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Comment by axg11 | Hacker News Reader

As mentioned in the article, pharma is high-risk high-reward long term investment. Many companies tried and failed to produce an effective COVID vaccine (CureVac, Merck). Those companies invested R&D dollars well before the pandemic and further invested in recent trials. When a pharma company finds a winner, of course the margin will be great, the raw materials for most medicines are cheap relative to the value they bring. But the margin doesn't account for the 10+ years of R&D spend that put Moderna into the position it is in today.

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> But the margin doesn't account for the 10+ years of R&D spend that put Moderna into the position it is in today.

This and the associated economics seem like the central problem with biotechnology funding.

The timescales and likelihood of success preclude anything under the size of government investment. Even Gates can only nibble around the edges.

Private investment in biotech dwarfs what the US puts in right now.

US gov't (NIH) = $41.6B (not all is for biotech) Biggest pharma (PhRMA members) R&D = $83B VC biotech funding + follow-on rounds = $62B

So private funding is ~3.5x what the gov't spends today.

The Gov't is often the earliest and most risk tolerant investor. I'm mainly thinking of DARPA (who funded Moderna to the tune of $25M if my memory serves) here, I have much less knowledge of how risk tolerant NIH is.

As the article points out, Modern was spending 500M a year on research before becoming profitable. Do you think a one time payment of 25M from DARPA was in any way relevant to their success?

If it happened at the very beginning when they had exactly $0 then yes it would be highly relevant. (I am not at all familiar with how Moderna got started)

Is there any good/better way to derisk this process? It's always seemed quite inefficient to me

It seems that at some level, that is exactly the purpose of (publicly-funded) basic research on the academic side. For example, the idea of messenger RNA therapy has been around for decades in the academic literature, and of course this idea was only possible with preceding advancements in general biology.

https://www.schrodinger.com/

totally off topic, but can't resist.

on their careers page, under Software Engineers there is this listing:

> Developer, Force Fields

haha, yes, the scifi nerd dream job. :)

I assume it's referring to this: https://en.wikipedia.org/wiki/Force_field_(chemistry)

And developing software similar to: https://openforcefield.org/software/

Protein folding helps some. Genomics help some. Computer models help with pharmacokinetics.

But all these things help you get to the expensive part more easily: testing safety and efficacy.

https://www.science.org/content/blog-post/big-problems

https://www.science.org/content/blog-post/ai-and-drug-discov...

https://www.science.org/content/blog-post/another-ai-generat...

Or to put it in other terms, "better" ways mean billions of dollars of avoided loss / additional profit to pharma companies doing drug development.

Suffice to say, there are a lot of people always looking at this, and a lot of money being spent on it.

It's just hard.

Read up on Andrew Lo. He used his background in finance to develop better biotech financing models.

https://www.bloomberg.com/news/articles/2021-02-05/out-of-gr...

Currently, no. Bringing a new drug to market takes several years and a billion dollars. And I haven't seen any compelling evidence that anyone is working on a "solution".

A large part of the cost and complexity is the clinical trials, of which several stages of increasing complexity are required to get FDA approval. While it might seem inefficient, it's also the reason why vaccines work so well and have such small side effects - about half of Phase 3 candidates either fail the clinical trial or are rejected by the government for some other reason.

Each round of clinical trials requires significantly more money than the one before it, but it's also arguably easier to get investors on board in the later rounds as risk is lower. Smaller pharma companies often aim to get bought outright before their product is brought to market because it makes more sense for everyone involved.

Maybe someone will come up with an algorithm that lets us simulate clinical trials with sufficient precision, but that seems like a far-off sci fi fantasy to me.

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