results, she found.
The strain of bacteria, the concentration of drugs, and the nutrients in the body’s environment can all lead to mixed results.
“But even if all of those things are exactly the same, you might always get different results in two different iterations just because evolution is built by random mutations,” Ardell said. “Two different populations could have randomly accumulated different mutations with different side effects, even if everything else is equal. There is so much variability and randomness in these processes, which is an incredibly important thing for patients to think about. We want to give drug pairs that we believe will produce collateral sensitivity as much as possible – not just 50% of the time.”
The researchers indicate that much remains to be learned about the diversity of side effects of resistance mutations.
Ardell is currently investigating drug pairs that address the same target, the ribosome, an important protein complex in bacterial cells. She builds a metabolic model of the cell to understand JDFE from a mechanistic point of view.
“The essence of our result is that we can predict the probability of collateral resistance evolution,” Kryazhimskiy said. “It’s not perfect, but it’s better to have no idea what’s going to happen. If we choose the drug pairs carefully, we can minimize the likelihood of collateral resistance. We can’t completely rule out the “adverse effect, but we can minimize the risk that it will happen. Our work could potentially help clinicians choose drugs that minimize the course of multidrug resistance.”