GLP-1 drugs like semaglutide and tirzepatide have demonstrated significant weight loss and improved metabolic profiles. However, a major clinical challenge is weight rebound after treatment ends. Trials like STEP-1 show a clear pattern of regain, raising questions about long-term sustainability. In our white paper, we explore this phenomenon—both in clinical data and mirrored in DIO (diet-induced obesity) rodent models.

Combination approaches are showing superior efficacy by targeting multiple mechanisms. CagriSema, for instance, combines semaglutide with cagrilintide and has demonstrated over 20% weight loss in trials. MariTide delivers similar efficacy with monthly dosing. In our lab, we have studied how these multi-target approaches are influencing preclinical model requirements.

Unlike simple HFD models, our DIO models incorporate genetic and environmental factors, enabling reliable simulation of metabolic syndrome features—body weight regulation, insulin resistance, fatty liver, and more. When paired with our metabolic phenotyping tools, they offer a robust translational platform. 

We utilize a triad of tools:

• EchoMRI for high-throughput, non-invasive body composition tracking

• TSE metabolic caging to monitor energy expenditure, feeding, and locomotion

• Biomarker panels including glucose tolerance tests, liver histology, and GLP-1 quantification
  These tools create a comprehensive metabolic fingerprint—crucial for confidently linking preclinical results to clinical endpoints.

Yes. Our in-house studies reveal a rebound pattern post-drug suspension that closely aligns with human outcomes. These findings not only validate our models but also offer opportunities to test next-gen compounds aimed at durability.

We also support small molecule and peptide screening with in vitro receptor assays for GLP-1R, GIPR, GCGR, and MC4R—covering cAMP signaling, β-arrestin recruitment, and binding affinity. Our recent whitepaper outlines how this dual platform (in vivo+in vitro) supports rapid go/no-go decisions.