Researchers have identified a single, simple metric to guide antibiotic dosing that could bring an entire arsenal of first-line antibiotics back into the fight against drug-resistant pathogens.
A computer simulation created by Hannah Meredith, a biomedical engineering graduate fellow at Duke University, revealed that a regimen based on a pathogen's recovery time could eliminate an otherwise resistant strain of bacteria. In theory, a database of recovery times for bacterial and antibiotic combinations could allow first-line antibiotics to clear many resistant infections.
Work to create such a database is underway and early tests are confirming her model's predictions.
"Bacteria are forming resistance to antibiotics faster than we can make new ones, so there is a real need to use the antibiotics that are already on the market more efficiently," said Meredith. "We hope this research will help hospitals improve patient outcomes while also making our antibiotics last as long as possible."
The computer simulation models the relationship between bacteria, antibiotics and a method of resistance called beta-lactamase -- an enzyme that degrades beta-lactam antibiotics, one of the largest and most-used classes of antibiotics. Many beta-lactam antibiotics are currently disregarded out of concern for the infection being completely resistant to that type of antibiotic -- even if the antibiotic appeared to be effective in the lab. The new model, however, reveals that the bacteria might be temporarily sensitive to the antibiotic before the beta-lactamase degrades the drug and allows the infection to recover.
"You can think of this as a race between the cells and the antibiotics," said Lingchong You, the Paul Ruffin Scarborough Associate Professor of Biomedical Engineering at Duke and Meredith's adviser. "Before their beta-lactamase degrades the antibiotics, the cells are still sensitive and can be killed. But the antibiotics degrade faster than the cell population declines, allowing some cells to survive and repopulate."
When clinicians realize an infection is resistant, they often skip straight to some of the strongest antibiotics available. But the study indicates that if they instead changed the dosing frequency of first-line antibiotics so that each dose is delivered while the bacteria are weakened during their recovery period, some infections could be cleared without skipping to the last resort.
Citation: "Bacterial Temporal Dynamics Enable Optimal Design of Antibiotic Treatment," Hannah R. Meredith, Allison J. Lopatkin, Deverick J. Anderson, Lingchong You. PLOS Computational Biology, 2015, 11(4): e1004201. DOI: 10.1371/journal.pcbi.1004201. This work was funded by the National Science Foundation (CBET-0953202), the National Institutes of Health (1R01GM098642, 1RO1GM110494), a DuPont Young Professorship award, a David and Lucile Packard Fellowship, the National Science Foundation Graduate Research Fellowship, and the Howard G. Clark Fellowship.
