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Speaker 1: Hey, Oddlots listeners, we're coming to DC.

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Speaker 2: We're finally doing it, Joe. It's going to be our

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Speaker 2: first live show in Washington, DC, our nation's capital. It's

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Speaker 2: also finally going to be the time where we actually

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Speaker 2: talk about the Jones Act.

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Speaker 1: Listen talk about doing the Jones Act episode of Odd

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Speaker 1: Lots for a long time, and it's become this recurring

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Speaker 1: joke that we've never done on But we're going to

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Speaker 1: do it in grand style because we're going to be

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Speaker 1: doing it live in DC and it's actually going to

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Speaker 1: be a debate.

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Speaker 3: Yeah.

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Speaker 2: So we have Sarah Fuentes from the Transportation Institute. She's

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Speaker 2: going to be taking the pro side, and we also

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Speaker 2: have Colin graybou of the Cato Institute. He'll be taking

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Speaker 2: the against side. It's going to be really interesting to

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Speaker 2: see how all of that shakes out.

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Speaker 1: In addition to that, we're going to be speaking with

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Speaker 1: Blair Levin, who was around during the telecom bubble, and

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Speaker 1: we have Andrew Ferguson, the new head of the FTC,

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Speaker 1: the one who's replaced Lina Kong. We're going to be

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Speaker 1: talking about mergers and acquisitions and all that stuff. So

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Speaker 1: it should be a really fun name.

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Speaker 2: If you want to come and join us for that evening,

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Speaker 2: it's going to be on March twelfth at the Miracle Theater.

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Speaker 2: Go to Bloomberg dot com forward slash odd Lots and

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Speaker 2: you can find the link to purchase tickets. We hope

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Speaker 2: to see you there.

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Speaker 4: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 1: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 1: I'm Jill Wisenthal and.

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Speaker 2: I'm Tracy Alloway.

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Speaker 1: Tracy, we're recording this February twenty fifth, and we're just

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Speaker 1: a little bit over a month into the new Trump

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Speaker 1: administration and sitting aside how one views the long term

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Speaker 1: goals of the administration. From the outside, it's chattic, and

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Speaker 1: there have been a lot of cuts, and it doesn't

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Speaker 1: look like scalpel types of cuts. Many things have been

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Speaker 1: shut down or frozen completely in some respect, and you

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Speaker 1: see a lot of people talk about the implications of

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Speaker 1: these moves, but I find it hard to wrap my

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Speaker 1: head around.

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Speaker 2: There's a lot happening, that's for sure, and I think

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Speaker 2: one of the struggles of everyone who's trying to follow

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Speaker 2: this at the moment is that you have new executive

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Speaker 2: orders coming out almost daily. Then you have the legal

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Speaker 2: challenges to those, and then you have the administration itself

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Speaker 2: sometimes arguing within itself about whether or not certain orders

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Speaker 2: should be followed, certain emails should be replied to. It

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Speaker 2: does feel very chaotic.

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Speaker 1: One area that I think has caught a lot of

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Speaker 1: people's attention, in particular, has been immediate moves at the NIH,

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Speaker 1: the National Institute of Health. And there are multiple moving

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Speaker 1: parts here, but from what I've been understand that when

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Speaker 1: there are grants awarded, some of the money goes to

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Speaker 1: direct costs and some of it's indirect, and there has

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Speaker 1: been a change in what's allowed on the indirect And

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Speaker 1: then I've also seen, and it's almost hard to believe,

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Speaker 1: but I've now seen it in an places that there

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Speaker 1: have been like whole programs that got hit with an

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Speaker 1: immediate freeze, which I have to imagine in science creates

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Speaker 1: all sorts of problems imagined. There are lots of endeavors

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Speaker 1: that can't just be stop started on a switch, and

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Speaker 1: so understanding what's actually happening right now in science funding

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Speaker 1: and the implications of some of these moves is obviously

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Speaker 1: something we need to discuss, especially given the broader thing

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Speaker 1: of like we want to be, I think, a country

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Speaker 1: at the leading edge of science and technology.

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Speaker 2: Yeah, this is exactly the tension.

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Speaker 5: I think.

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Speaker 2: So even Elon Musk and Donald Trump will talk about

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Speaker 2: how important it is for the US to be technologically

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Speaker 2: advanced and to beat competitors like China. But at the

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Speaker 2: same time they're doing this, and I guess their argument

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Speaker 2: is that by capping some of the cost of research,

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Speaker 2: you might make it more efficient, you might make it

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Speaker 2: more monetizable, you might see more breakthroughs. But again, there

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Speaker 2: is this tension to the sledgehammer approach that we've seen,

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Speaker 2: where if you just put in a cap on something

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Speaker 2: like indirect funding, it can affect a bunch of research programs,

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Speaker 2: So we should talk about it. I also, I must

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Speaker 2: admit I don't know anything about NIH funding. It seems

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Speaker 2: very sprawling and very complicated, so I want to get

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Speaker 2: into that as well.

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Speaker 1: All right, well, I'm very excited to say we have

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Speaker 1: the perfect guest, someone who actually is a scientist in

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Speaker 1: the lab working on these things and has been talking

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Speaker 1: about the impact of the moves over the last several weeks.

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Speaker 1: Thrilled to bring on to the show. Carol Lebon, Professor

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Speaker 1: of Molecular Biosciences at Northwestern University, Professor Lebon, thank you

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Speaker 1: so much for coming on the out Lots podcast.

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Speaker 3: Happy to be here.

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Speaker 1: What's happened in the last month?

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Speaker 3: Why don't you ask a broad question?

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Speaker 2: So we need forty minutes for just answering that question.

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Speaker 3: Fine, Three major things have happened in the last months.

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Speaker 3: One is what you alluded to already, this attempt to

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Speaker 3: cap the indirect costs for grants at fifteen percent. And

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Speaker 3: I'm happy to go into that. Well, but you can

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Speaker 3: think about it if you pick any area of research

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Speaker 3: that you might care about. Let's say pediatric cancer, that

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Speaker 3: basically amounts to a fifteen to twenty percent decrease in

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Speaker 3: funding for studying those cancers. And this is an area

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Speaker 3: where NIH has made huge progress. So forty years ago,

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Speaker 3: if your child was diagnosed with cancer, there was less

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Speaker 3: of a sixty percent of those children that would still

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Speaker 3: be alive in five years. Today there's a ninety percent

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Speaker 3: survival rate. So I mean, these are impacts that are

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Speaker 3: going to be huge.

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Speaker 1: I'm gonna actually stop you right there because I think

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Speaker 1: this is important. There is science research that happens outside

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Speaker 1: of the NIH. When you look at that forty year history,

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Speaker 1: how do you draw the line for someone who'd you know,

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Speaker 1: people are aware of big pharma companies exist that this

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Speaker 1: is like the NIH should get credit for that progress.

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Speaker 3: So the NIH is really sort of two different systems.

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Speaker 3: They have an intramural system. So there are scientists running

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Speaker 3: laboratories at the NIH, and by the way, they have

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Speaker 3: not been exempt from these cuts to probationary people. I

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Speaker 3: know of young scientists who have started their independent laboratories

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Speaker 3: at the NIH in the last couple of years, who

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Speaker 3: least a week ago Saturday received an email at night

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Speaker 3: saying that they were terminated and that their access to

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Speaker 3: campus was gone. So there's intramural research, but the vast

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Speaker 3: majority of NIH grant funds go into their extramural program.

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Speaker 3: These are funds that go to grants at universities in

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Speaker 3: all fifty states, and they are incredibly important for all

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Speaker 3: levels of science. Basic research, translational research, clinical trials are

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Speaker 3: going on not at the NIH, but in Iowa and

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Speaker 3: in Ohio and in Florida, and so you know, it's

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Speaker 3: estimated that these grant monies that come to the universities

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Speaker 3: they support directly about four hundred thousand or more employees

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Speaker 3: across those fifty states. But also they drive more more

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Speaker 3: than ninety three billion dollars of economic activity each year.

