Phenomix Sciences Launches Clinical Trial to Predict Patient Response to GLP-1 Obesity Treatments
核心洞察
Phenomix Sciences (搜索) has initiated its first prospective, placebo-controlled clinical study to evaluate how its MyPhenome (搜索)™ genetic test can predict patient responses to semaglutide, a popular GLP-1 (搜索) therapy for obesity (搜索).
A new Mayo Clinic study using Phenomix's AI algorithm found patients with a high Genetic Risk Score were more than twice as likely to experience nausea from GLP-1 (搜索) medications compared to those with a low score (68% vs. 30%).
The research aims to improve treatment efficacy by matching patients to appropriate therapies based on their biology, potentially reducing side effects and improving adherence to GLP-1 (搜索) treatments.
Biotechnology company Phenomix Sciences (搜索) has launched its first prospective, placebo-controlled clinical trial to evaluate the ability of its MyPhenome (搜索)™ genetic test to predict individual patient responses to semaglutide, a GLP-1 (搜索) receptor agonist widely used in obesity (搜索) treatment.
The study builds upon promising retrospective research conducted at Mayo Clinic, which indicated that patients identified as "Hungry Gut" positive by the MyPhenome (搜索) test experienced nearly twice the weight loss on semaglutide compared to those who tested negative for this phenotype.
"This first-in-human clinical study is another important step for Phenomix in advancing precision obesity (搜索) treatment," said Mark Bagnall, CEO of Phenomix Sciences (搜索). "As GLP-1 (搜索) medications reshape obesity treatment, this study ensures they are used more effectively by matching patients to the right interventions based on their biology."
Predicting GLP-1 Side Effects with AI Algorithm
In parallel research presented at Digestive Disease Week (DDW) 2025, Mayo Clinic researchers in partnership with Phenomix Sciences (搜索) revealed that the company's proprietary machine-learning algorithms can predict which patients are more likely to experience side effects from GLP-1 (搜索) therapies.
The study, led by Phenomix co-founder Andres Acosta, MD, PhD, analyzed post-hoc genetic data from 110 participants using a machine learning-assisted Genetic Risk Score (GRS). Results showed patients with a high GRS were more than twice as likely to experience nausea from liraglutide, a GLP-1 (搜索) medication, compared to those with a low score (68% vs. 30%).
"These findings represent a meaningful advancement in how we approach obesity (搜索) treatment at an individual level," said Dr. Acosta. "By identifying which patients are more likely to experience side effects before starting therapy, we can improve tolerability, support long-term adherence, and better match the right treatment to the right patient."
Nausea is the most common side effect of GLP-1 (搜索) medications, affecting up to 40% of patients and causing approximately 6.4% to discontinue treatment. The ability to predict this adverse event could significantly improve patient outcomes and treatment adherence.
The MyPhenome Test: A Precision Medicine Approach
The MyPhenome (搜索) genetic obesity (搜索) test uses a simple cheek swab to identify underlying biological factors, or phenotypes, that contribute to an individual's obesity. The non-invasive test is designed to help healthcare providers develop tailored treatment strategies, including lifestyle modifications, dietary changes, and targeted medication recommendations.
Previous research presented by Phenomix at DDW 2024 demonstrated that the MyPhenome (搜索) test can identify patients more likely to respond to semaglutide. The current clinical trial is actively enrolling patients and will assess semaglutide's effectiveness in individuals with obesity (搜索) who receive either a positive or negative MyPhenome test result for the "Hungry Gut" phenotype, characterized by abnormal satiety.
"Our team's research builds on previous findings by showing we can now predict not just who will benefit from GLP-1s, but who is more likely to struggle with side effects," said Thomas Fredrick, MD, who presented the findings at DDW 2025. "That allows for more balanced, individualized treatment planning."
Implications for Clinical Practice and Drug Development
The predictive capabilities of the MyPhenome (搜索) test and associated algorithms have significant implications for both clinical practice and pharmaceutical development. For clinicians, the ability to predict both efficacy and side effects before initiating treatment could reduce unnecessary prescribing, prevent avoidable emergency room visits, and ensure patients receive medications they can tolerate.
For pharmaceutical companies, these tools could improve participant selection and retention in clinical trials, potentially accelerating time to market for new obesity (搜索) medications.
"This study underscores the power of predictive tools like MyPhenome (搜索) to transform how we approach obesity (搜索) treatment — not just in the clinic, but in the drug development pipeline," said Bagnall. "By identifying patients at risk for side effects before treatment begins, we can match the right patient to the right therapy, increase real-world adherence, and dramatically improve clinical trial efficiency through smarter patient selection."
The research presented at DDW 2025 was one of 17 studies by Mayo Clinic researchers, with eight incorporating Phenomix's machine learning-based algorithms. The company is backed by Health2047, the innovation arm of the American Medical Association.
As obesity (搜索) rates continue to rise globally and demand for GLP-1 (搜索) medications increases, precision medicine approaches like those developed by Phenomix Sciences (搜索) may play a crucial role in optimizing treatment outcomes and resource allocation in obesity management.