A recent study utilized artificial intelligence (AI) to discover two plant compounds that may offer weight loss benefits comparable to current medications but with fewer drawbacks. Researchers from the Catholic University of Murcia in Spain employed advanced AI methods to analyze over 10,000 natural compounds, identifying candidates that mimic the action of GLP-1, a hormone targeted by existing weight loss drugs like tirzepatide and semaglutide.
Natural GLP-1 agonists address drawbacks of current receptor agonists
GLP-1 receptor agonists are medications that activate the GLP-1 receptor in cells, leading to appetite suppression, slowed stomach emptying, and increased feelings of fullness, promoting weight loss. However, according to study author Elena Murcia, current GLP-1 agonists have drawbacks.
Murcia explained that GLP-1 agonists have demonstrated effectiveness, but they come with side-effects including gastrointestinal problems and mental health changes. Recent data also shows that weight lost during treatment is regained when treatment stops.
Researchers utilized advanced virtual screening techniques to sift through a large pool of compounds, identifying 100 with strong binding to the GLP-1 receptor. Among these, “Compound A” and “Compound B” displayed interactions similar to promising synthetic GLP-1 agonists, indicating potential oral administration alternatives to existing peptide-based treatments.
Two undisclosed compounds are currently being developed by researchers. The identities of the plants and compounds are being withheld pending patent applications. These compounds are being tested for their ability to activate GLP-1 receptors, with potential plans for pill formulation pending successful laboratory tests and subsequent human clinical trials.
Researchers using AI to develop GLP-1 agonists from natural sources
The development of new GLP-1 agonists from natural sources, with an emphasis on utilizing AI-based calculations for validation in both in vitro and clinical settings is in early stages, according to Murcia. This approach offers potential therapeutic options for managing obesity, highlighting the benefits of computer-based methods in early drug discovery.
Murcia highlights the benefits of computer-based research, emphasizing cost and time savings, quick analysis of large data sets, flexible experimental design, and the ability to address ethical and safety concerns beforehand. They also note the advantage of using AI for analyzing complex problems, particularly in drug discovery efforts.