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Speaker 3: And again that's across all fifty states. That's not staying

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Speaker 3: in Washington, DC. If there's an estimate that for every

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Speaker 3: dollar of NIH grant money that is granted, it generates

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Speaker 3: almost two dollars and fifty cents worth of economic activity.

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Speaker 2: Just to press on Joe's point, how do you actually

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Speaker 2: measure the success of NIH funding? Is it you produce

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Speaker 2: some new wonder drug that is monetizable and everyone starts

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Speaker 2: using it. Is it just the sort of nebulous concept

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Speaker 2: I guess of like advancing scientific research, how do you

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Speaker 2: judge the success and efficacy?

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Speaker 3: So I think that you can look at that on

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Speaker 3: two levels. So there are the studies that are translational

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Speaker 3: studies that immediately impact human health, and those are very,

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Speaker 3: very important, but just as important as the kind of

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Speaker 3: research that the general public sometimes has trouble wrapping their

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Speaker 3: heads around because you don't necessarily see an immediate as

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Speaker 3: from that research in human health. But most of the

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Speaker 3: advances that we see today that are being translated and

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Speaker 3: are really making a direct effect on human health. They

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Speaker 3: stem from studies that were done ten or even twenty

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Speaker 3: years ago that were foundational that at the time, again

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Speaker 3: didn't This is called discovery based science or basic science

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Speaker 3: or fundamental research. But it's these foundations that are built

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Speaker 3: on in the more clinical studies and also in industry,

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Speaker 3: in the pharmaceutical industry and biotech, they are building on

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Speaker 3: these basic discoveries that were funded by the NIH. I

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Speaker 3: can give you one really cool example. So you've got

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Speaker 3: to have heard of ozenpic right, the semaglut hide that's

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Speaker 3: everywhere these days. Did you know that that came from research,

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Speaker 3: very basic research that was done ages ago on a

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Speaker 3: venomous lizard called a gilla monster. A gila monster at

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Speaker 3: this Scala monster is native to the southwest of the US,

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Speaker 3: and people noticed that. Scientists noticed that it had the

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Speaker 3: ability to fast for an incredibly long period of time,

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Speaker 3: so wanting to understand the biological mechanisms of how it did,

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Speaker 3: that led them to isolate from the saliva of a

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Speaker 3: Glian monster. This what would eventually become ozenpec.

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Speaker 1: Talk to us about the math of direct versus indirect costs,

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Speaker 1: because I think this is just something that was in

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Speaker 1: the headline of the announcement. And I don't think I

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Speaker 1: really have a concrete understanding of what the difference is,

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Speaker 1: why there is this distinct allocation. What do you walk

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Speaker 1: us through a year ago or just in the up

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Speaker 1: until recently, what this allocation was all about.

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Speaker 3: So every grant that goes to let's say Northwestern where

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Speaker 3: I am, has two components to it. Direct costs that

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Speaker 3: come directly to my research laboratory, and indirect costs that

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Speaker 3: are used to support my research. So a lab like

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Speaker 3: mine is analogous to running a small business. So let's

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Speaker 3: say that business as a restaurant. Ye costs or the

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Speaker 3: restaurant would be the food, the cooks, and the servers.

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Speaker 3: But there are other costs to running a restaurant business.

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Speaker 3: You have inventory and purchasing and upkeep the kitchen equipment

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Speaker 3: and the building, et cetera. And so the restaurant can't

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Speaker 3: run without those other things. It can't exist with just

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Speaker 3: the food, the cooks, and the servers. And so for

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Speaker 3: a research lab like mine, the direct costs are the

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Speaker 3: chemicals and the reagents and the salaries of the scientists

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Speaker 3: who are carrying out the studies. But there's also indirect costs,

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Speaker 3: and those again include maintenance and replacement of equipment, ordering, bookkeeping,

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Speaker 3: handling hazardous waste, compliance with government regulations, all of which

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Speaker 3: are absolutely essential to doing the research. And the reason

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Speaker 3: it gets separated out like this is actually to save

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Speaker 3: money because while the direct costs are specific to my

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Speaker 3: particular research, most of those indirect costs are for most,

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Speaker 3: if not all, research labs at a given university, and

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Speaker 3: so you get economies of scale by lumping those together.

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Speaker 3: Does that make sense?

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Speaker 2: Yeah, So going back to your restaurant analogy, I guess

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Speaker 2: it's kind of like your funding of food court, right,

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Speaker 2: So each individual restaurant within a food court might have

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Speaker 2: their own direct costs, but then the cost of actually

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Speaker 2: maintaining the space, renting out the space is sort of

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Speaker 2: shared by everyone doing different things.

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Speaker 3: Sure, I think that's a great advance on the analogy,

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Speaker 3: and it would be particularly so if they had a pooled,

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Speaker 3: let's say, central building, so that they didn't each have

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Speaker 3: to handle the finances individually.

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Speaker 2: So one thing that I was wondering is how the

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Speaker 2: NIH cap on indirect funding currently or I guess up

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Speaker 2: until a month or two ago, actually compares with other

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Speaker 2: medical research organizations because I'm thinking specifically about organizations like

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Speaker 2: the Gates Foundation, and I think in the announcement that

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Speaker 2: NIH talked about aligning with other types of research organizations

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Speaker 2: like the John Templeton Foundation. So how do private research

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Speaker 2: organizations manage to keep their own indirect costs down? And

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Speaker 2: why do their indirect costs seem lower than the nihs?

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Speaker 3: Oh, I mean they are lower, and there are in

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Speaker 3: fact some types of small foundations that have no indirect costs.

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Speaker 3: I remember back when I was a graduate student at Harvard.

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Speaker 3: Harvard wanted to stop letting their researchers take those grants

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Speaker 3: because they were not paying for the other real costs

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Speaker 3: of doing research. But in general, over time, what's come

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Speaker 3: out of it is in the research ecosystem, universities admit

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Speaker 3: that these smaller foundations and even something like the Gates Foundation,

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Speaker 3: you know, they're directing money at very specific things, and

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Speaker 3: they can do more with their work if they lean

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Speaker 3: on the NIH and universities for covering more of those

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Speaker 3: indirect costs. So they don't cover the indirect costs, it's

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Speaker 3: just that it's understood that they can't.

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Speaker 1: So your assertion here is that what appears to be

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Speaker 1: a finance mechanism with lower indirect costs is capable of existing.

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Speaker 1: Because I don't know if free writing is the right word,

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Speaker 1: but because there is also the indirect costs that come

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Speaker 1: from the NIH too aligned or the same labs.

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Speaker 3: Absolutely, they're like a lost leader.

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Speaker 1: So one of the arguments is that a lot of

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Speaker 1: NIH funding goes to a handful of extremely well endowed universities.

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Speaker 1: It's a skewed That also is something you hear a lot,

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Speaker 1: including from people academia, that there is a tremendous amount

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Speaker 1: of bureaucratic bloat that exists. And I think, setting aside

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Speaker 1: the science, it does not surprise me that the current

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Speaker 1: administration wants to take aim at bureaucratic bloat within America's universities,

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Speaker 1: et cetera. Why shouldn't we be skeptical of the degree

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Speaker 1: of costs that these labs have born.

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Speaker 3: I mean, I think you shouldn't be skeptical because these

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Speaker 3: are negotiated between Health and Human Services and the universities

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Speaker 3: through a very thorough process where they sit down and

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Speaker 3: really have to lay out what the costs are and

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Speaker 3: show proof that that's what the costs are. So this

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Speaker 3: isn't somebody picking the number out of the air. This

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Speaker 3: has been the result of negotiations to really figure out

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Speaker 3: what the costs are. And you know, most university would

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Speaker 3: tell you that even the negotiated interrect cost raise don't

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Speaker 3: cover all the costs of research.

