Study Finds Potential to Repurpose Cancer Therapies to Treat Rare Diseases
October 21, 2021
Researchers have developed a computational algorithm that they say can help identify certain cancer therapies that can be repurposed to treat non-cancerous rare diseases.
The paper, published in Science Advances, lists more than 30 authors representing 11 universities and medical facilities, including Harvard University, MIT, and Boston Children’s Hospital. As part of that work, researchers at Prairie View A&M in Texas developed a computational platform based on a combination of cancer cell line data and small molecule response data to investigate a direct link between a rare disease, such as pulmonary hypertension and cancer, at a molecular level using a computational algorithm developed by Seungchan Kim, chief scientist and executive professor at Prairie View A&M University in Texas, a co-senior author of the study.
“Repurposing drugs can cut down the time and cost of developing treatments for rare diseases, which historically don’t receive much investment into research and drug development,” said University of Pittsburgh Medical Center physician-scientist Stephen Chan, co-senior author on the paper and professor of medicine and director of the Vascular Medicine Institute at the University of Pittsburgh, who collaborated with Kim over five years. “Pulmonary hypertension is an example of a rare disease where there is an unmet need for new treatments, given its devastating consequences. We developed this pipeline to rapidly predict which drugs are effective for PH and get these treatments to patients more immediately.”
The researchers said the findings could facilitate future applications that would allow for the repurposing of cancer therapy drugs to tailor a precise treatment or predict the response to a drug to treat non-cancerous conditions.
They said this computational platform could be used to determine an individual’s response to a drug leading to a more precise, successful treatment of disease.
Author: Rare Daily Staff
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