Dave Ricks, CEO of Eli Lilly, on GLP-1s and the business of pharma
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What is the origin story and current focus of Eli Lilly as introduced by Dave Ricks?
Dave Ricks is CEO of Eli Lilly, which is now a $700 billion company and the world's most valuable pharma company. Eli Lilly is 150 years old. They grew up as the first company to mass produce insulin in the 20th century. But today, most of the company's business is in the new GLP1 diabetes and weight loss drugs, where they've become the market leader. Simultaneously, Eli Lilly is upending the traditional model by selling directly to their consumers over the internet with LilyDirect rather than through the traditional middlemen.
All right. Cheers, cheers, cheers coming.
I'm very impressed that you came and you just poured your own pint.
Yeah.
Major flex.
Have glass will pour.
Exactly. Well actually a good place to start. Tell us about your um NVIDIA announcement that you just had.
Yeah, so today at the what's it called? GTC conference they have, they unveiled that we're well underway, actually should be done by the end of the year, but building a supercomputer on our on-prem for us, really just to run proprietary drug discovery models. We think it's the biggest. um biologically focused supercomputer there is. And certainly the biggest pharma's done. Yes. Um with B three hundred's latest chipset and uh Yeah, we're only constrained by power like everyone else. But um yeah, we built a bunch of tools, we'll run them on that. And uh scientists use it to sort of co invent, co-develop, focus mostly on chemistry to begin with, but we'll expand from there.
And so the is the idea here you have some target, you've had some challenges actually drugging it, and so you give it to one of these new chemistry models and it's you know, you ask it whether it can come up with something totally orthogonal beyond what a human might have tried.
So take a take a really good popular example is like uh GLP1. So that's a hormone, peptide that we all excrete. It engages targets that are what we call um G protein coupled receptors. So they're hard target hard to drug targets on the outside of cells. And to try to mimic a big, huge protein with a very small chemical. Is a complicated undertaking. And by the way, do only that and not other things that are untoward. And so this is sort of a frontier of drug discovery that's been tough and very empirical. That's a hot area for this kind of technology because these strange arrangements of atoms don't look like other drugs that have come before, but they do follow the principles of organic chemistry and seem to engage these targets effectively.
I don't know. Of one that's come through the machine driven discovery process, which is really machine plus human, that's made it to the clinic yet, but they're coming. And I think that's exciting because those have been structures that are they don't exist in nature. And yet uh the machines are alien and they can predict these. Uh these interactions.
Derek Lowe's arguments where he's always sounding this note of caution, I guess, um uh about the uh the optimism and maybe what he might view as boosterism around AI and biomedicine, where as I see at least his two claims are one, it's really hard to select the targets and AI doesn't help you that much there.
Nope.
And then so much fails at like human toxicity. And again, at least so far, AI has not been all that helpful at that step. Do you do you agree with him or is he overrating you know these particular challenges and maybe underrating the challenges that AI does help solve or you know, thoughts
in that argument?
How is Eli Lilly using a new on‑premise supercomputer and AI to accelerate drug discovery?
Probably we need to create The equivalent of what got created with human language, which is a a more complete repository of biological knowledge to train against before the machines get a lot better. And today, I don't know, I would estimate we might know ten to fifteen percent of human biology. So the machine's not gonna be good at all until we get way above fifty percent. That probably requires, you know, robotic 24 7 experiments just to create training data sets and you know sort of this kind of big lift effort. The kind of thing actually NIH should be doing right now, I I would think.
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Chapters
7 chapters
1
What is the origin story and current focus of Eli Lilly as introduced by Dave Ricks?
0:00–3:26
2
How is Eli Lilly using a new on‑premise supercomputer and AI to accelerate drug discovery?
3:26–36:42
3
Why are clinical‑trial enrollment costs so high and how is Lilly trying to improve enrollment?
36:42–1:01:38
4
How does Eli Lilly fund early‑stage research and allow teams to explore ideas?
1:01:38–1:08:14
5
What is the origin story of GLP‑1 drugs and how did Lilly turn a Gila‑monster protein into a weight‑loss therapy?
1:08:14–1:20:09
6
Why are “shadow generics” eroding patents and how is the U.S. first‑to‑file system influencing biotech innovation?
1:20:09–1:38:00
7
How is LilyDirect changing drug pricing and distribution by selling medicines like Zepbound directly to patients?
1:38:00–2:05:11
Speakers
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