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Speaker 2: This is a very wide ranging question. But going back

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Speaker 2: to Joe's point about the administrative burden, could you maybe

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Speaker 2: walk us through the process of getting an NAH grant, Like,

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Speaker 2: how does it actually work, how long does it take?

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Speaker 2: How many steps do you have to go through?

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Speaker 3: Yeah, so NIH runs three funding cycles, each of them

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Speaker 3: take more than half a year to complete, almost three

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Speaker 3: quarters of a year. So basically, there will be one

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Speaker 3: date where grants are submitted, another date where grants are

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Speaker 3: reviewed by a panel of experts from around the country,

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Speaker 3: usually a panel of twenty five to thirty scientists with

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Speaker 3: subject matter expertise in the area of the grants, and

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Speaker 3: those are called study sections. And then finally they'll be

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Speaker 3: reviewed by an advisory council that will basically okay grants

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Speaker 3: for funding. So here's an example. I have a really

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Speaker 3: talented post doctoral fellow working in my research group who

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Speaker 3: applied for a career transition award back in the summer.

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Speaker 3: His grant was reviewed in October by a study section.

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Speaker 3: It got a phenomenal score that should have been funded.

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Speaker 3: His council was supposed to meet at the beginning of

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Speaker 3: this month in February, but because no study sections or

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Speaker 3: councils have been allowed to meet, his grant can't be

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Speaker 3: approved for funding. So there are three of those cycles

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Speaker 3: a year, and right now, I mean, I'm not sure

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Speaker 3: that if your listeners are aware of this, but besides

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Speaker 3: the kinds of things that we're talking about with indirect

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Speaker 3: costs and direct costs, HHS has blocked NIH from posting

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Speaker 3: in the Federal Register. And why that matters is in

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Speaker 3: order to hold one of these study sections, these grant

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Speaker 3: review sections, or hold one of these advisory councils, they

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Speaker 3: have to be posted in the Federal Register at least

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Speaker 3: fifteen days ahead by law. So when a judge put

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Speaker 3: a restraining order on the freeze to grants, HHS got

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Speaker 3: around this bureaucratically by just simply not letting NIH submit

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Speaker 3: those notices. So the whole system has been ground to

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Speaker 3: a halt. Grants that were reviewed like my postdocs last

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Speaker 3: fall can't get approved at council grants that should have

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Speaker 3: been reviewed this month can't be reviewed, and who knows

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Speaker 3: whether their councils will meet coming in May when they should.

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Speaker 3: So right now, labs are really in sort of an

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Speaker 3: existential crisis. I mean so again, going back to this

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Speaker 3: small business model, labs are businesses that run on very

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Speaker 3: tight margins. And so if I'm going to be running

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Speaker 3: my laboratory on one of these NIH grants, which let's

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Speaker 3: say is four years long, in year three, I would

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Speaker 3: be applying for a renewal of that grant to continue

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Speaker 3: that research, and remember research that the NIH has already

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Speaker 3: invested in. If that grant can't get reviewed or funded,

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Speaker 3: then I'm turning around to the people in my lab

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Speaker 3: and saying, I don't want to have to do this,

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Speaker 3: but I'm going to have to let you go. So

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Speaker 3: not only is that a real cost in terms of

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Speaker 3: jobs for people who are again experts in what they're doing,

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Speaker 3: but you're damaging research that you've already invested in. The

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Speaker 3: whole thing is kind of senseless. That's certainly not government efficiency.

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Speaker 1: Can you talk more about what's being frozen right now?

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Speaker 1: So you've just described the stopping of the review process.

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Speaker 1: So Theoretically, if this freeze is in place by the

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Speaker 1: time you need your next transfer funding and it can't happen,

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Speaker 1: then you could have what's happening operationally in labs that

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Speaker 1: you know of right now as a result of the

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Speaker 1: overall freeze. Are there specific trials or tests or I

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Speaker 1: guess experiments is what a lay person might call them,

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Speaker 1: that had been going on in January that aren't happening

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Speaker 1: right now.

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Speaker 3: So I think there are some clinical trials out of

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Speaker 3: NIHS that have been affected directly. For most of us,

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Speaker 3: it's damaging things, but we could still pull back from

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Speaker 3: it being absolutely disastrous if things get turned around very

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Speaker 3: very soon. But otherwise, the layoffs are going to include

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Speaker 3: people who take care of research animals. It's going to

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Speaker 3: include the people that make sure that hazardous waste is

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Speaker 3: supposed of safely. And right now it really depends on

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Speaker 3: each university. A lot of universities have begun to either

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Speaker 3: rescind offers for graduate students for the coming fall, or

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Speaker 3: basically decide not to have the next class of graduate

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Speaker 3: students who are cut it down by twenty to fifty percent.

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Speaker 3: And I think this is something it's important to bring up.

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Speaker 3: So NIH research really does three things, right. It funds

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Speaker 3: the science that we've been talking about, so making drugs

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Speaker 3: like ozienpic or pediatric cancer research. It funds money in

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Speaker 3: the economy, as I mentioned, so hundreds of thousands of jobs,

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Speaker 3: ninety three billion dollars a year in economic activity. But

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Speaker 3: the other thing it does is to train the next

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Speaker 3: generation of research scientists. And I can tell you that

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Speaker 3: the train needs right now. Graduate students and postdoctoral fellows

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Speaker 3: are completely scared and demoralized and wondering whether there is

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Speaker 3: a future for them in science. And if we lose

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Speaker 3: the next generation of scientific researchers, it's not just going

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Speaker 3: to affect academic research. We train the workforce for the

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Speaker 3: pharmaceutical industry and the biotech industry. This is going to

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Speaker 3: decimate US scientific biomedical research broadly written.

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Speaker 2: So just on that note, one thing you hear from

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Speaker 2: the Trump administration continuously is this idea that we need

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Speaker 2: to compete with China. And it's certainly true when it

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Speaker 2: comes to electronics technology like semiconductors, And they say also

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Speaker 2: in the realm of scientific funding, can you maybe talk

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Speaker 2: a little bit about how the Chinese research model stacks

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Speaker 2: up against the US research model, and I guess how

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Speaker 2: much competition there is currently between the two countries for

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Speaker 2: that younger generation of talent.

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Speaker 3: Yeah. No, absolutely, As I'm sure you're probably aware, federal

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Speaker 3: investment in science like at NIH and the National Science

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Speaker 3: Foundation after World War Two was what drove the US's

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Speaker 3: enormous growth in the fifties and sixties and really boosted

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Speaker 3: America to the forefront of the world in technology development

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Speaker 3: in science, and we barely keep pace. I mean, to

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Speaker 3: say that we spend too much money on science is

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Speaker 3: so far from being true that it's crazy. On the

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Speaker 3: other hand, China recognizes what these investments do, and they

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Speaker 3: have been upping and upping the kinds of investments that

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Speaker 3: they make in biotechnology and another technology because that is

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Speaker 3: what's going to drive the economic growth of the future.

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Speaker 3: The investments that US has made in science today have

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Speaker 3: driven the formation of entire industries, not just innovations in medicine,

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Speaker 3: but also in engineering and technology. We are going to

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Speaker 3: lose that leadership.

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Speaker 1: You've given these examples of talented postdocs being unsure that

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Speaker 1: they're going to get their funding, or the prospect that

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Speaker 1: the next generation of talent who will go into private

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Speaker 1: sector labs is going to be devastated because their education

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Speaker 1: trajectory is off. One of the arguments for NIH reform

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Speaker 1: is that a lot of NIH grants actually go to

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Speaker 1: established professionals, that there is this hierarchy that a lot

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Speaker 1: it's very hard or very rare to get grants for

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Speaker 1: people under forty. Setting aside the Trump elon reforms, do

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Speaker 1: you believe that there are flaws within the existing NIH

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Speaker 1: regime causing money to not go into the most promising areas.

