The Exchanges

Q All right, so you are in Montreal, and, um, I know that you went on to, to study, uh, chemical engineering at McGill in Montreal, but then you went to graduate school in the U.S., you went to MIT in, in Boston, and from what I read, you, there is where you began to focus on biochemical engineering, right?

A Yeah, so as I was graduating from McGill, MIT was the only place I applied to, and now I know in hindsight that it was very hard to get in, and I might not have gotten in, but I must say I didn't know then. I, I just wasn't, maybe this is, I've learned since this is all kind of the, the comedy of being an immigrant, in that you actually don't know a lot of things, because people don't tell you you're supposed to apply to five schools, and you know, there may be some in Canada, some in the States. So I just applied to one school, Thank God they took me, and it was fascinating. It was just a completely transformative experience, um, and ultimately ended up, uh, joining a lab and pursuing a PhD, and everybody wanted to start doing this form of chemical engineering, biochemical engineering, because it was clear that a whole new industry was being born without the necessary engineering, cadre of engineers and, and principles to apply to actually making the The end product. So it was a really fortunate time to be entering the field. Took, it took about four years.

AI assessment note: “everybody wanted to start doing this form of chemical engineering, biochemical engineering”

Q And as you were developing more products for perceptive biosystems, who were your customers? I mean, I mean, obviously most of the people I talked to are making consumer products, so they're like, today it's social media marketing and going on Instagram to find influencers, but this is not what you do, or did then, obviously. Who were you selling your, your equipment to?

A Um, in the first instance, there were a handful of biotechnology companies at the time, Who were noteworthy. Biogen, Genentech, Amgen, Genzyme. And then there was a whole bunch of pharmaceutical companies, the household brands of, you know, Roche and Novartis and GSK, Pfizer, etc. And they all had research labs. They had huge research labs. And every one of them used to do what it is we were selling a new technology to do. And so, you know, early adopters were not a concept back then, but clearly early adopters is how you, how you get anything to be taken up and, and they become your best salespeople because either they change jobs or they tell their colleagues, Hey, I'm doing this thing really cool. You should look into it. And so eventually we started getting kind of slowly, slowly growing, hired our first salespeople.

AI assessment note: “Biogen, Genentech, Amgen, Genzyme. And then there was a whole bunch of pharmaceutical companies”

Q all kinds of research, and, um, in the early 2000, um, I read a story about some researchers from, from University of Pennsylvania who were, uh, doing some pretty groundbreaking work around mRNA. They went to a scientific conference, and almost nobody, this is 20 years ago, almost nobody in the scientific community was taking this seriously. When did you first, when did MRNA research first come onto your radar?

A Let me answer that by first pointing out to you that five years ago, nobody was taking MRNA research seriously, so that's kind of an interesting thing, but back in 2010, May of 2010, got a call from Bob Langer one day, and he said to me, hey, look, I just met with a junior faculty member at Harvard who approached me, and so I went over and met with him and met with the faculty member, Derek Rossi, And he showed some of the scientific work that this lab had done. Essentially, what they had done is they'd taken mRNA and used the codes for the four, what's called Yamanaka factors. Yamanaka factors are transcription factors. Think of these as proteins that interact with the human genome and control what genes get made and what genes don't get made. And that's what he was presenting, and he was interested in that as potentially The basis of something useful in the biomedical research field. I got interested on the spot, you know, what if we could do this to introduce it into the body and have our own cells make drugs out of it? And so I asked that question in the meeting with Bob. We kind of, he said, look, I don't, I don't know why we couldn't try. And so what ended up happening is we agreed that I would go back and initiate a project within Flagship To start exploring whether that could be something of interest and use without any regard to whether we could technically do it or ho…

AI assessment note: “back in 2010, May of 2010, got a call from Bob Langer”

Q was probably a coronavirus. And I, I think a few days later, the first death was publicly announced in China. And at At that point, I think that's January 1120 20, um, almost no one in the U.S. was, was talking about this seriously or in a big way. I mean, probably a few people, but almost nobody. Do you remember this story coming onto your radar around that time?

A Well, in the very early parts of January, we were exchanging emails and articles that were, you know, kind of appearing largely around the Wuhan Situation initially was thought to be a flu-like thing, pneumonia, and it wasn't until January 23rd, which I remember quite clearly, partly because it was my daughter's, one of my daughter's birthdays, and, and, and so I, I was out at dinner with her in Cambridge, close to MIT. I actually got a call from Stefan, who was in Davos. Uh, the situation had increased in terms of intensity. It was not clear what this would become. It was not a pandemic. There was not at all Viewed that way, but, but that it could become a threat was beginning to at least appear, and, um, our discussion, interestingly, was twofold. One, we probably didn't have any choice but to at least do the first steps of this work, because if we didn't, then we'd be late, uh, reacting to it later on, but then second, it was an interesting opportunity for us to test the platform in one of its earliest At least imagined advantages that we could never test in any other time, which was that it would be a very rapid response technology. This technology had this innate advantage. We knew from day one that if you ever needed to go quickly, we could really go quickly because it was so, it's just a code molecule. You just sequence, put the sequence in for the DNA. You make the RNA …

AI assessment note: “it wasn't until January 23rd, which I remember quite clearly”

Q We're just going to start something in biotech, and we'll kind of figure it out. And, but it is amazing to me that, that meeting with David Packard, because he was, I mean, of course, everybody knows that name now, right? You let Packard in a, but it really sounds like that encounter kind of started to get the gears in your head turning.

