Sano Genetics

Sano combines genetic testing, recruitment, and long-term engagement in one platform, accelerating enrollment and simplifying operations for precision medicine

[Sano Genetics @sanogenetics]

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: https://t.co/4lYAM95u3n

#GeneTherapy #RareDisease #ClinicalTrials #PrecisionMedicine

[Sano Genetics @sanogenetics]

🤖 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: https://t.co/MZwKBuiKIX

[Sano Genetics @sanogenetics]

🎙️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👇

🎧 https://t.co/ZKXtukRsGd
📽️ https://t.co/rf7S5s6Wmq

[Sano Genetics @sanogenetics]

🧬 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: https://t.co/NYtZnORNe4

[Sano Genetics @sanogenetics]

Last week, the FDA issued draft guidance proposing that a single license could cover multiple patient-specific versions of a genome editing product, provided the genetic variants fall within a defined Individualized Feature Space.

Because the pathway is set at the level of individual variants, patient identification starts to affect development planning well before recruitment begins. Our analysis covers four areas sponsors may want to plan for.

🔹 Variant diversity in the clinically studied population
🔹 Genotype-phenotype evidence for variants found in very few patients
🔹 Testing strategies built around the full range of covered variants
🔹 Recontact of patients as eligibility changes after approval

Learn more at the link in the comments below.

[Sano Genetics @sanogenetics]

🫀 Lowering LDL by 40 mg/dL for five years reduces heart disease risk by about 22%. Sustained for a lifetime, the estimate is almost 90%.

In the latest episode of The Genetics Podcast, Dr. Sek Kathiresan, CEO of Verve Therapeutics and head of obesity research and early development at Eli Lilly, explains why he treats heart disease as a cumulative exposure problem. His research on people with naturally low cholesterol led to a one-time base editing therapy, which lowered LDL by roughly 50 to 60% at the top two doses in the first 35 patients.

Our recap also covers what the Lp(a) HORIZON result means for patient selection, how polygenic risk scores could sharpen future trials, and where obesity treatment is heading.

📖 Read it here: https://t.co/96vzBjURtN

[Sano Genetics @sanogenetics]

🎙️Tune in to Episode 263 of #TheGeneticsPodcast 🚀

This week, we’re joined by Dr. Sek Kathiresan, CEO of Verve Therapeutics and SVP, Head of Obesity Research and Early Development at Eli Lilly. He shares how an idea that faced early doubt became a one-time gene editing therapy for cholesterol, what the first patient data show, and where obesity treatment is heading next.

Available here👇

🎧 https://t.co/9DfPNelO4S
📽️ https://t.co/6gaae5z8Pv

[Sano Genetics @sanogenetics]

A patient identified for one clinical trial could also be relevant to another study several years later. But too often, patient identification starts again with each new protocol.

Familial hypercholesterolemia (FH) offers an interesting example of why a longer-term approach could be valuable. Between 2014 and 2024, the Dutch national cascade screening program genetically tested 8,447 relatives of people with FH. Just over half were found to carry FH.

Cascade screening shows how identifying one person can help uncover others who may otherwise remain undiagnosed. For sponsors developing multiple cardiovascular therapies, there's an opportunity to think beyond finding patients for the next study.

Patients identified and characterized today may be relevant to future programs, provided the appropriate consent, governance and recontact processes are in place. That doesn't mean every patient will be eligible for another trial. But maintaining those relationships could reduce the need to repeat the same identification work across programs.

It's something sponsors should be considering much earlier in clinical development, particularly as cardiovascular genetic medicine pipelines grow.

❓ What would it take to build patient-finding infrastructure that supports more than one trial?

We explore this in our article: https://t.co/0Rhdn5NRWj

[Sano Genetics @sanogenetics]

🧬 Population genomic screening is identifying genetic risk at an unprecedented scale. But what does that mean for clinical research?

At ASHG 2026, Helix presented findings from more than 525,000 adults across 14 US health systems, identifying approximately 9,200 carriers of variants linked to familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome.

The findings showed encouraging changes in preventive care following genetic screening.

