A very nicely written article (and I don’t say that often!)
And the overall premise is spot on: while it’s a shame that the drugs failed, it’s okay, because we want companies to be taking bets on targets that might result in the next big drug to save or prolong lives.
> In 2026, a BMJ Oncology analysis would give a clinical name to what had happened: “herding.” The authors estimated that nearly 49,000 patients had been enrolled in anti-TIGIT trials by pharmaceutical companies, at a cost of more than $3 billion, all because their fellow pharmaceutical companies were doing the same thing
This is also spot on. I’ve been in the room when people have been infected by this peculiar competitive mania. Rational science takes a backseat to FOMO. But it’s also somewhat understandable: the model we have relies on companies making money to continue to exist and invest in further research and drug development. So of course, they all wanted a slice of the pie, no matter how wrong this was in retrospect. It’s just how the current system works, and it’s the least bad (?) system we’ve yet evolved for such sharing out of resources.
epistasis 1 days ago [-]
This blog has the best storytelling of any of the many biotech blogs I read, and is far more accessible to boot. I highly recommend subscribing to it if this interested you!
abhishaike 23 hours ago [-]
Thank you for the kind words :)
bobbiechen 24 hours ago [-]
It's just like a parallel of tech venture capital, where missing the next big thing is far more costly than making a wrong bet. No wonder we see herding in tech investments as well.
epistasis 23 hours ago [-]
I think it's a legitimate question to ask: how much capital should be redirected to studying this promising direction?
Is the herd effect wrong? This is not a simple question to answer with objective pareto-optimal answers for everyone.
If the promising direction pans out, having 3-5 drugs in the pipeline represents a far faster optimization problem, with far faster discovery, leading to more years of lives saved. Going slow, waiting for one drug to succeed or fail, learning maximally, then maybe trying another, may be dollar optimal, but has other risks: abandoning a good direction too early because of stochastic decision making (see for example the story of GLP-1 agonists which were delayed for decades because of optimizing to avoid me-too diabetes injectables), and also not exploiting a very promising target that pans out well in the first trial.
The speed issue is also one reason that trials are so expensive, and why drug discovery is limited in what we can try. If there were lower barriers to entry for trials--that somehow maintain the same safety characteristics--then perhaps we could learn faster and better and more cheaply. And the quote talks about a very important thing: there's only so many data points we can collect because there's only so many people who can ethically go on to a clinical trial in the first place. How we optimize the best outcome for those clinical trial patients, and the best outcome for society in general from what we learn from the trials, does not have a clear and obvious answer. This is why ethics classes belong in the curriculum of all advanced bio degrees, IMHO!
yes_man 23 hours ago [-]
Also if it turns out in the end the next big thing that everyone bet on just wasn’t it, you don’t stand out. But if it did work out and you missed the train you come out looking like a fool. There is asymmetry in the downsides for your career between these two options
choilive 17 hours ago [-]
Everything is obvious in hindsight, but the data (and theory) at the time was that this had a really good shot at being the next big thing in a world where >90% of drugs never make it past clinical trials. 10% probability of success * $200B in lifetime sales (assuming a Keytruda level smash hit) means an EV of ~$20B or more. Not a surprise more than a few companies wanted a shot at it.
doctorpangloss 17 hours ago [-]
Actually a lot of drugs make it all the way through to approval from Phase I. I think you probably mean preclinical to approval. It's something like 33% across all therapy areas in the USA, and in some areas closer to 50%.
epistasis 1 days ago [-]
At the same time we see TIGIT targeted drugs failing, we are seeing the success of drugs against another white whale of cancer drug targets: KRAS.
It's the most frequently mutated cancer activating gene out there, but has been declared "undruggable" for the 15-20 years I've been close enough to drug developers to have heard about it.
Yet we're seeing clinical successes in recent trials with Revolution Medicine's daraxonrasib, and now there's blood in the water, with tons of new approaches going after it.
The progress in biotech in the past few decades has been unbelievable, and lots of things that were considered impossible a few decades ago are now happening left and right. Whenever I hear that somebody thinks that technological progress has stopped, I just think that they've stopped looking in the right places for the huge advances that are going on.
mft_ 23 hours ago [-]
Agree. It's incredible that for pancreatic cancer (which is one of the single-most lethal and difficult-to-treat cancers there is) we're moving from a choice between a couple of decade-old and brutal chemotherapy cocktails, to discussions about sequencing and even the possibility of chemotherapy-free treatment, in a single generation of new agents.
strbean 22 hours ago [-]
What's the status of just using CRISPR to fix pathogenic variants?
epistasis 21 hours ago [-]
For fully formed humans? Not great, with exceptions where you can target a 2D layer of cells that are covered in fluids. I've seen some people trying this with, say, retinas, where you only need to target some of the cells. And also with intestinal/colorectal linings, where cells reproduce quickly so there's a chance of actually replacing all of the cells eventually. But for most of the body, I don't think anybody has ever been optimistic about CRISPR fixing pathogenic variants.
