00:22:04 · Helmy Eltoukhy
[00:22:04] S0: I would say that a lot of it I think we sort of inherited in some ways. It wasn't that long before us that essentially whole mechanisms for reimbursement for these more expensive precision medicine tests sort of came into being, and so without sort of that happening or that foundation, like, you know, Guardant wouldn't exist, and we are sort of beneficiaries of all that work that had been done by Genomic Health and other companies like before us. I would say that maybe the area where we have innovated is sort of international expansion where, just like The US has its own model, I think the mistake that companies make is sort of taking The US model and applying it to every other country in a cookie cutter kind of way. And what we found in Europe, something that actually works really well, is this public private partnership model where essentially we go in and we say, Okay, here are the keys to the technology. You guys help us run it. We partner with the top sort of cancer center there. Because they're also part of the of the national health system in some ways, they're able to sort of go to bat and say that, okay, this is great technology, let's try to get it reimbursed, let's do all the pilot data, all the clinical studies. And so from, even though the prices may be different in terms of how those prices, those tests are reimbursed, from a sort of P and L perspective, it's still, I think, sustainable and very viable for a business model. And so it's finding out how to take the technology and fit it in different ways so that it fits the healthcare system there. I think that is something that, you know, it wasn't obvious from day one. It took us probably five or seven years to figure out, but is working really well now.
00:24:03 · Mark Jacobstein
[00:24:03] S1: I think another interesting example there was actually the pharma business. Daniel Simon says hello. He could not be here today. But in the very early days, there was no pharma business, right? It was a consulting project to see if there was even any pharma demand. And obviously, I don't know if you want talk about how that played out, but that felt like a pretty innovative strategy as well.
00:24:25 · Helmy Eltoukhy
[00:24:25] S0: Yeah, I would say that that's something that a lot of companies struggle with. We did, too. We said, pharma market's an important market, but it's not a $10,000,000,000 it's not a $50,000,000,000 TAM. It's a much smaller TAM. So we said, is that a distraction? Should we just focus on the clinical markets, which are the sort of bogey that we're going after, is everything else a distraction? You know we actually straddled the fence between the two and we found that working with pharma made us better. It was frankly a nice source of high gross margin sort of non dilutive funding in some ways, but we learned a lot. If you think about the market, it's, you know, you think about pharma and clinical trials, it's sort of the pre commercialization, pre commercial drug sort of side of things. So it became a leading indicator for us if we knew that they were buying signals on the pharma side, they were likely to be sort of buying signals on the clinical side as well. It allowed us to test other things much more quickly as well, so it's become, I think, a strong component of how we develop new products, how we sort of validate them, and so on. And, you know, we work with 200 pharma companies now, and yeah, they've made us, I would say, their partnership has made us better.
00:25:44 · Mark Jacobstein
[00:25:44] S1: But also felt like all these different pieces of the puzzle supported each other. By working with pharma, you ended up with more credibility with the KOLs who also worked with pharma and that gave you credibility when you went to talk to payers and if you built a companion diagnostic assay with somebody that would help you with the FDA and like this is one of the reasons, it's a very complex business, but you sort of had to execute on all those things. Maybe switching gears, you know, Guardant launched in part because of the early, early, early ML revolution. Like you couldn't do Guardant without ML, right? Was like very early, like you couldn't look at the amount of data that Guardant was looking at and make sense of it without sort of the AI revolution v one. Obviously, we're living now in like the AI revolution, and it feels extraordinarily transformative, even more so than the dawn of the internet or the dawn of mobile or the dawn of cheap omics. You're running a big company, you have a billion dollar P and L, but we're in this transformative moment. How are you thinking about that and not getting caught in innovator's dilemma? I don't know how much you can talk about what you're thinking about for the future. We talk a lot about minus one to zero here at SPC. I'm curious how you think about what's the minus one for you and for Guardant right now.
00:27:05 · Helmy Eltoukhy
[00:27:05] S0: Yeah, so it's frankly a really exciting time. Mean, one of the things we, our sort of tagline or mission statement is conquering cancer with data, which you're part of in terms of when we sort of revamped that. And we don't take that lightly. We have, I now one point three million cancer patients that we've tested. We have 700,000 epigenomic profiles, which I don't think anyone has. And we have all of that married up with a lot of real world evidence and outcomes data and so on. But the thing that we are missing, I would say, is enough bandwidth and ability to sort of do the analytics on that and drive a lot of the insights. And now with a lot of the new AI tools, it's really given us this amazing increase in velocity essentially. Every single one of our R and D individuals is now a manager of multiple agents essentially being able to go in and do all of these analyses and develop these applications on that data. There's a lot of excitement in the greater field about sort of like AI being used to cure disease or discover new drugs and so on, or foundation models for cells, and I would say that 90% of that is probably overhyped in some ways. And the reason for that is that the underlying data that's collected to essentially look at those models is very under sampled. It's looking at a lot of these cells or these processes or RNA transcripts with very low resolution. 60% of a cell is mostly housekeeping genes, and if you under sample it, all you're seeing is essentially the standard stuff, not the stuff that is actually making those cells unique. And so I think we took things the other way where we developed, I would say, very exquisite, like very deep technology to look at like a lot of those things, but we just didn't have the bandwidth to sort of analyze a little bit. And now we do. And so it's really been a, I would say, sort of a renaissance for us in terms of like, you know, the velocity of new applications we're developing. And it's really been a godsend, I would say, in terms of what we're doing. And now we're putting AI on other reports, and we're able to take stuff that I think you, like we all envisioned when you were there, that just wasn't possible technologically at the time.
00:29:40 · Mark Jacobstein
[00:29:40] S1: We had big plans in 2018. As I recall, I mean, there were a lot of issues. I mean, biology's complicated and then like, okay, we've got at the time hundreds of thousands, not millions of metastatic patients. We have a view across their genome. We've got an extra tube of blood in a freezer, but you needed phenotypic information and downstream stuff. It took a while, but play this out three, four years. What do you think? Now it's working. You got modern AI tools. You have over a million metastatic patient samples in the freezer and a lot of information on them already. What's the most optimistic scenario here? What falls out of this?