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The FDA approves more than 99% of single patient "compassionate use" applications.
That's great, but I suspect it's because the application process is way too onerous, and a lot of applications die silently before submission.
We've heard excellent centers tell patients they have "extremely limited" capacity to do expanded access applications due to the work required.
I think it'd be much better for patients if we did 20x the number of compassionate use programs, even if the approval rate comes down significantly.
Hugely frustrating fact about cancer:
The best centers (Sloan Kettering, Stanford, UCSF, etc) typically run patients through in-house genomic diagnostics that are incredibly limited (small gene panel; no RNAseq).
So many of our cases are folks who've had MSK-IMPACT or Stanford STAMP and UCSF 500 that missed obvious things. They're well behind Caris and Tempus, and miles behind Valius.
They're at the cutting edge of everything else; very sad that they're so behind here.
What happened to the LAGE-1 TCR-T?
Angiosarcoma patient of ours has profoundly elevated LAGE-1 levels (99th percentile versus basically every cohort in our database.)
Lete-cel registrational trial (in sarcomas!) seemed to be positive 2 years ago, but then never filed after Adaptimmune sold assets to US World Meds?
Edward Larkin retweeted
Today, we’re announcing mBER-2, our latest AI protein design system, and sharing a bit of how we use it to explore biology in vivo at scale.
For a long time, we’ve been working on what I think of as “frontier problems” in medicine and biology. We know a lot about the targets involved in disease, but there is still an enormous amount of biology we haven’t explored, and potential uses for that biology we haven’t discovered.
One of those problems is drug delivery. There are thousands of potential targets, and most receptors remain unexplored as routes for delivering medicines. We want to understand which ones we can use, where they can take a drug, and what kinds of molecules make that possible.
Ultimately, we need to test these molecules in vivo to understand what they actually do. We’ve spent the last six years building measurement technologies that let us generate millions of measurements in living systems.
mBER-2 is an AI protein design system built for that scale of in vivo measurement. Our goal is to generate binders across the full range of targets we care about, while systematically exploring the possible binding sites on each one. It’s not just what you bind, but where and how you bind that determines whether a molecule performs it's function. mBER-2 lets us probe those differences at massive scale.
We’re also sharing a look at some of our in vivo data. We’ve designed more than 50 million molecules across over 4,000 targets, and screened millions of molecules in living systems. By probing hundreds of receptors, we’ve found new pathways for delivering genetic medicines into fat that outperform industry benchmarks by a large margin.
This is just the beginning. We call the space of all bindable sites across proteomes the EpiTome. As we explore it, we’re building the data to connect AI design, receptor binding, and what a molecule actually does in a living organism.
Much like the idea of a virtual cell, we envision a Virtual Organism that helps us design medicines with specific properties in mind. The foundation is our own in vivo data, connecting molecular design to outcomes measured in living systems.
The endgame is to use AI to explore more biology, measure what happens, and use what we learn to design better medicines. Every round should deepen our understanding of biology and improve our ability to build molecules that do what we need them to do.
Edward Larkin retweeted
I'm a cancer genomics expert and I want to analyze sequencing data from my friend's cancer, in my spare time and with my personal account.
Will I be granted access? Will report back with the answer.
Today we’re opening applications for the Life Sciences Verification Program.
Through the LSVP, life science professionals can use our models—including, for the first time, Mythos—with a new set of safeguards designed to enable the full range of biology-related work. We designed these new safeguards to provide a better experience for biologists and more protection from risk of misuse.
The program is launching in beta for teams of all kinds—from academic labs to startups, pharma companies, and more. We will continue to improve the program and expand access to individual Pro and Max plans over time.
Learn more about these access grants and apply: anthropic.com/news/life-scie…
Beauty of single cell: you can salvage samples that are 1% tumor purity, and get important insights to oncologists & patients.
Second time this week that we've resurrected <10% purity samples which failed standard assays.
Metastatic biopsies are often messy, with low tumor content. Single cell is a major unlock.