CRISPR is a great tool, but it doesn't help with delivering gene therapy across the body.
strbean 19 hours ago [-]
So for masses of solid tissue, we can't effectively deliver the payload to all the cells?
How about for lymphomas?
kazinator 1 days ago [-]
Anti-amyloid drugs work at reducing amyloid plaques. Only problem is that the idea that those plaques are the root cause for Alzheimer's was academic fraud.
mrkeen 21 hours ago [-]
I love that failure is an option. Despite all the pressure from money, the science still won out.
In a parallel universe, it would be hard to imagine that much money not altering the reported results significantly.
rad-b 23 hours ago [-]
A greatly written article! Really a masterclass of (1) getting the point across and (2) being emotionally engaging all the while not requiring me to have a PhD in pharmacology to understand it.
y1n0 23 hours ago [-]
I enjoyed reading this. It gives some insight as to some reasons drugs can be expensive. Amortizing the cost of research and studies, which I think is well understood by many people, but this article presents it nicely. Especially when those costly studies don't pan out.
sijourneyweezer 23 hours ago [-]
I don’t know how you type out an acronym 40 times and not say what it is the letters stand for. Even if the author doesn’t think it’s worth knowing, which in the long run it isn’t, how could I know that without hearing what it stands for? I had to leave the page to look it up.
bglazer 21 hours ago [-]
Gene names aren't really acronyms in the traditional sense. Often they were originally conceived as acronyms at the time of the gene's discovery and naming, but the original acronym frequently reflects an incomplete or factually wrong understanding of the gene. For example, TP53 is a very very important gene in cancer. TP53 originally meant Tumor Protein 53, where the 53 signified its molecular weight of 53 kilodaltons. The problem is that the experiment used to measure TP53's molecular weight was incorrect and TP53 actually weighs about 44 kilodaltons. Oops, now we're stuck with TP53 for eternity. There are a ton more examples of this.
So, in biology a gene's name is sometimes an acronym but it's meaning is generally forgotten
> TIGIT (/ˈtɪdʒɪt/ TIJ-it;[5] also called T cell immunoreceptor with Ig and ITIM domains) is an immune receptor present on some T cells and natural killer cells (NK).
1 days ago [-]
CyberFreeBird 13 hours ago [-]
[flagged]
Rendered at 18:23:28 GMT+0000 (Coordinated Universal Time) with Vercel.
And the overall premise is spot on: while it’s a shame that the drugs failed, it’s okay, because we want companies to be taking bets on targets that might result in the next big drug to save or prolong lives.
> In 2026, a BMJ Oncology analysis would give a clinical name to what had happened: “herding.” The authors estimated that nearly 49,000 patients had been enrolled in anti-TIGIT trials by pharmaceutical companies, at a cost of more than $3 billion, all because their fellow pharmaceutical companies were doing the same thing
This is also spot on. I’ve been in the room when people have been infected by this peculiar competitive mania. Rational science takes a backseat to FOMO. But it’s also somewhat understandable: the model we have relies on companies making money to continue to exist and invest in further research and drug development. So of course, they all wanted a slice of the pie, no matter how wrong this was in retrospect. It’s just how the current system works, and it’s the least bad (?) system we’ve yet evolved for such sharing out of resources.
Is the herd effect wrong? This is not a simple question to answer with objective pareto-optimal answers for everyone.
If the promising direction pans out, having 3-5 drugs in the pipeline represents a far faster optimization problem, with far faster discovery, leading to more years of lives saved. Going slow, waiting for one drug to succeed or fail, learning maximally, then maybe trying another, may be dollar optimal, but has other risks: abandoning a good direction too early because of stochastic decision making (see for example the story of GLP-1 agonists which were delayed for decades because of optimizing to avoid me-too diabetes injectables), and also not exploiting a very promising target that pans out well in the first trial.
The speed issue is also one reason that trials are so expensive, and why drug discovery is limited in what we can try. If there were lower barriers to entry for trials--that somehow maintain the same safety characteristics--then perhaps we could learn faster and better and more cheaply. And the quote talks about a very important thing: there's only so many data points we can collect because there's only so many people who can ethically go on to a clinical trial in the first place. How we optimize the best outcome for those clinical trial patients, and the best outcome for society in general from what we learn from the trials, does not have a clear and obvious answer. This is why ethics classes belong in the curriculum of all advanced bio degrees, IMHO!
It's the most frequently mutated cancer activating gene out there, but has been declared "undruggable" for the 15-20 years I've been close enough to drug developers to have heard about it.
Yet we're seeing clinical successes in recent trials with Revolution Medicine's daraxonrasib, and now there's blood in the water, with tons of new approaches going after it.
The progress in biotech in the past few decades has been unbelievable, and lots of things that were considered impossible a few decades ago are now happening left and right. Whenever I hear that somebody thinks that technological progress has stopped, I just think that they've stopped looking in the right places for the huge advances that are going on.
CRISPR is a great tool, but it doesn't help with delivering gene therapy across the body.
How about for lymphomas?
In a parallel universe, it would be hard to imagine that much money not altering the reported results significantly.
So, in biology a gene's name is sometimes an acronym but it's meaning is generally forgotten