Phenomix and Mayo Clinic Use AI to Predict GLP-1 Side Effects
Phenomix Sciences and Mayo Clinic have unveiled a new study demonstrating the use of AI algorithms to predict side effects from GLP-1 therapies, specifically nausea. Announced in a press release, the study was presented at Digestive Disease Week 2025 and highlights the potential for more personalized obesity care.
The research, led by Dr. Andres Acosta and presented by Dr. Thomas Fredrick, utilized a machine learning-assisted Genetic Risk Score (GRS) to analyze genetic data from 110 participants. The findings revealed that patients with a high GRS were more than twice as likely to experience nausea from liraglutide, a common GLP-1 medication, compared to those with a low score.
This predictive capability is significant as nausea is a prevalent side effect of GLP-1 therapies, affecting up to 40% of patients and causing some to discontinue treatment. By identifying patients at higher risk for side effects, the study aims to improve treatment tolerability and adherence, while also enhancing clinical trial design and pharmaceutical development.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Life AI Weekly or Daily AI Brief.
Also, consider following us on social media:
More from Life Sciences
Oct 16 Study Tests Smartphone AI for Children's Lung Sounds Oct 16 Manhattan Physician Invests in RunPractice Oct 16 Attilio appoints Buck Institute CEO Eric Verdin as cochairman Oct 16 Aignostics launches HETA Clinical for pathology workflows Oct 16 Specialty1 Partners Invests in AI for Dental Practice OperationsLife AI Weekly
Weekly coverage of AI applications in healthcare, drug development, biotechnology research, and genomics breakthroughs.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Insilico Medicine Selects AI Designed Obesity Drug Candidate ISM1354
LUNGevity Awards $2 Million for Lung Nodule Risk Studies
Zealand Pharma signs AI obesity drug deal worth up to $1.2 billion
SimBioSys Gets FDA Clearance for AI Breast Cancer Recurrence Tool
MediSights Reports Study Using AI Agents for Myeloma Market Research
Daily AI Brief: the AI news that matters, in your inbox.