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Speaker 3: So I would take issue with what's going on being

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Speaker 3: termed that. But Okay, the problem is that there's not

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Speaker 3: enough money in the system. And so those grant cycles

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Speaker 3: that I just mentioned, so one of the study sections

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Speaker 3: is going to be evaluating many, many dozens of grants

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Speaker 3: in each cycle, and then there are many study sections

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Speaker 3: that are evaluating different areas of science. At current funding levels,

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Speaker 3: less than ten percent of those grants are going to

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Speaker 3: be able to be funded. Where is the line that

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Speaker 3: I could draw where I could clearly say that these

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Speaker 3: ones are absolutely should be funded and these ones maybe

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Speaker 3: need more work or more thought. That line is more

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Speaker 3: around the twenty five to thirty percent. So we are

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Speaker 3: really underfunding research and you're right. When you are under

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Speaker 3: funding research, there is going to be, if you will,

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Speaker 3: a competitive advantage to establish researchers versus your researchers. But

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Speaker 3: the NIH recognizes that, and so they have put in

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Speaker 3: place a number of different mechanisms to try to alleviate that.

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Speaker 3: So there are score boosts for early career investigators that

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Speaker 3: help more of them get funded. They are special granting

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Speaker 3: programs that are aimed at those early career investigators. So,

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Speaker 3: I mean, one of the things about NIH is that

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Speaker 3: it's really conscientious about trying to course correct. They are

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Speaker 3: constantly looking at what they're doing and what the outcomes are,

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Speaker 3: and then consulting with the broader scientific community about where

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Speaker 3: they could find efficiencies or how they could solve particular problems.

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Speaker 3: It's not like all of this is going on in

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Speaker 3: a vacuum and nobody's paying attention.

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Speaker 1: That makes a lot of sense to me. I'm just

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Speaker 1: looking at stats. The median age for researcher designated an

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Speaker 1: NIH award for the first time had increased since nineteen

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Speaker 1: ninety five and is now, according to something that was

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Speaker 1: reported in twenty twenty one, over forty years old. Like government,

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Speaker 1: corporate prosties whatever. There's always, as you say, there's course correcting,

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Speaker 1: there's awareness of the issues, but awareness is not the

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Speaker 1: same thing as addressing them. And I'm just trying to

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Speaker 1: press on whether there are reasons to be skeptical about

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Speaker 1: the efficacy of the existing system or the ability of

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Speaker 1: the existing system to course correct or is this not

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Speaker 1: a number that we should care about at all? Does

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Speaker 1: that not mean anything that the average age of a

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Speaker 1: first time awardy has gone up. Maybe that's not necessarily

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Speaker 1: a bad thing.

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Speaker 3: Yeah, So, you know, one thing you can say about

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Speaker 3: almost anything this large is it's the best flawed system

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Speaker 3: that we have. The US scientific research enterprise is the

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Speaker 3: envy of the entire world, hands down. The reason for

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Speaker 3: the increase in age has nothing really to do with

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Speaker 3: the NIH and so that's more on science and scientists.

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Speaker 3: To some extent. The time to degree for graduate students

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Speaker 3: has been creeping up, how long it takes them to

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Speaker 3: earn their PhD, and also the time that faculty spend

440
00:25:13,400 --> 00:25:16,120
Speaker 3: as postdoctoral fellows. And you know, when I was first

441
00:25:16,119 --> 00:25:18,840
Speaker 3: starting out as a graduate student in the nineties, there

442
00:25:18,880 --> 00:25:21,600
Speaker 3: were people who were hired into faculty positions that never

443
00:25:21,640 --> 00:25:26,240
Speaker 3: did postdoctoral fellows. Then that became almost non existent, and

444
00:25:26,280 --> 00:25:29,720
Speaker 3: now there are some programs like the Whitehead Fellows where

445
00:25:29,760 --> 00:25:34,560
Speaker 3: you have mentored postdoc positions where they are independent faculty

446
00:25:34,720 --> 00:25:37,040
Speaker 3: member like people, but with a little bit more safety

447
00:25:37,080 --> 00:25:40,199
Speaker 3: net than a typical assistant professor would have. And so

448
00:25:40,680 --> 00:25:42,879
Speaker 3: you know, I'm going to swing back to those people

449
00:25:42,920 --> 00:25:47,080
Speaker 3: that get fired from their laboratories at THEIH two weeks ago.

450
00:25:47,760 --> 00:25:51,520
Speaker 3: So each one of those people will have spent more

451
00:25:51,600 --> 00:25:54,520
Speaker 3: than ten years training for their jobs, generally more than

452
00:25:54,560 --> 00:25:59,520
Speaker 3: twelve years training. But between graduate school and their postdoctoral fellowship,

453
00:26:00,080 --> 00:26:04,320
Speaker 3: each one of those people was almost certainly supported by

454
00:26:04,600 --> 00:26:09,159
Speaker 3: NIH funding through those dozen years, whether it be independent

455
00:26:09,200 --> 00:26:14,960
Speaker 3: fellowships to them personally or funding from their pi's research grants.

456
00:26:15,520 --> 00:26:18,760
Speaker 3: So these are people who the US has already made

457
00:26:18,960 --> 00:26:24,200
Speaker 3: an enormous investment in and they were successful at getting

458
00:26:24,560 --> 00:26:27,960
Speaker 3: research laboratory positions at one of the top places in

459
00:26:28,000 --> 00:26:30,600
Speaker 3: the world, the NIH. So these are the best of

460
00:26:30,640 --> 00:26:33,560
Speaker 3: the best, and we just fired them by email on

461
00:26:33,600 --> 00:26:36,800
Speaker 3: a Saturday night. That's a waste of all that money

462
00:26:36,800 --> 00:26:39,679
Speaker 3: that was spent training those people and the research they

463
00:26:39,680 --> 00:26:40,080
Speaker 3: were doing.

464
00:26:55,359 --> 00:26:58,560
Speaker 2: You mentioned slim profit margins earlier, and I think This

465
00:26:58,760 --> 00:27:00,639
Speaker 2: is one of the things that a lot of people

466
00:27:00,760 --> 00:27:04,200
Speaker 2: struggle with when they talk about public funding for research

467
00:27:04,359 --> 00:27:08,639
Speaker 2: at elite universities, in particular, because you see numbers like,

468
00:27:08,720 --> 00:27:12,240
Speaker 2: you know, the Harvard Endowment has more than fifty billion dollars,

469
00:27:12,280 --> 00:27:15,600
Speaker 2: and there are other universities out there with even more.

470
00:27:16,119 --> 00:27:19,800
Speaker 2: And I guess the question is, why can't universities that

471
00:27:19,880 --> 00:27:24,639
Speaker 2: are charging incredibly high tuition, that run these massive investment

472
00:27:24,640 --> 00:27:28,240
Speaker 2: accounts and that also get donations, why can't they fund

473
00:27:28,280 --> 00:27:29,240
Speaker 2: everything themselves?

474
00:27:29,480 --> 00:27:31,760
Speaker 3: Okay, so first I just want to make a correction

475
00:27:31,880 --> 00:27:35,000
Speaker 3: that it's not profit margins. There's no profit here. It's

476
00:27:35,040 --> 00:27:38,720
Speaker 3: operating margins, right, It's the ability to keep your laboratory afloat.

