New approach to prescribing antibiotics could curb resistance
Researchers at Rice University have proposed a new approach to understanding how to prescribe antibiotics in a way that will help to curb antibiotic resistance.
Shutterstock | Kateryna Kon
Currently, a course of antibiotics does not necessarily eradicate all of the bacteria infecting a patient and any remaining bacteria can be especially effective at timing their re-emergence.
Now, Anatoly Kolomeisky and Hamid Teimouri argue that it should be possible in the future to prescribe a more accurate dose that will ensure every last bacterium is dead or at least minimize the chances of bacterial colony developing resistance.
As reported in the Royal Society journal Interface, the study has shown that the fluctuating rates at which bacteria grow can increase the length of time it takes for a colony to completely die out, thereby improving the bacteria’s chances of developing resistance.
"Our calculations suggest this fluctuation, which bacteria can easily do, might help them bide their time and try different mutations. We think this is the possible first step in antibiotic resistance," says Kolomeisky.
The study showed that the fluctuating growth rates mean the bacterial extinction probabilities currently used to determine dose do not correlate with actual extinction times.
Kolomeisky says that current models only tell doctors the probability that a course of antibiotics will work. This means that when doctors calculate how much antibiotic a person should receive, everyone is treated equally: “They assume you have a huge amount of bacteria in your body and use a very simple deterministic model to prescribe the minimal concentration of antibiotic. Below that threshold, they say you will not be cured, and above it, you will always be cured.”
The researchers now present a preliminary model that accounts for the random fluctuations by incorporating the size of an infecting colony and averaging the amount of time it will take for the colony to die out completely.
"We care much more about the average time to be cured, not the probability. This will give doctors a much clearer description of what should be done," concludes Kolomeisky.
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