If that was correct we’d expect that FDA approval process tends to vary by how quickly manufacturing capacity can be ramped up.
I think the right reference class here is emergency use authorizations, not FDA approval. Under my model, yes, their emergency use authorization timeline would vary by the speed of the manufacturing ramp-up. I might expect a global pandemic context to be a special context even among EUAs, but let’s not add epicycles at this point.
Another factor to consider is that the interplay of manufacturing and regulation may vary depending on whether the treatment is among the first, or whether it is a latecomer. For example, Novavax only got preliminary data on its vaccine in Jan 2021. It’s only started scaling up after that. When they got the prelim data, the CEO said it would take 5-6 months to scale up to 150 million doses.
So why is it that they can get to that scale in 5-6 months, if it took 8 months+ to scale up vaccine production for Pfizer or Lonza, as I claim? One possibility is that their vaccine is easier to manufacture than others. Another is that they’re able to use global production capacity that’s been scaled up over the last year and a half.
A third, of course, is that it really doesn’t take more than 5-6 months to scale up to 150 million+ quality-controlled doses if you’re adequately incentivized. But then, we’d expect Pfizer to be cranking out that much right now. The number is closer to 20 million per month, even though they’re a much bigger company than Novavax.
Furthermore I find it highly unlikely that producers would ramp up production just as quickly before results of trials are in as afterwards.
Novavax’s decision to start scaling up only after getting their prelim results is some evidence in favor of your argument. Likewise, the fact that, back in July 2020, Operation Warp Speed offered them $1.6 billion to scale up, but only after their data was in showing that the vaccine works.
It makes perfect sense that companies are risk-averse, and don’t want to invest lots of money in scaling up without good data showing it’s not going to be a wasted investment. An HCT would give them that confidence faster.
The overwhelming evidence I’ve ever seen is that politicians and government orgs are highly inneficient. My prior on them being efficient here is extremely low.
The important comparison here is the inefficiency of the politicians vs. the difficulty of the manufacturing process. It doesn’t matter how slow the politicians go if the manufacturing scale-up process is even slower than that.
Anyway, I find that overall, there’s reason to think and some evidence in favor of regulation being an important source of slowdown. I find your argument that HCTs could have reduced uncertainty around trial outcomes, thus accelerating scale-up efforts, particularly compelling.
At the same time, the discrepancy between Pfizer and Novavax’s current and projected scale-up timelines, even months post-EUA for Pfizer, makes me think that manufacturing must also be an important source of slowdown. That may be a tighter constraint than improving regulation. We can’t kick science and manufacturing to make it go faster, but sometimes we can kick the government to make it go.
From a political perspective, then, it makes some sense to focus on the failures of government. From an epistemic standpoint, though, I think we really do need to spend more time understanding the manufacturing problems.
I think the right reference class here is emergency use authorizations, not FDA approval. Under my model, yes, their emergency use authorization timeline would vary by the speed of the manufacturing ramp-up. I might expect a global pandemic context to be a special context even among EUAs, but let’s not add epicycles at this point.
Another factor to consider is that the interplay of manufacturing and regulation may vary depending on whether the treatment is among the first, or whether it is a latecomer. For example, Novavax only got preliminary data on its vaccine in Jan 2021. It’s only started scaling up after that. When they got the prelim data, the CEO said it would take 5-6 months to scale up to 150 million doses.
So why is it that they can get to that scale in 5-6 months, if it took 8 months+ to scale up vaccine production for Pfizer or Lonza, as I claim? One possibility is that their vaccine is easier to manufacture than others. Another is that they’re able to use global production capacity that’s been scaled up over the last year and a half.
A third, of course, is that it really doesn’t take more than 5-6 months to scale up to 150 million+ quality-controlled doses if you’re adequately incentivized. But then, we’d expect Pfizer to be cranking out that much right now. The number is closer to 20 million per month, even though they’re a much bigger company than Novavax.
Novavax’s decision to start scaling up only after getting their prelim results is some evidence in favor of your argument. Likewise, the fact that, back in July 2020, Operation Warp Speed offered them $1.6 billion to scale up, but only after their data was in showing that the vaccine works.
It makes perfect sense that companies are risk-averse, and don’t want to invest lots of money in scaling up without good data showing it’s not going to be a wasted investment. An HCT would give them that confidence faster.
The important comparison here is the inefficiency of the politicians vs. the difficulty of the manufacturing process. It doesn’t matter how slow the politicians go if the manufacturing scale-up process is even slower than that.
Anyway, I find that overall, there’s reason to think and some evidence in favor of regulation being an important source of slowdown. I find your argument that HCTs could have reduced uncertainty around trial outcomes, thus accelerating scale-up efforts, particularly compelling.
At the same time, the discrepancy between Pfizer and Novavax’s current and projected scale-up timelines, even months post-EUA for Pfizer, makes me think that manufacturing must also be an important source of slowdown. That may be a tighter constraint than improving regulation. We can’t kick science and manufacturing to make it go faster, but sometimes we can kick the government to make it go.
From a political perspective, then, it makes some sense to focus on the failures of government. From an epistemic standpoint, though, I think we really do need to spend more time understanding the manufacturing problems.