Introduction
GLP-1–based drugs have gone from a niche diabetes treatment to the center of metabolic disease pipelines. Now there’s growing interest in dual and triple incretin agonists, oral peptide delivery, and expanding indications into NASH, cardiovascular disease, and even neurodegenerative conditions.
That complexity means more data – and more questions. This is where bioinformatics plays a critical role.
We’ve worked with companies developing incretin-based therapies, and what we see across the board is that bioinformatics helps teams move faster and more confidently through some of the most uncertain parts of R&D. Here are a few examples of how it’s being used:
Exploring Targets Beyond GLP-1
Most next-gen metabolic therapies go beyond GLP-1 alone. We’re seeing combinations like GLP-1/GIP, GLP-1/glucagon, and even triple agonists. That raises a key question: where are these targets expressed, and what’s their role in different tissues?
This is where transcriptomic analysis comes in. By comparing expression patterns across tissue types (pancreas, liver, adipose, brain, etc.), developers can:
- Assess off-target effects
- Prioritize combinations with complementary expression profiles
- Understand how targets may behave differently in obese vs. lean individuals
Public datasets found in repos like GTEx, single-cell atlases can help here—but they need careful curation and analysis to be useful.
Biomarker Discovery for Emerging Indications
GLP-1 drugs are being tested for a range of indications beyond obesity, including:
- MASLD/NASH
- Cardiovascular and renal disease
- Cognitive decline and Alzheimer’s
Each of these indications have different biomarker needs. In NASH, for instance, researchers are looking at transcriptomic signatures of fibrosis or inflammation. In Alzheimer’s, the focus might be on neuroinflammatory pathways or metabolic dysfunction in the brain.
Bioinformatics pipelines can support:
- Differential gene expression and pathway analysis
- Identification of candidate pharmacodynamic or predictive biomarkers
- Integration of transcriptomics, proteomics, and metabolomics data
This kind of analysis can be particularly helpful in early-stage studies where clinical endpoints are slow to appear.
Stratifying Responders
GLP-1–based therapies work well overall, but patient response can vary – especially in more complex indications like NASH or Alzheimer’s. Being able to predict who will respond (and why) is a major focus right now.
Bioinformatics supports this in a few ways:
- Identifying molecular subtypes of disease that correlate with response
- Analyzing baseline omics data to find predictive signatures
- Supporting the development of companion diagnostics or enrichment strategies for trials
This kind of stratification can improve trial design and may ultimately support precision medicine approaches for metabolic disease.
Understanding Differentiation
As more GLP-1–based drugs enter the clinic, differentiation becomes a major challenge. Often the differences are subtle – pharmacokinetics, durability of response, or effects on secondary endpoints like liver adiposity or inflammatory markers.
Omics data can provide another layer of evidence. For example, some teams are looking at:
- Changes in gene expression profiles in liver or adipose tissue
- Unique immune or inflammatory signatures
- CNS-related effects in preclinical models
These data won’t replace clinical outcomes, but they can help explain why one molecule might be more effective -or better tolerated – than another.
When’s the Right Time to Bring in Bioinformatics Support? Let’s Talk.
One of the most common questions we get from teams in the GLP-1 space is, “When should we bring in outside help for our data analysis?”
The truth is, it depends. But you don’t have to figure it out alone.
At Bridge Informatics, we’ve worked with a range of metabolic disease teams, from early-stage startups juggling preclinical data to enterprise R&D groups mining real-world evidence from thousands of patients. Whether you’re trying to make sense of RNA-seq from liver biopsies, clean up imaging data from fat mass studies, or just need someone to wrangle that backlog of trial biomarkers – we can help.
If there’s one thing we’ve learned from watching the GLP-1 space evolve, it’s that the companies making the most of their data aren’t always the biggest – they’re the ones who ask the right questions early.
Click here to schedule a free introductory call with a member of our team.