Edward Larkin retweeted
Starting a new thing at UNC with @BenjaminGVincen to figure out which tumor-specific pMHCs are actually on tumor cells & which vaccine platforms are more immunogenic.
Lots of long-read WGS/scRNA, targeted mass spec, tumor-specific TCRs coming your way in the near future
Half the battle in helping cancer patients involves just being a dog on a bone operationally, and having a maniacal ops team.
We're working with a young adult patient whose samples had failed sequencing three times before we met them.
We tried 4 more times, and failed every time. Tumor content too low. Necrosis. DNA / RNA extraction failed.
Finally, we managed to get some results on single cell RNA sequencing. Tumor cells were only 9% of the total, but they were present.
When looking at those tumor cells, we saw clear signs of enrichment in a T-cell therapy target.
But we also needed to know their HLA type, and we couldn't infer that from our single cell assay. So we did a Guardant 360 blood test, and fortunately, got the exact HLA type we were hoping for.
A few days ago, we got the terrible news that the patient progressed. Now, this therapy might be the best option.
Our ops team works with feral persistence for exactly these situations, even before it's urgent.
In cases of recurrence, we want all the data to be on the table, so patients and physicians can make the best possible decisions.
@Valius_Sciences
Edward Larkin retweeted
Reporting is impossible without people in the field guiding and helping you. Very grateful to Edward and others for helping me point me towards the right people and sharing their experience on the ground.
It was fun to talk with @RuxandraTeslo as she developed what became this excellent and very thoughtful piece.
We deal with the challenges around clinical trial access and single patient INDs every day at Valius - it is the single biggest challenge we face.
In a world where we get better at forecasting a priori what drugs are likely to work for patients (an area I think we're improving and will continue to do so), it's critical to have an agile trial infrastructure and streamlined ways for patients to access drugs outside of the often very narrow inclusion/exclusion criteria.
This is especially the case for patients that have it hardest, like rare cancer patients where there are usually fewer trial options.
It's a very important thing to get right, so I'm glad that Rux and folks like @DavidHongMD are pushing for change. We are too @Valius_Sciences.
It was fun to talk with @RuxandraTeslo as she developed what became this excellent and very thoughtful piece.
We deal with the challenges around clinical trial access and single patient INDs every day at Valius - it is the single biggest challenge we face.
In a world where we get better at forecasting a priori what drugs are likely to work for patients (an area I think we're improving and will continue to do so), it's critical to have an agile trial infrastructure and streamlined ways for patients to access drugs outside of the often very narrow inclusion/exclusion criteria.
This is especially the case for patients that have it hardest, like rare cancer patients where there are usually fewer trial options.
It's a very important thing to get right, so I'm glad that Rux and folks like @DavidHongMD are pushing for change. We are too @Valius_Sciences.
We are on the cusp of a wave of new therapies for some of the worst diseases. But the world won’t benefit unless the US fixes its drug regulatory system. My new essay for @nytimes, on how slow clinical trials are now the biggest obstacle to curing cancer.
nytimes.com/2026/09/04/opini…
- I interviewed dozens of researchers, especially oncologists at leading U.S. centers. A striking consensus emerged: science is no longer the main bottleneck to new cancer drugs. It is our ability to test discoveries in patients through clinical trials.
- The cost of starting a Phase 1 trial in America has roughly doubled over the past decade. As a result, companies increasingly take early trials abroad: Australia’s Phase 1 trial volume has nearly doubled in a decade, driven mainly by U.S. companies.
- Unfortunately, the underlying incentives are badly asymmetric: Institutions can be blamed for harms caused by moving too fast, but almost no one is blamed when patients deteriorate during avoidable delays. One doctor called the emerging system “ritualized safety over actual risk assessment.” Or as, @DavidHongMD put it: "We often forget that the biggest risk is the cancer itself."
- This problem is becoming more urgent because medicine itself is changing. Sequencing, biological engineering and A.I. make increasingly personalized therapies possible. But our regulatory system was mostly built for standardized drugs tested in large populations.