477
00:27:39,440 --> 00:27:42,960
Speaker 3: So that said, endowments are not this sort of bucket

478
00:27:43,000 --> 00:27:48,080
Speaker 3: of free funds. Endowments are a collective of money that

479
00:27:48,400 --> 00:27:53,520
Speaker 3: has been donated by specific donors and earmarked for particular things,

480
00:27:53,920 --> 00:27:57,000
Speaker 3: and so there are legal requirements that you spend that

481
00:27:57,200 --> 00:27:59,679
Speaker 3: money on what the donor asks for it to be

482
00:27:59,720 --> 00:28:03,520
Speaker 3: spent on. There are a few places Johns Hopkins is

483
00:28:03,560 --> 00:28:07,520
Speaker 3: a great example. Bloomberg himself has made investments there that

484
00:28:07,560 --> 00:28:10,879
Speaker 3: can be spent directly on science. That's making a huge

485
00:28:10,880 --> 00:28:15,040
Speaker 3: difference there. But in general, at most universities, people who

486
00:28:15,119 --> 00:28:20,040
Speaker 3: are funding endowments are funding the sports stadium. Unfortunately, they're

487
00:28:20,160 --> 00:28:23,960
Speaker 3: funding financial aid, which is great. My own university has

488
00:28:23,960 --> 00:28:28,640
Speaker 3: made enormous strives in having an increase in Pelgramt eligible students,

489
00:28:28,760 --> 00:28:32,040
Speaker 3: which is super important. But for the most part, they're

490
00:28:32,080 --> 00:28:35,399
Speaker 3: not general funds that can be used to support anything

491
00:28:35,400 --> 00:28:35,879
Speaker 3: you want to.

492
00:28:36,280 --> 00:28:41,800
Speaker 1: Let's get back to operational realities. So some of your research. Recently,

493
00:28:42,240 --> 00:28:45,600
Speaker 1: I'm reading an article at Northwestern dot edu about lamp

494
00:28:45,640 --> 00:28:49,440
Speaker 1: pre eels, and obviously, as you mentioned, with the example

495
00:28:49,480 --> 00:28:52,800
Speaker 1: of ozempic and the Gila monster, you know what seems

496
00:28:52,920 --> 00:28:57,080
Speaker 1: like sort of pure lab experimentation in biology has the

497
00:28:57,080 --> 00:29:01,479
Speaker 1: potential to turn into an incredible profit. But even sitting

498
00:29:01,520 --> 00:29:04,560
Speaker 1: aside that question, Okay, let's say you have some new idea,

499
00:29:04,840 --> 00:29:08,320
Speaker 1: there's something new you want to explore about the biology

500
00:29:08,680 --> 00:29:12,360
Speaker 1: of eel and eel sells, et cetera. How do you

501
00:29:12,360 --> 00:29:14,960
Speaker 1: come up with the price you're applying for a grant

502
00:29:15,040 --> 00:29:17,840
Speaker 1: you want to build on some research that you do.

503
00:29:18,240 --> 00:29:21,920
Speaker 1: Talk to us just about that process of you think

504
00:29:21,960 --> 00:29:24,480
Speaker 1: you need or you feel you need X money, etc.

505
00:29:24,800 --> 00:29:27,440
Speaker 1: What do you do how do you blank sheet of paper,

506
00:29:27,760 --> 00:29:29,920
Speaker 1: you want to do a new experiment and you want

507
00:29:30,000 --> 00:29:32,600
Speaker 1: an IH money. What does that process look like of

508
00:29:32,600 --> 00:29:33,560
Speaker 1: coming up with that number?

509
00:29:34,000 --> 00:29:38,200
Speaker 3: Okay, so there's coming up with the ideas and coming

510
00:29:38,280 --> 00:29:41,160
Speaker 3: up with the grant numbers and budget it yourself. The

511
00:29:41,280 --> 00:29:45,120
Speaker 3: ideas usually stem from research that you've been doing and

512
00:29:45,560 --> 00:29:48,960
Speaker 3: are informed by research that other people are doing. It's

513
00:29:49,040 --> 00:29:52,400
Speaker 3: usually you will get into conversations with other researchers to

514
00:29:52,480 --> 00:29:55,120
Speaker 3: refine those ideas to really make them to the point

515
00:29:55,120 --> 00:29:58,760
Speaker 3: where they're shovel ready for actually doing experiments so you

516
00:29:58,760 --> 00:30:01,800
Speaker 3: can apply for grant funding for them. So the NIH

517
00:30:01,960 --> 00:30:06,320
Speaker 3: has two kinds of granting mechanisms going back since the

518
00:30:06,360 --> 00:30:09,920
Speaker 3: early two thousands. One is which has been the main

519
00:30:09,960 --> 00:30:13,280
Speaker 3: way that people get funding, is called modular grants. And

520
00:30:13,360 --> 00:30:16,120
Speaker 3: those grants are really a set amount of money, so

521
00:30:16,200 --> 00:30:20,560
Speaker 3: there's there's no sort of negotiating for what their actual

522
00:30:20,640 --> 00:30:23,400
Speaker 3: costs are. It's just that this is the amount of money,

523
00:30:23,680 --> 00:30:26,320
Speaker 3: and that amount of money has not changed in the

524
00:30:26,400 --> 00:30:31,120
Speaker 3: last twenty five years. And so while costs for personnel

525
00:30:31,280 --> 00:30:34,720
Speaker 3: and cost for reagents and everything else has gone up

526
00:30:35,160 --> 00:30:38,560
Speaker 3: a lot, that amount of grant money has not gone up.

527
00:30:38,640 --> 00:30:40,400
Speaker 1: What is are we talking one million?

528
00:30:40,400 --> 00:30:40,480
Speaker 4: Like?

529
00:30:40,480 --> 00:30:42,040
Speaker 1: What is a what is a money?

530
00:30:42,040 --> 00:30:43,920
Speaker 3: We're talking about two hundred and fifty thousand dollars a

531
00:30:44,000 --> 00:30:47,400
Speaker 3: yach ok Okay, So this is not a lot of money, right,

532
00:30:47,600 --> 00:30:50,680
Speaker 3: So if you want more money than that, and more

533
00:30:50,760 --> 00:30:53,640
Speaker 3: and more people are having to do this because two

534
00:30:53,680 --> 00:30:56,560
Speaker 3: hundred and fifty thousand dollars is not enough to support

535
00:30:56,560 --> 00:31:00,160
Speaker 3: a research laboratory anymore, then you have to submit a

536
00:31:00,280 --> 00:31:04,400
Speaker 3: non modular budget where you basically have to estimate in

537
00:31:04,520 --> 00:31:08,880
Speaker 3: excruciating detail how many personnel it needs and how much

538
00:31:08,960 --> 00:31:12,400
Speaker 3: that will cost. What are the actual animal costs, what

539
00:31:12,680 --> 00:31:16,720
Speaker 3: are the let's say sequencing costs for next generation sequencing,

540
00:31:16,760 --> 00:31:21,000
Speaker 3: genum sequencing, What are the reagent costs, etc. Do you

541
00:31:21,040 --> 00:31:24,320
Speaker 3: need any specialized pieces of equipment? Attach a quote for

542
00:31:24,400 --> 00:31:26,120
Speaker 3: me for that to show me exactly how much that

543
00:31:26,120 --> 00:31:28,760
Speaker 3: piece of equipment will cost. So this is non trivial.

544
00:31:29,240 --> 00:31:32,160
Speaker 3: You might argue, if you're looking for inefficiencies in the system,

545
00:31:32,400 --> 00:31:34,840
Speaker 3: that making people go into that much detail and then

546
00:31:34,880 --> 00:31:37,960
Speaker 3: needing people at the NIH to go into a forensic

547
00:31:38,080 --> 00:31:40,960
Speaker 3: dive onto each of those things is maybe not the

548
00:31:41,000 --> 00:31:43,280
Speaker 3: most efficient way of doing it, but it's the only

549
00:31:43,320 --> 00:31:45,560
Speaker 3: way of doing it right now, So it's not that

550
00:31:45,640 --> 00:31:47,920
Speaker 3: you're sort of pulling some number out of thin air

551
00:31:48,200 --> 00:31:50,240
Speaker 3: and saying, hey, dude, this is how much money I

552
00:31:50,480 --> 00:31:53,120
Speaker 3: need to do my research. It's going to get scrutinized.

