by In a groundbreaking study led by UC San Francisco, researchers have identified genetic variants that can predict whether patients will respond to treatment for preterm birth, a condition that affects 1 in 10 infants born in the United States.
Currently, there is no medication available in the U.S. to treat preterm birth after the FDA withdrew the only approved therapy, a synthetic form of progesterone known as Makena, citing its ineffectiveness.
The study found that individuals with high levels of mutations in genes associated with involuntary muscle contraction were less likely to respond to the treatment. By screening for these mutations, doctors can now target medication to those who are most likely to benefit.
According to the senior author of the study, Jingjing Li, Ph.D., this research highlights the need for a precision framework for future drug development. Instead of focusing solely on population averages, it is crucial to consider the individual drug response of each patient and determine why some patients respond while others do not.
Preterm birth, defined as babies born alive prior to 37 weeks of gestation, is a leading cause of infant mortality worldwide and affects approximately 15 million pregnancies each year. Additionally, preterm birth can lead to long-term health consequences such as breathing problems, neurological impairments, developmental disabilities, visual and hearing impairments, heart disease, and other chronic illnesses.
To conduct the study, researchers developed a machine-learning framework to analyze the genomes of 43,568 patients who had experienced spontaneous preterm births. Through this approach, they discovered genes that had not previously been associated with preterm birth.
The researchers then examined mutations in these genes among patients who had received Makena, the progesterone treatment. While the drug was approved by the FDA in 2011 after a single clinical trial, it was taken off the market last year due to its lack of efficacy.
The study found that patients with low levels of mutations in genes related to muscle contraction were more likely to respond to Makena, while those with higher levels were less likely to respond. This suggests that a personalized medicine approach involving genetic screening could lead to successful results in patients without a high burden of these mutations.
The withdrawal of Makena by the FDA left doctors without an approved medication for preterm birth, frustrating those who had found it effective for some of their patients. This led researchers to question whether there was a genetic reason for the differences in response to progesterone therapy.
The study also included a cohort of African American patients to determine if the findings applied across different races. It was discovered that the genetic burden did not vary by race, suggesting that the higher rate of preterm birth among Black mothers may be primarily due to environmental factors such as elevated stress hormones, healthcare biases, and lack of prenatal care.
In addition to identifying genetic variants for predicting response to treatment, the researchers went further to identify new targets and potential therapies for preterm birth. They screened over 4,000 compounds and identified 10 that could potentially interact with the genes associated with preterm birth.
Some of these compounds are already being used to treat cancer and other diseases, indicating that they could be repurposed to help prevent preterm labor. One promising candidate is the small molecule RKI-1447, currently used to treat cancer, glaucoma, and fatty liver disease. Further investigation is needed to determine the potential of these compounds in treating preterm birth.
This research not only provides valuable insights into personalized medicine and the genetic factors influencing treatment response, but also opens doors to new possibilities in developing effective therapies for preterm birth.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