But for sponsors developing genotype-defined therapies, they also highlight an important distinction: identifying someone with a relevant genetic variant doesn't mean they're ready or available to participate in a clinical trial. Between a screening result and an enrolled participant sit several important steps: consented recontact, clinical prescreening, confirmatory testing, and ongoing engagement.

Our latest blog explores what these findings mean for clinical trial feasibility, and the questions sponsors should be asking as genetically characterized populations continue to grow.

📖 Read more: https://t.co/WaWPd7acQ9

[Sano Genetics @sanogenetics]

🧬 Around 1 in 150 people of Northern European ancestry are homozygous for the HFE C282Y variant. Many are diagnosed late or never, and not all of them will develop hemochromatosis.

On the latest episode of The Genetics Podcast, Luke Pilling of the University of Exeter explained that a genotype is where the clinical question starts. Our recap breaks down his argument.

✅ The treatment is simple, but timing is the hard part. Phlebotomy is safe and effective when started early, so the challenge is finding people in time.
✅ The variant "pushes risk up a bit." Waist-hip ratio and polygenic risk scores add to that risk, steering homozygotes toward outcomes such as diabetes or osteoarthritis.
✅ Shared genetics can cut both ways. A CTLA4 variant that raised rheumatoid arthritis risk lowered a cancer risk, so treating one condition could raise risk of the other.

The recap also covers multimorbidity, where genotype-guided prescribing stands today, and what lifespan genetics shows about aging.

🔗 Read the full recap here: https://t.co/aaKu1fIGAk

[Sano Genetics @sanogenetics]

🎙️Tune in to Episode 262 of #TheGeneticsPodcast 🚀

This week, we’re joined by Dr. Luke Pilling, Senior Lecturer in Genetic Epidemiology at the University of Exeter. Listen in to hear how genetics can help find people with hemochromatosis earlier, what shared genetic drivers reveal about multimorbidity, and where pharmacogenetics is making a difference to patient care.

Available here👇

🎧 https://t.co/9q5gFKZimc
📽️ https://t.co/vipjaupgkH

[Sano Genetics @sanogenetics]

🔬 Early results from Beam Therapeutics' BEAM-301 program show promise for treating glycogen storage disease type Ia (GSD Ia), but they also highlight a challenge for precision medicine trials.

When a therapy targets a specific genetic mutation, finding patients with the disease is only part of the picture. Sponsors also need to know how many patients carry the relevant variant, where they are, and how to reach them.

This makes early feasibility work, including genetic testing access and patient identification, more important than ever.

💡 Our latest article looks at what BEAM-301 tells us about the challenges of recruiting for mutation-specific therapies, and why these questions need to be addressed well before enrollment begins.

Read more: https://t.co/krbvi7cTZE

#PrecisionMedicine #ClinicalTrials #GeneEditing

[Sano Genetics @sanogenetics]

Fewer than 1 in 10 people with familial hypercholesterolemia (FH) in the US have been identified – that's a significant blind spot for cardiovascular trials relying on diagnosed patient populations to assess feasibility.

For trials requiring a confirmed LDLR, APOB or PCSK9 variant, or a specific lipid profile, the number of patients visible in site databases may be a fraction of those who could actually qualify. It raises an important question for sponsors: are feasibility assessments measuring the patients who exist, or just those already known to the healthcare system?

Finding people who haven't been diagnosed or tested takes time. And when testing only begins at site activation, it can become a significant constraint on enrollment.

Rare disease programs have been dealing with similar challenges for years. There's a lot cardiovascular sponsors can learn from how those programs approach patient identification and testing earlier in development.

🔎 Our latest article explores what cardiovascular genetic medicine can learn from rare disease patient-finding approaches, and why feasibility needs to be considered much earlier.

Read it here: https://t.co/MV3fJ5eWRq

[Sano Genetics @sanogenetics]

Thousands of AI agents have been set loose on past drug trials to work out why so many of them fail.

A recent episode of The Genetics Podcast with Dr. James Zou (Stanford University) covers a virtual biotech built from AI agents that read trial papers, company reports and news coverage, then tested what separates drugs that reach the market from those that don’t.