553
00:31:53,200 --> 00:31:56,760
Speaker 3: At the university level. We actually have to submit even

554
00:31:56,800 --> 00:31:59,960
Speaker 3: more detailed budgets than will actually go into the NIA,

555
00:32:00,560 --> 00:32:02,600
Speaker 3: because they want to make sure that we are in

556
00:32:02,720 --> 00:32:04,800
Speaker 3: compliance so that we don't ever get in trouble with

557
00:32:04,840 --> 00:32:07,720
Speaker 3: the NIH. So I get asked for things internally that

558
00:32:07,920 --> 00:32:10,239
Speaker 3: are even beyond what NIH is going to ask for,

559
00:32:10,640 --> 00:32:13,000
Speaker 3: and then NIH will get those budgets and that will

560
00:32:13,000 --> 00:32:16,440
Speaker 3: get scrutinized before anything gets paid out. And it gets

561
00:32:16,440 --> 00:32:19,440
Speaker 3: scrutinized in two ways. It gets scrutinized during the grant

562
00:32:19,480 --> 00:32:22,320
Speaker 3: review process, where the reviewers are asked away in on

563
00:32:22,360 --> 00:32:24,560
Speaker 3: the budget and whether it's appropriate for the research that

564
00:32:24,760 --> 00:32:27,840
Speaker 3: is proposed. It will be scrutinized by the council, who

565
00:32:27,840 --> 00:32:31,040
Speaker 3: will do the same thing and who are increasingly, particularly

566
00:32:31,040 --> 00:32:34,600
Speaker 3: at the National Institutes of General Medical Sciences, are capping

567
00:32:34,640 --> 00:32:38,080
Speaker 3: the total amount of money that anyone investigator can have.

568
00:32:38,600 --> 00:32:41,400
Speaker 3: And it will get again scrutinized at the program officer

569
00:32:41,520 --> 00:32:43,640
Speaker 3: level before those grants are paid out.

570
00:32:44,440 --> 00:32:46,719
Speaker 2: I find this so interesting. How do you actually come

571
00:32:46,840 --> 00:32:49,640
Speaker 2: up with the estimate for some of the costs and

572
00:32:49,680 --> 00:32:53,080
Speaker 2: how does THEIAH actually go about evaluating whether or not

573
00:32:53,160 --> 00:32:57,080
Speaker 2: it's reasonable, because I think about with scientific research often

574
00:32:57,400 --> 00:33:00,320
Speaker 2: you're doing something very novel. So going back to the

575
00:33:00,360 --> 00:33:03,840
Speaker 2: Gila monster example, I mean, how do you know the

576
00:33:03,880 --> 00:33:08,560
Speaker 2: cost the reasonable cost of housing like two dozen deala

577
00:33:08,680 --> 00:33:11,480
Speaker 2: monsters for a few years while you experiment on them.

578
00:33:12,080 --> 00:33:14,280
Speaker 3: You have to do due diligence and figure it out,

579
00:33:14,480 --> 00:33:16,760
Speaker 3: and it does. It's time consuming. So a lot of

580
00:33:16,800 --> 00:33:20,360
Speaker 3: what faculty like me spend their time doing is deep

581
00:33:20,440 --> 00:33:24,240
Speaker 3: diving into these costs and keep diving into the accompanying

582
00:33:24,440 --> 00:33:28,160
Speaker 3: paperwork and administrative burden for lack of a better word,

583
00:33:28,360 --> 00:33:31,400
Speaker 3: that comes with a lot of these mechanisms, but it

584
00:33:31,520 --> 00:33:33,920
Speaker 3: is the only way to get the funding to do

585
00:33:33,960 --> 00:33:36,480
Speaker 3: the research. So if some things are easy. We know

586
00:33:36,560 --> 00:33:40,320
Speaker 3: in any given market what a competitive salary is for

587
00:33:40,600 --> 00:33:43,520
Speaker 3: a scientist at this level or that level, et cetera.

588
00:33:44,120 --> 00:33:47,440
Speaker 3: We know, at least at the beginning of the grant

589
00:33:47,640 --> 00:33:50,320
Speaker 3: what the costs for the reagents that we know that

590
00:33:50,320 --> 00:33:52,840
Speaker 3: we're going to need are. But You're right. As you

591
00:33:52,960 --> 00:33:56,200
Speaker 3: proceed in the grant, you may require a kind of

592
00:33:56,240 --> 00:33:59,880
Speaker 3: technology or kinds of reagents that you didn't initially budget for,

593
00:34:00,360 --> 00:34:03,920
Speaker 3: and that's when you're scrambling to find additional funds to

594
00:34:04,320 --> 00:34:05,840
Speaker 3: be able to do that, and that's where some of

595
00:34:05,920 --> 00:34:07,640
Speaker 3: these foundation graps sometimes come in.

596
00:34:08,120 --> 00:34:12,600
Speaker 1: I just have one last question. You know, obviously science

597
00:34:12,719 --> 00:34:15,800
Speaker 1: is a very broad category, but the thing that excites

598
00:34:15,800 --> 00:34:19,200
Speaker 1: people from a sort of commercial or return on public

599
00:34:19,280 --> 00:34:24,640
Speaker 1: spending investment perspective is the connection between science funding. Often

600
00:34:24,840 --> 00:34:28,160
Speaker 1: and it turned into a drug or a new therapy

601
00:34:28,560 --> 00:34:31,200
Speaker 1: of some sort. You mentioned to zempic and anti cancer

602
00:34:31,239 --> 00:34:34,680
Speaker 1: drugs and so forth. We've never like really done an

603
00:34:34,680 --> 00:34:38,640
Speaker 1: episode on the economics of labs in general, etc. And

604
00:34:38,719 --> 00:34:40,560
Speaker 1: I want to do way more on this because I

605
00:34:40,560 --> 00:34:43,120
Speaker 1: think it's really important. Can you sort of paint us

606
00:34:43,160 --> 00:34:47,440
Speaker 1: a general outline of in America right now, at the

607
00:34:47,440 --> 00:34:51,839
Speaker 1: source of original discovery, what is the distribution between sort

608
00:34:51,880 --> 00:34:55,280
Speaker 1: of what happens between the public sector versus a research

609
00:34:55,320 --> 00:34:57,840
Speaker 1: institution and then private sector labs.

610
00:34:58,600 --> 00:35:01,439
Speaker 3: Yeah, I mean since the nineteen fifties, this has been

611
00:35:01,680 --> 00:35:05,960
Speaker 3: an invaluable three way partnership. Right The government wants to

612
00:35:05,960 --> 00:35:09,000
Speaker 3: do research, but it knows that it doesn't have the capacity,

613
00:35:09,080 --> 00:35:11,920
Speaker 3: let's say, to do it all on the NIHS campus,

614
00:35:12,160 --> 00:35:16,359
Speaker 3: and so it partners with scientific laboratories at universities by

615
00:35:16,360 --> 00:35:19,439
Speaker 3: providing grant money so that basic research can be done.

616
00:35:20,320 --> 00:35:24,919
Speaker 3: The pharmaceutical industry or the biotechnology industry, they don't make

617
00:35:25,000 --> 00:35:27,960
Speaker 3: investments in that kind of foundational research because there's no

618
00:35:28,200 --> 00:35:30,920
Speaker 3: exact timeline for when you might get a payoff from that.

619
00:35:31,600 --> 00:35:34,719
Speaker 3: So what they do is look to the universities to

620
00:35:34,800 --> 00:35:38,000
Speaker 3: provide them with that foundational knowledge that then they can

621
00:35:38,320 --> 00:35:42,080
Speaker 3: make a startup company or take your promising results on

622
00:35:42,160 --> 00:35:44,720
Speaker 3: what could be a new drug and fund the clinical

623
00:35:44,760 --> 00:35:47,600
Speaker 3: trials that are going to see whether it's effective. And

624
00:35:47,719 --> 00:35:51,000
Speaker 3: this partnership has been incredibly successful and it is the

625
00:35:51,040 --> 00:35:55,320
Speaker 3: reason why the US is a leader in this area worldwide.

