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.

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bobbiechen an hour ago | parent | next [-]

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 43 minutes ago \| parent \| next [-]
		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 43 minutes ago \| parent \| prev [-]
		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

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epistasis 2 hours ago | parent | prev [-]

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 15 minutes ago \| parent [-]
		Thank you for the kind words :)