Sano combines genetic testing, recruitment, and long-term engagement in one platform, accelerating enrollment and simplifying operations for precision medicine
Cambridge, England
Joined August 2017
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🎙️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👇
🎧 eu1.hubs.ly/H0zv69Q0
📽️ eu1.hubs.ly/H0zv69m0
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: eu1.hubs.ly/H0zcLLV0
🧬 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: eu1.hubs.ly/H0zqYzb0
🧬 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: eu1.hubs.ly/H0zlZX-0
🎙️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👇
🎧 eu1.hubs.ly/H0zjXhs0
📽️ eu1.hubs.ly/H0zjXht0
🔬 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: eu1.hubs.ly/H0zfrbc0
#PrecisionMedicine #ClinicalTrials #GeneEditing
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: eu1.hubs.ly/H0zcLdx0
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: eu1.hubs.ly/H0zbKky0
🫀 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: eu1.hubs.ly/H0z2Km10
🎙️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👇
🎧 eu1.hubs.ly/H0z6LQx0
📽️ eu1.hubs.ly/H0z6LPG0
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.
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: eu1.hubs.ly/H0yZ9vH0
Prefer audio? The full episode is available wherever you get your podcasts.
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.
🎙️Tune in to Episode 260 of #TheGeneticsPodcast 🚀
This week, we're joined by Dr. Jeffrey Barrett, Group Leader at the Ellis Institute Finland and the University of Helsinki. Listen in to hear how AI and biobank data could reveal molecular subtypes of common chronic disease, how LLMs can read clinical notes to uncover treatment response, and what the COVID sequencing effort means for biosecurity.
Available here👇
🎧 eu1.hubs.ly/H0yY-jL0
📽️ eu1.hubs.ly/H0yY-fM0
📣 The FDA has approved Emcitate for peripheral thyrotoxicosis in MCT8 deficiency, an ultra-rare genetic condition and the first approved therapy for symptoms of the disease.
In ultra-rare development, the same very small patient population may need to support clinical trials, natural history studies, external controls and long-term follow-up.
In our latest blog, we cover:
✅ How the Emcitate evidence base was assembled
✅ Why delayed diagnosis shrinks the pool of patients available to study
✅ Why earlier genetic identification, longitudinal data and recontact matter across an entire development program
With an approved treatment now available in the US, a late diagnosis can also mean delayed access to therapy. How sponsors find, characterize and follow patients will shape how much evidence can accumulate before and after approval.
🔗 Learn more via the link in comments.
🧬 Most resourcing plans treat genetic eligibility infrastructure as a late-stage problem.
We looked at when that infrastructure actually gets tested, across a 4,565-trial dataset spanning five markets.
51% of combined Phase 1/2 trials involve genetics, roughly double the rate of single-phase studies. Across the 553 trials in that category, the genetic eligibility burden shows up earlier in a program's life than a standard phase-by-phase plan assumes.
Teams that wait for late-phase trials to build genetic recruitment infrastructure are already behind.
📖 Explore the phase-by-phase breakdown here: eu1.hubs.ly/H0yxLhz0
🫀 About half of the Danon disease community supported by The Danon Foundation is female. Around 60% of known members have had a heart transplant, a rate on par with males.
So why are women with Danon disease still being missed?
Our new blog recaps the latest episode of The Genetics Podcast with Jenny Hsieh, Co-Founder and President of the Danon Foundation.
Key takeaways include:
🔹 Why women's cardiac symptoms are often dismissed or misdiagnosed
🔹 How the diagnostic gap can be closed for Danon
🔹 The current therapeutic landscape for Danon and how trials can be made more accessible for rare disease families
Read the full recap here: eu1.hubs.ly/H0yQXcR0
#DanonDisease #RareDisease #GeneticTesting #PrecisionMedicine