626
00:35:56,000 --> 00:35:57,960
Speaker 2: Just on this point, is there an argument to be

627
00:35:58,040 --> 00:36:02,840
Speaker 2: made that maybe universities could monetize some of their research better,

628
00:36:03,040 --> 00:36:06,880
Speaker 2: maybe have higher equity stakes or any equity stakes in

629
00:36:06,960 --> 00:36:10,800
Speaker 2: promising new drugs in exchange for the work they basically

630
00:36:10,840 --> 00:36:12,240
Speaker 2: do for big pharma.

631
00:36:12,960 --> 00:36:15,000
Speaker 3: Oh, you know, I think you could take that argument

632
00:36:15,040 --> 00:36:19,400
Speaker 3: even more broadly. So why is it that a drug

633
00:36:19,400 --> 00:36:21,600
Speaker 3: that was developed at Northwestern and there's a real example

634
00:36:21,640 --> 00:36:23,360
Speaker 3: for this, but i'll leave the drug in the person

635
00:36:23,400 --> 00:36:27,960
Speaker 3: out of it that was then turned into trillions of

636
00:36:28,040 --> 00:36:31,520
Speaker 3: dollars by a pharmaceutical company. Now the university did get

637
00:36:31,520 --> 00:36:33,359
Speaker 3: a cut of that because they had the patent on it.

638
00:36:33,560 --> 00:36:36,080
Speaker 3: But you know who isn't getting the benefits of it? You,

639
00:36:36,880 --> 00:36:39,960
Speaker 3: So you were getting charged huge amounts more money for

640
00:36:40,040 --> 00:36:43,600
Speaker 3: that same drug that was developed using US research funds

641
00:36:43,719 --> 00:36:46,399
Speaker 3: than someone in Europe. Is that, I think is where

642
00:36:46,400 --> 00:36:47,839
Speaker 3: the inefficiencies in the system were.

643
00:36:48,280 --> 00:36:49,759
Speaker 1: There's so much more I want to do on this.

644
00:36:49,840 --> 00:36:52,760
Speaker 1: Sometimes you read like a story about like some MIT

645
00:36:53,440 --> 00:36:56,680
Speaker 1: professor and somehow some biotecher was born out of his

646
00:36:56,760 --> 00:36:59,239
Speaker 1: labs and retires and is a billionaire, And I want

647
00:36:59,280 --> 00:37:02,239
Speaker 1: to understand more the economics of then how that was allocated.

648
00:37:02,560 --> 00:37:06,040
Speaker 1: But this was a fantastic introduction to the topic of

649
00:37:06,080 --> 00:37:08,919
Speaker 1: what's going on. So Carol Lebond, thank you so much

650
00:37:09,000 --> 00:37:11,560
Speaker 1: for coming on off lots happy to have talked with you.

651
00:37:11,600 --> 00:37:12,279
Speaker 3: Thank you very much.

652
00:37:12,680 --> 00:37:15,080
Speaker 2: Thanks Carol, that was great and I'd be happy.

653
00:37:14,840 --> 00:37:15,880
Speaker 3: To explain Lamprey to you.

654
00:37:15,960 --> 00:37:18,920
Speaker 2: At some point they frightened me. I think yea much

655
00:37:19,120 --> 00:37:21,359
Speaker 2: like I find them very interesting, but I also find

656
00:37:21,400 --> 00:37:22,920
Speaker 2: them extremely off putting.

657
00:37:22,960 --> 00:37:25,440
Speaker 3: At the same time, I'm convinced that they are the

658
00:37:25,600 --> 00:37:27,080
Speaker 3: inspiration for the Demi gorgan.

659
00:37:27,520 --> 00:37:29,160
Speaker 2: Oh yeah, you're right.

660
00:37:29,280 --> 00:37:32,719
Speaker 3: They look like that absolutely, But they are like a

661
00:37:33,000 --> 00:37:37,640
Speaker 3: living fossil. They are the closest thing we have to

662
00:37:38,320 --> 00:37:41,480
Speaker 3: a living example of what the most primitive vertebrate was.

663
00:37:41,560 --> 00:37:43,680
Speaker 3: And so if you want to understand where you, as

664
00:37:43,719 --> 00:37:46,960
Speaker 3: a human vertebrate comes from, we can look to evolutionary

665
00:37:47,000 --> 00:37:48,200
Speaker 3: studies using the lambrey.

666
00:37:48,480 --> 00:37:50,480
Speaker 1: Can we keep this last little bit? And that was

667
00:37:50,520 --> 00:37:51,240
Speaker 1: actually really.

668
00:37:51,080 --> 00:38:05,680
Speaker 5: Good, Thanks Carol, Thanks bye bye, Tracy.

669
00:38:05,719 --> 00:38:09,440
Speaker 1: I thought that was a really good introduction to the topic.

670
00:38:09,560 --> 00:38:14,560
Speaker 1: I will say this, like, I'm sure that someone could

671
00:38:14,600 --> 00:38:17,839
Speaker 1: walk in here and convince me, or at least make

672
00:38:17,880 --> 00:38:21,360
Speaker 1: a compelling argument that we need radical overhaul to the

673
00:38:21,640 --> 00:38:24,800
Speaker 1: NIH way we do drug discovery.

674
00:38:24,360 --> 00:38:26,480
Speaker 2: Or the way physcal shock therapy.

675
00:38:26,800 --> 00:38:29,440
Speaker 1: Yeah, or just that we really need to totally rethink

676
00:38:29,480 --> 00:38:32,719
Speaker 1: the way we do science investment in this country. I

677
00:38:33,040 --> 00:38:37,319
Speaker 1: also think that if the main lever you pull is

678
00:38:37,400 --> 00:38:40,440
Speaker 1: just less money, and it means that there are going

679
00:38:40,480 --> 00:38:42,640
Speaker 1: to be people who have worked for ten years and

680
00:38:42,680 --> 00:38:46,319
Speaker 1: then their career is derailed or various labs get shut

681
00:38:46,360 --> 00:38:49,279
Speaker 1: down or in a state of limbo that I'm very

682
00:38:49,320 --> 00:38:53,840
Speaker 1: skeptical that that alone would turn into better results.

683
00:38:54,040 --> 00:38:54,520
Speaker 5: Yeah.

684
00:38:54,560 --> 00:38:55,640
Speaker 1: Well, a couple things here.

685
00:38:55,719 --> 00:38:59,200
Speaker 2: So Number one, I liked your plea for billionaire researchers

686
00:38:59,239 --> 00:39:02,120
Speaker 2: to get in touch with thoughts, So I'll just repeat that.

687
00:39:02,360 --> 00:39:04,960
Speaker 2: If you are a researcher who has made tons of money,

688
00:39:05,120 --> 00:39:08,319
Speaker 2: yeah some invention, then please get in touch with us.

689
00:39:08,560 --> 00:39:10,960
Speaker 2: But secondly, I thought the point Carol made at the

690
00:39:11,080 --> 00:39:14,040
Speaker 2: very end of the podcast about how you know, a

691
00:39:14,040 --> 00:39:17,520
Speaker 2: lot of these discoveries, a lot of scientific advancement does

692
00:39:17,680 --> 00:39:23,319
Speaker 2: eventually get monetized, usually by private companies, even though it's

693
00:39:23,400 --> 00:39:27,839
Speaker 2: funded through public grants, And I think that's I mean

694
00:39:27,920 --> 00:39:31,160
Speaker 2: to her point like that is a huge area of inefficiency.

