Computer Model May Help Improve Antibiotic Prescribing, Fight Resistance

Researchers from Duke University have found a single metric to help design a medication regimen that could reintroduce first-line antibiotics in fighting drug-resistant pathogens. The study is published in PLOS Computational Biology.

Hannah Meredith, a biomedical engineering graduate fellow at Duke, created a computer simulation that showed a regimen based on a pathogen’s recovery time could eliminate resistant bacterial strains. This database containing recovery times for antibiotic combinations may potentially allow first-line antibiotics to overcome many resistant infections. The simulation works by modeling the relationship between bacteria, antibiotics, and beta-lactamase resistance. Many beta-lactam antibiotics are not being considered nowadays due to resistance of the strains but the new model demonstrates that the infection may be temporarily sensitive to the antibiotic before the beta-lactamase enzyme degrades the drug and enables the infection to recover.

Instead of resorting to the strongest available antibiotics when encountering resistance, the study suggests that some infections can be cleared by adjusting the dosing frequency of first-line antibiotics so that each dose is delivered when bacteria are weakened during the recovery period. The computer model may also help determine the most efficient regimen to keep total exposure low when integrated with a database describing the different strains’ responses to different antibiotics. Healthcare providers could also be informed if multiple doses would not work and stronger antibiotics would be necessary.

Currently, Meredith is testing her theory by utilizing 80 well-known antibiotic-resistant bacteria strains. Preliminary data has confirmed many of the model’s clinical predictions. Researchers hope this data will help improve hospital outcomes while also making antibiotics last as long as possible.

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