- @sytse, the co-founder of GitLab, shows what personalized medicine can achieve: after relapsed osteosarcoma and being told there were no options left, he pursued a highly individualized approach and has now been cancer-free for a year. But doing so required extraordinary resources and regulatory expertise.
- Pierce Ogden’s father was less lucky. After molecular analysis identified a drug that might target his glioblastoma, the manufacturer agreed to provide it. But administrative barriers delayed access until it was too late. “My dad was ready to try anything,” Pierce told me. “But the system is paternalistic.”
- The A.I. revolution is making this bottleneck more important, not less. A.I. relies on relevant data. Information from early-stage trials could compound with A.I. tools to achieve truly revolutionary medicines. Without the data, this is far less likely to happen.
- Another important shift is that innovation increasingly comes from academic labs and small biotech companies rather than Big Pharma. These small companies find it far harder to unable to absorb delays and regulatory barriers.
- Apart from cancer, China is the biggest winner from America's outdated medical regulations. China has a much faster trial system, with testing often starting a full year earlier. This allows Chinese pharmaceutical companies to experiment and improve medicines while American companies play with mice. Today, half of all drugs licensed by major pharmaceutical companies originate there, up from less than 5 percent only a decade ago.
- But we don't need to copy China. The best model is Australia: lots of on-site scientific and ethics reviews, and requirements that are proportionate to small, early-stage trials. Phase 1 studies there begin roughly 6–12 months faster, without any notable increases in adverse safety events.
- Operation TrialBlazer, a 2026 HHS initiative is a good start in this direction, but we need legislative action by Congress to truly make Phase I trials faster and more efficient!
I want to thank everyone who helped me with this article: everyone I interviewed and the amazing editors at the Times. This is the result of a months long journey of extensive interviews and research.
Special thanks go to those who came on the record. One of the features of the system is an atmosphere of fear, where practitioners are afraid to publicly come out and explain these issues. So anyone who does is a hero in my book!
I agree with this, but there is one one area it may be too conservative.
If we do start to generate therapies that have true step change efficacy *with* low tox, we will start to see really excellent response rates (90%+) in early stage trials.
If that happens, my expectation is that all the downstream trials will go much faster, or, at the limit, be unnecessary.
I don't *necessarily* expect this to happen soon (a lot of things to solve), but if we do start reliably hitting early stage home runs, I think the entire trial and approval apparatus will speed up immensely.
NewLimit CEO @jacobkimmel explains the clinical trial bottleneck AI can't fix: biology has irreducible latency
"It's unlikely we're gonna see rapid expedition of clinical trials because a lot of the periods of time can't be expedited based on the biological primitives. In computer science you often have a notion of bandwidth and latency."
"There are some irreducible latencies in clinical trials. If you wanna give a drug to a patient and see if they're healthy a month later, it's not like you can enroll 30 patients and check back in one day. You just need to wait a full month. The classic nine people can't have a baby in one month story."
"The other thing we can hope for is that the success rate might improve. Right now, rough math, roughly 9 out of 10 drugs that go into the clinic will ultimately fail."
"If you're able to even just 2X that success rate, getting to 20% success rather than 10%, then you would double the number of drugs that come out every year, and you would cut the effective cost in half."
@newlimit
Maximum sequencing (DNA + RNA) at every timepoint. Liquid biopsies in between.
Each new sample is a chance to learn something new and treat differently.
Play to win 💯
Fantastic. Practically, given the somewhat permissive label, will daraxonrasib used liberally in first line?
I'd love to understand the steelman for not using it first line, more than just the trial not being done yet.
Public markets investing is very hard, but every once in a while, it's obvious.
Highest conviction I've ever felt about a stock was 10X Genomics early last year. Here's what I wrote in Feb 2025.
Great competitive position, great management, and perhaps most importantly, great balance sheet (thus, effectively unkillable), just waiting to get its mojo back amidst a "once in a century" negative macro environment.
$12/sh then, $64 now.
