Dr. Fernando J. Martinez, Bruce Webster Professor of Internal Medicine, and Chief, Division of Pulmonary and Critical Care Medicine, WDOM is lead author of a pivotal paper in JAMA that studied a novel trial design for the treatment of idiopathic pulmonary fibrosis (IPF).
“IPF is a rare but fatal disorder that has a prognosis worse than most cancers and, unfortunately, has very limited therapeutic options,” explains Dr. Martinez. IPF develops when the lungs become damaged or scarred, making it difficult to breathe. Researchers have long been searching for a better mechanistic understanding of IPF that could lead to better treatment.
The paper authored by Dr. Martinez and colleagues and published in JAMA (The Journal of the American Medical Association) was based on a multicenter, phase 3 clinical trial sponsored by Weill Cornell Medicine. It was found that adding antibiotics to usual care does not improve outcomes for people with IPF. Although this was a negative finding, it is believed that the unique and novel way in which the trial was designed has paved the way for future studies that will be less expensive and easier to conduct. Furthermore, this trial was the first pragmatic trial to be conducted in patients suffering with IPF. A pragmatic trial aims to evaluate effectiveness of treatments in real-life settings.
This trial was conducted in collaboration with Duke Clinical Research Institute, Three Lakes Foundation, the IPF Foundation, Veracyte, Inc., 35 clinical centers across the United States, and the National Heart, Lung, and Blood Institute, part of the National Institutes of Health (NIH), included 513 adults with IPF. About half had either co-trimoxazole (commonly known as Bactrim) or doxycycline added to their treatment. The rest received no additional treatment beyond standard care. The patients were followed until they died or experienced a lung-related hospitalization. After an average follow-up time of just over a year, the difference in the time to disease progression among the three groups was not statistically significant.