Our recap blog covers some of the key takeaways include:
✅ Drug targets backed by single cell evidence were more likely to make it to market
✅ Adding more agents to a project is emerging as a new way to scale AI, alongside building bigger models
✅ The agents only learn from public data, so failed trials that never get published leave a gap
✅ Turning papers into agents also tests whether the science can be reproduced

Read it here: https://t.co/zb7SLLjk21

[Sano Genetics @sanogenetics]

🫀 Cardiovascular genetic medicine is about to need a different way of finding patients.

Gene editing and Lp(a)-targeted therapies define eligibility by genotype or biomarker level. The people who qualify sit inside populations of millions, and most have never been tested.

Our latest article covers:
✅ How rare disease programs already find people before they reach sites
✅ What the FH and Lp(a) evidence shows about testing gaps
✅ How feasibility, testing and cohort planning change for trial strategy

Sponsors who treat testing as infrastructure and cohorts as reusable assets will start each new protocol with a known population in place.

🔗 Learn more here: https://t.co/hasl1sKn1S

[Sano Genetics @sanogenetics]

🎙️Tune in to Episode 261 of #TheGeneticsPodcast 🚀

This week, we’re joined by Dr. James Zou, Associate Professor of Biomedical Data Science at Stanford University. Listen in to hear how a virtual biotech of AI agents found that drug targets backed by single cell evidence are more likely to reach the market, how papers can become agents that reproduce their own results, and how one night of sleep can predict risk for over 100 diseases.

Available here👇

🎧 https://t.co/zTFIOgKf4y 
📽️ https://t.co/GssrZ4drOg

[Sano Genetics @sanogenetics]

https://t.co/ndhgCSHK8u

[Sano Genetics @sanogenetics]

The FDA may be about to bring its rare disease flexibilities into one framework, and finding the right patients could become the hardest part of every program.

A notice seeking public input is under review at the Office of Management and Budget, according to Citeline. It has not been published yet, so the detail is still unknown.

✅ FDA appears to be consolidating tools that sponsors already use into a single structure
✅ Several of these tools rely on natural history data and small groups of well-characterized patients
✅ Finding and characterizing those patients remains the sponsor’s job under any framework
✅ Everything so far rests on trade press reporting until the notice is published

Programs built on fewer patients leave less room for any one of them to be missed or poorly characterized. Sponsors who treat identification and genotype confirmation as protocol-stage decisions will be better placed when the framework lands.

🔗 Link to the full blog in the comments below.

[Sano Genetics @sanogenetics]

About 3–4% of IBD patients carry anti-IL-10 autoantibodies. In that subgroup, the HLA association rises from an odds ratio of about 2 to roughly 20–50.

Jeffrey Barrett calls it a molecular subtype that has been "hiding in plain sight for forty years." 🔎

On the latest episode of The Genetics Podcast, Jeff argues that common chronic diseases likely hold many more subtypes like this. Our new blog recaps his reasoning.

Three points from the recap:
1. Chronic disease lags cancer on stratification. Most cancers now have companion diagnostics, while common chronic disease has almost none.
2. Data access is the main constraint. Federated learning across biobanks could let models learn from a few million people. The barrier is governance, a human, ethical, and legal challenge LLMs won't speed up.
3. Clinical notes record treatment response. In Finland, a secondary-use registry covering up to 7 million people lets LLMs extract why clinicians switched treatments.

The blog also covers how Jeff uses AI without outsourcing his thinking, and what sequencing about 2.5 million SARS-CoV-2 genomes at Sanger taught him about biosecurity.

Read the full recap on the Sano blog: https://t.co/tmvRaQdwOi

Prefer audio? The full episode is available wherever you get your podcasts.

[Sano Genetics @sanogenetics]

https://t.co/RvZwmJ8SJl

[Sano Genetics @sanogenetics]

The sponsors running the most trials aren't necessarily the ones running the most rare disease trials.

Across the 1,571 sponsors behind a 4,565-trial dataset, absolute volume and rare disease concentration turned out to be different signals.

AstraZeneca runs 175 active trials, and 11% target a rare disease. Ionis runs 8, and every one does. argenx runs 14, 11 of them rare. Vertex runs 18, 56% rare.

A team scanning only the largest sponsors by volume will miss where these genetically defined populations are most dense. 61% of sponsors in the dataset run exactly one trial.

See the sponsor-by-sponsor numbers in our industry whitepaper.

🔗 Link in the comments below.