A Well, yeah, it definitely, I'll tell you one of the things that I've thought a lot about, and, you know, generally, I realized These are fields that people don't talk about, and you only really, at least back then, used to hear about it, glorified in some success story, which, like Michael Jordan playing basketball, is completely unapproachable for somebody who doesn't have the physique and doesn't have, as I did, I grew up playing basketball, I still play some basketball, but Michael Jordan was never an inspiration to me because he was so, so aloof, if you will, physically from, I could not do what he did. Here was this guy who was completely grounded and normal, and telling me about how he set out to do this, and the way he said it actually made me feel like, and you can do it too. And it was fascinating. That probably singularly, his making it approachable, um, relatable was an important, very, and that's why I've kind of tried to do the same to as many other people as I can.

AI assessment note: “his making it approachable, um, relatable was an important, very”

Q that the news was good, that, um, the initial results from, from phase one were good, but, but you had to go through two more phases, and we, the public, were not, um, uh, privy to that information for a variety of reasons, right? You can't, you could not come out and say, hey, phase one looks great. These are the results. Is that, there are rules against that, right?

A Well, we indeed, in May, we indeed did come out and say at a very high level that we had some encouraging results, and people attacked us every which way for having said that, because they basically said, well, that's doing science by press release and many other things, and the reality is when you have a pandemic, and you're a public company, and you've got material data, our sense was that we at least needed to at a high level Put that out there. So one of the things we learned, because none of us have been in this situation before, and was that we were going to get criticized no matter what we did. And so we just had to do what we thought was right, uh, make some calls, you know, explain ourselves. And so we did that. We, we ultimately published the data. The NIH came out and presented their data. We were quite surprised that people were questioning the NIH's own clinical, clinical research. We didn't do the clinical work. They did.

AI assessment note: “we indeed, in May, we indeed did come out and say”

Q How did you do that? How did you go from the capacity to make a thousand or 10,000 to a Billion in less than a year.

A Well, first of all, all these things start with people. We had the great fortune of having a gentleman named Juan Andres, who, as head of our whole technical operations, manufacturing, quality, the whole field, previously held that role at Novartis, one of the largest pharmaceutical companies, had gone through previous flu epidemic, uh, uh, kind of scale-up of vaccines of that generation, and just is a phenomenal leader of people. And he was really a godsend to us, and when it came to planning rapidly, responding, organizing, hiring people, and just not taking, that's not possible for an answer for just about anything that they, that they did in terms of either timeframes or scale, and, uh, several months later, we started producing millions of doses, and then, and then eventually tens of millions and hundreds of millions of doses.

AI assessment note: “We had the great fortune of having a gentleman named Juan Andres”

Q mistakes. Um, how did you know how to do that? I mean, you had partners and people involved. And I mean, even with your investors, you're 24, 25. If they were like, yeah, we'll give you, you know, a 100,000 dollars for 10% of the business. Like, how did you know What it was worth, and how to structure it, and did you, did you go to anybody for help?

A I did. I went to lots of people for help, and it's actually one of the more formative things that I've learned, which is that if you don't pretend you know a lot, people are much more prone to kind of advise you and help you, and it's not hard to pretend that when you're 25 years old, but that is the first time that helped me was, was back in those days. I clearly didn't know much about Raising money, spending money, planning, budgeting. I did have a lot of people around me who were willing to give advice. Quickly realized that a lot of that advice was quite, you know, kind of opposed to each other. So you had to figure out.

AI assessment note: “I did. I went to lots of people for help”

Q Nubar, obviously by this point, when you started Flagship, you were a known entity. People knew the story of, you know, your previous businesses, but how did you recruit talent to come, to come to Flagship and to take that leap that you guys would, would have their back?

A Well, I would say it took quite a while. Um, so the first battle was hiring leadership into our companies because our own team size didn't have to grow very much, but the companies we were forming needed to attract leadership. And that was where we started really thinking more systematically what kind of people could make the transition, unlike the software field. And for that matter, these days, internet based companies or apps, you know, there's a lot of people who've done multiple companies. In biotech, these companies have a half-life or a life cycle of, say, 10 years, 15 years. So people don't do one company after another, after another, after another. If they do one company after 1015 years and they succeed, they stop doing it. So you have to create the talent. You have to create and cross-fertilize. And so the early leadership of our companies is all flagship team members. And, you know, that, what that does is It doesn't mean that they're any better. It just means that they're more experienced and we're more familiar with them.

AI assessment note: “So you have to create the talent. You have to create and cross-fertilize.”