Guest Post by Grace Xiong, NC School of Science and Math
For those who study antibiotic resistance, a future where people die from infected cuts or the measles is not some horrible alternative universe but something that could very well be a reality soon. In 1998, the House of Lords in London gave a stark report proclaiming, “Antibiotic resistance threatens mankind with the prospect of a return to the pre-antibiotic era.” They were especially concerned with findings that indicated an increase in resistance to antibiotics that treated common but deadly diseases such as typhoid, dysentery and various strains of meningitis.
As hospitals use antibiotics at an unprecedented rate to treat patients, there has been an increase in the number of infections that are antibiotic-resistant. This leads to more patient deaths from infections that could have been treated with antibiotics 20 or 30 years ago.
Hannah Meredith, a graduate student in Lingchong You’s lab at Duke, has been studying the phenomenon of altruistic cell death in bacteria, which is when a bacterial cell will self destruct, breaking down the antibiotics they’re faced with as well. This process not only protects its fellow bacteria, it creates bacteria selectively chosen to be more resistant to the antibacterial due to the fact that the only bacteria left will be the ones resistant to the antibacterial.
When I asked her why she believed why studying this was so critical, she immediately responded that, “Optimizing antibiotic regimens is imperative so that treatment outcomes are positive and negative side effects are minimized. Although doctors and researchers recognize the need to optimize treatments, no one has come up with a standard method to design the best antibiotic regimen, given a particular antibiotic and specific infecting bacteria.”
Given that most pharmaceutical companies have stopped researching new antibiotics, the solution Hannah Meredith wants to use is to change the antibiotic dosage and the time the dosage is administered so that the maximum amount of bacteria can be killed while not using an extraordinarily large amount of antibiotics.
Using computer modeling, she is able to predict when a strain is most susceptible to an antibacterial and what percentage of bacteria can be killed with a certain amount of antibacterial. Ultimately she hopes to model common strains of infections (she has started using samples from the VA hospital) and create a data base that hospitals can use to more effectively treat their patients and in turn, reduce the amount of antibiotic resistance present in hospitals by getting it right the first time.