Sano Genetics

🎙️Tune in to Episode 265 of #TheGeneticsPodcast 🚀

This week, we’re joined by Dr. Joe Volpe, Executive Director, Scientific Strategy & Commercialization at Labcorp. Listen in to hear how blood biomarkers are changing Alzheimer’s diagnosis, which biomarkers are emerging in Parkinson’s disease and ALS, and what real-world testing data reveals about how physicians use these tests.

Available here👇

🎧 eu1.hubs.ly/H0zRpgt0
📽️ eu1.hubs.ly/H0zRpg90

In rare disease gene therapy trials, a handful of manufacturing lots and trial participants may form the foundation of an approval application. That leaves little room for gaps in the evidence.

The FDA's new potency assurance guidance reinforces the importance of planning manufacturing quality and clinical evidence generation early in development.

For sponsors working with small patient populations, that means thinking beyond potency testing to consider how genetic confirmation, antibody screening, and long-term patient engagement support interpretable clinical outcomes.

In our latest blog, we explore what the guidance means for rare disease gene therapy developers, including:

🧬 How potency assurance expectations evolve throughout development
🔬 Why patient characterization and antibody screening matter for trial planning
📊 How sponsors can strengthen evidence generation when every participant counts

Read the full article: eu1.hubs.ly/H0zPghj0

#GeneTherapy #RareDisease #ClinicalTrials #PrecisionMedicine

🤖 AI systems have started producing results on open problems in mathematics, and some of those proofs have been confirmed by a proof checker in a few hours each.

In a solo episode of The Genetics Podcast, our CEO and co-founder Patrick Short asks what that means for drug development. His view is that AI will advance in biology only as fast as its ideas can be verified, and that validated surrogate endpoints could play the role in clinical development that the proof checker plays in math.

Our recap covers the feedback loops in biology from fastest to slowest, why results build on each other less readily in biology than in math, and the test Patrick applies to upstream AI investment, which is whether it improves Phase II success rates.

🔗 Read it here: eu1.hubs.ly/H0zKqk90

🎙️Tune in to Episode 264 of #TheGeneticsPodcast 🚀

This week, Patrick goes solo to explore what AI’s recent math breakthroughs mean for biology. Listen in to hear why math and code are so easy for AI to improve on, how self-driving labs are shortening the preclinical feedback loop, and why surrogate endpoints could be the biggest opportunity in drug development.

Available here👇

🎧 eu1.hubs.ly/H0zGm9k0
📽️ eu1.hubs.ly/H0zGmc00

🧬 Lp(a) is becoming an increasingly important biomarker in cardiovascular drug development, but testing remains far from routine.

An analysis of 70.8 million adults at Cleveland Clinic found that just 3.52% of people with familial hypercholesterolemia had received an Lp(a) test. In the All of Us research program, only 0.8% of participants with health record data had been tested.

That's a challenge for sponsors developing Lp(a)-targeted therapies. Even when potentially eligible patients are receiving cardiovascular care, many won't have the test results needed to identify them for a trial. Sponsors are then left establishing testing pathways alongside recruitment, often working site by site to build a population of eligible participants. And much of that work has to be repeated when the next trial comes along.

As more cardiovascular therapies target genetically defined or biomarker-specific populations, there's a case for thinking about testing access much earlier, and beyond the needs of an individual study.

📖 We take a closer look at the testing gap, its implications for trial planning, and how sponsors might approach it differently.

Full analysis: eu1.hubs.ly/H0zzdBC0