Tailoring Antibiotic Treatment For Patients with Cystic Fibrosis
Posted On: September 2019
Written by: Conan Y. Zhao, Quantitative BioSciences graduate student, Georgia Tech and Sam P. Brown, PhD, Associate Professor, Georgia Tech, and Alison Laufer Halpin, PhD, Associate Director, Office of Scientific Innovation and Integration, CDC
Healthcare providers can face two growing crises that affect their ability to treat bacterial infections: antibiotic resistance and chronic (long-lasting) infections.
A difficult combination for healthcare providers and patients are chronic infections caused by antibiotic-resistant germs.
Through a project with CDC, the Brown Lab at Georgia Tech developed new approaches to study the lung microbiomes of patients with cystic fibrosis (CF), who often get chronic infections. This work will help improve treatments for these patients.
Chronic infections are persistent illnesses that need to be treated for a long time before the infection clears.
Patients with cystic fibrosis (CF) are at particular risk for chronic infections. One of the traits of CF is build-up of a thick mucus layer coating the inside of the lungs, called sputum. Many types of commensal (non-disease causing) and harmful bacteria can survive in this environment, continually putting patients with CF at risk of lung infections. Because of these infections, CF patients are exposed to more antibiotics.
Assessing the Lung Microbiome
The microbiome is a community of naturally-occurring germs. Antibiotics can disrupt (unbalance) microbiomes by changing the natural composition of both commensal and harmful bacteria. With a disrupted microbiome, antibiotic resistant disease-causing bacteria can take over and cause infection.
A number of studies have found connections between the lung’s microbiome structure and the lung’s function (i.e., how well the lungs work). For example, the presence of pathogenic (disease-causing) germs are associated with poorer health.
These studies hint at the central role non-pathogenic (not disease-causing) germs can have to help maintain lung function. However, they have yet to answer if these germs—mostly commensal oral germs in the mouth, nose, or throat—are indicators of better health or actively help keep pathogenic germs at bay in the lung microbiome ecosystem.
To address this key question, the Brown Lab built an experimental modelexternal icon of the CF lung infection microbiome. A “synthetic sputum” recipe mimics sputum found in lungs of patients with CF. The lab adds defined combinations of up to 12 bacterial species to the model.
Together, these 12 bacterial species account for more than 90% of the bacterial community within the lungs of people with CF. Half of these germs are common pathogenic (disease causing) germs (e.g., Staphylococcus aureus, Pseudomonas aeruginosa). The rest of the bacteria are mostly non-pathogenic oral germs often found in the lungs of people with CF.
Results from the model support the idea that specific oral germs are able to slow growth of disease-causing germs within the CF lung. However, when we add antibiotics that are commonly used to treat patients with CF, these “protective” oral germs are quickly wiped out. This allows any existing, disease-causing germs that are resistant to the antibiotics to grow, take over, and cause disease.
Improving antibiotic prescribing and use is critical to effectively treat infections, protect patients from harms caused by unnecessary antibiotic use, and combat antibiotic resistance.
Using microbiome data and mathematical models of the CF lung microbiome, the Brown Lab is learning more about how each bacterial species affects each other, and their ability to resist antibiotics. Armed with this information, the lab is making predictions and testing them with the goal of improving antibiotic selection and treatments.
From there, the lab can develop new, personalized treatment strategies that combine antibiotics and, potentially, probiotics (live non-disease-causing bacteria that are generally recognized as safe [GRAS]) that are tailored to the infection and microbiome profile of the individual.