695
00:39:31,360 --> 00:39:35,680
Speaker 1: It's very easy to look around and find things you

696
00:39:35,760 --> 00:39:39,600
Speaker 1: don't like about any system, and there's too much paperwork,

697
00:39:39,960 --> 00:39:42,560
Speaker 1: or there are things that slow it down, and or

698
00:39:42,640 --> 00:39:45,840
Speaker 1: there is money that is going towards such a nebulous

699
00:39:45,880 --> 00:39:49,239
Speaker 1: area of science and you find some random example that

700
00:39:49,320 --> 00:39:53,000
Speaker 1: has no prospect of commercial application. I'm sure all of

701
00:39:53,040 --> 00:39:58,080
Speaker 1: that exists to an extent. It also seems objectively true

702
00:39:58,560 --> 00:40:01,320
Speaker 1: that at least at this moment in twenty twenty five,

703
00:40:01,960 --> 00:40:07,920
Speaker 1: the US has the world's most advanced pharmaceutical and biotech industry.

704
00:40:08,120 --> 00:40:10,560
Speaker 1: Could it be way better in some sort of like

705
00:40:10,680 --> 00:40:14,520
Speaker 1: alternate scenario where everything moved faster and more efficiently. It

706
00:40:14,600 --> 00:40:17,400
Speaker 1: seems plausible. But I think to Carol's point that I

707
00:40:17,440 --> 00:40:20,799
Speaker 1: really liked is that for the last several decades, the

708
00:40:20,920 --> 00:40:23,719
Speaker 1: US has really had the leading edge industry of the

709
00:40:23,719 --> 00:40:26,759
Speaker 1: world of studying the hard sciences, and those sciences have

710
00:40:26,880 --> 00:40:31,239
Speaker 1: turned into all these sort of commercialized technological breakthroughs. I

711
00:40:31,280 --> 00:40:35,360
Speaker 1: think people should appreciate what we have currently in this

712
00:40:35,480 --> 00:40:41,319
Speaker 1: country and understand the interplay between public money, research foundations

713
00:40:41,440 --> 00:40:45,719
Speaker 1: and commercial ventures that it put us at the technological frontier.

714
00:40:45,880 --> 00:40:45,920
Speaker 3: No.

715
00:40:46,239 --> 00:40:48,960
Speaker 2: Absolutely, And the one other thing I would add on

716
00:40:49,200 --> 00:40:53,120
Speaker 2: is I really like the Gila monster octhic example, because

717
00:40:53,360 --> 00:40:55,880
Speaker 2: this is something else that you see happen quite a

718
00:40:55,920 --> 00:40:58,960
Speaker 2: lot when people talk about research studies. So I think

719
00:40:58,960 --> 00:41:02,640
Speaker 2: Elon Musk has talked about like all these crazy scientific

720
00:41:02,719 --> 00:41:06,160
Speaker 2: projects like having shrimp run on treadmills and things like that,

721
00:41:06,719 --> 00:41:11,200
Speaker 2: but some of them actually lead to monetizable drugs, And

722
00:41:11,239 --> 00:41:14,360
Speaker 2: I think the shrimp example they were actually stress testing

723
00:41:14,880 --> 00:41:20,799
Speaker 2: marine animals or crustaceans ability to withstand environmental stresses. So

724
00:41:21,120 --> 00:41:25,200
Speaker 2: even if the projects sound very niche like with lampreys

725
00:41:25,280 --> 00:41:31,440
Speaker 2: or gila monsters, they can have interesting and potentially profitable consequences.

726
00:41:31,520 --> 00:41:34,520
Speaker 1: I'm never compelled, you know. People love to point out

727
00:41:34,520 --> 00:41:37,480
Speaker 1: areas of like, look at this, they spent one million

728
00:41:37,520 --> 00:41:40,160
Speaker 1: dollars to hold a really race among shrimp, you know,

729
00:41:40,400 --> 00:41:44,000
Speaker 1: or something like that. And I'm never compelled by I

730
00:41:44,080 --> 00:41:47,719
Speaker 1: believe that there exists government waste. There probably is even

731
00:41:47,760 --> 00:41:49,920
Speaker 1: a lot of it. I find those to be like

732
00:41:50,040 --> 00:41:55,480
Speaker 1: the least compelling. But it's not SPS. Maybe it's but

733
00:41:55,560 --> 00:41:57,640
Speaker 1: I but I do think we need to spend more

734
00:41:57,840 --> 00:41:59,720
Speaker 1: on shrimp doing relay races.

735
00:41:59,800 --> 00:42:03,480
Speaker 2: You know, It's funny whenever I hear guila monster. So

736
00:42:03,920 --> 00:42:08,200
Speaker 2: my dad, one of his best friends is called Gilah

737
00:42:08,440 --> 00:42:11,360
Speaker 2: and her husband is Dusty, and I always think that's

738
00:42:11,400 --> 00:42:14,960
Speaker 2: like the most Texan duo ever, Guila and Dusty.

739
00:42:15,280 --> 00:42:18,280
Speaker 1: Well that is, you know whatever, I always think about Guila,

740
00:42:18,760 --> 00:42:21,400
Speaker 1: although I see it's actually I was gonna say Guila

741
00:42:21,440 --> 00:42:23,960
Speaker 1: is one of the few words that was imported into

742
00:42:23,960 --> 00:42:28,080
Speaker 1: the US language from Southeast Asia. But actually in this case,

743
00:42:28,160 --> 00:42:30,680
Speaker 1: it is not. It has a totally different etymology. It

744
00:42:30,680 --> 00:42:33,120
Speaker 1: has something to do with the Southwest. So I thought

745
00:42:33,120 --> 00:42:34,200
Speaker 1: I knew something, but I don't.

746
00:42:34,320 --> 00:42:37,239
Speaker 5: Okay, shall we leave it there?

747
00:42:37,360 --> 00:42:38,120
Speaker 1: Let's leave it there.

748
00:42:38,320 --> 00:42:40,960
Speaker 2: This has been another episode of the All Thoughts podcast.

749
00:42:41,040 --> 00:42:44,480
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

750
00:42:44,160 --> 00:42:46,920
Speaker 1: And I'm Jill Wisenthal. You can follow me at the Stalwart.

751
00:42:47,120 --> 00:42:50,720
Speaker 1: Follow our guest Carol Lebon, She's at Lebon Lab. Follow

752
00:42:50,760 --> 00:42:54,640
Speaker 1: our producers Carmen Rodriguez at Carmen Arman, dash, Ol Bennett

753
00:42:54,640 --> 00:42:57,759
Speaker 1: at Dashbott and Keil Brooks at Cale Brooks. For more

754
00:42:57,760 --> 00:43:00,600
Speaker 1: odd Lost content, go to Bloomberg dot com slash odd Lots.

755
00:43:00,680 --> 00:43:02,840
Speaker 1: We have a blog and a daily newsletter that you

756
00:43:02,920 --> 00:43:05,279
Speaker 1: can subscribe to. And if you want to chat about

757
00:43:05,320 --> 00:43:08,360
Speaker 1: all of these topics twenty four seven, including hard sciences

758
00:43:08,360 --> 00:43:11,680
Speaker 1: and pharma, go to our discord Discord dot gg slash

759
00:43:11,719 --> 00:43:12,200
Speaker 1: odd lots.

760
00:43:12,520 --> 00:43:15,120
Speaker 2: And if you enjoy odd Lots, if you like it

761
00:43:15,160 --> 00:43:19,480
Speaker 2: when we talk about scientific research funding and the etymology

762
00:43:19,719 --> 00:43:22,640
Speaker 2: of the word dela, then please leave us a positive

763
00:43:22,680 --> 00:43:26,200
Speaker 2: review on your favorite podcast platform. And remember, if you

764
00:43:26,280 --> 00:43:28,960
Speaker 2: are a Bloomberg subscriber, you can listen to all of

765
00:43:29,000 --> 00:43:32,160
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766
00:43:32,239 --> 00:43:35,360
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767
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