ImmunoPrecise Antibodies announces results from GLP-1 peptides study

ImmunoPrecise Antibodies (IPA) announced in vitro results demonstrating that its artificial intelligence-designed GLP-1 receptor agonist peptide sequences achieve comparable or superior receptor activation to Semaglutide, a benchmark GLP-1 therapy and one of the most commercially successful drugs in the world. The in vitro analysis was conducted by an independent third party, further strengthening the objectivity and reliability of the findings. The results validate the power of IPA’s LENSai platform and its proprietary Hyft technology, which enables first-principles-based in silico peptide discovery and optimization. Five rationally engineered peptide sequences-AI-optimized for improved stability and peptidase resistance-were tested against benchmark GLP-1 analogs in receptor activation assays. Two lead candidates outperformed or matched Semaglutide under controlled assay conditions. The sequences were not discovered by chance. They were designed using HYFT-derived intrinsic biological patterns-mathematical representations of functional relationships in biology-making this approach highly reproducible and generalizable to other drug targets. With GLP-1 therapies representing one of the fastest-growing segments in the global pharmaceutical market, these results demonstrate that AI can now generate not only predictive but functionally validated peptide drugs, paving the way for rapid expansion into adjacent therapeutic areas using the same HYFT-driven approach. IPA is now considering two complementary preclinical paths for its lead GLP-1 candidates: Injectable delivery studies, including pharmacokinetic profiling and efficacy validation in animal models. Non-invasive delivery strategies, including formulation work for transdermal patches and exploratory evaluation of nucleic acid-based delivery, designed to enhance durability, patient compliance, and ease of administration. The sequences were also engineered for compatibility with next-generation expression systems, including nucleic acid-based vectors, which support scalable expression in both traditional and emerging human-relevant models, aligning with evolving regulatory interest in more predictive, flexible, and efficient therapeutic development approaches.