Ep169: Kevin Fitzgerald on the Past & Future of RNAi Medicines

Thank you. Thanks for having me.

I've been here quite a long time, right? So, I've seen a lot of changes over that period of time. I've actually done a number of jobs over that time, but it's been, you know, one hell of a ride. So, I wouldn't change a thing.

Yeah, so I grew up in upstate New York in a little town called Massena. And from there I went to Cornell as an undergraduate and there I got my bachelor's. And you know, I wanted to be a veterinarian out of the gate and was that way back when you first... you know, I liked animals, thought that would be great. And my dad was pretty smart. He, you know, made me go work for a veterinarian over a summer, one college summer, and, you know, figured out that it wasn't all about saving dogs and rainbows and ponies, that there was a lot more to the job and decided that I might want to go a different direction.

And so from there, I, you know, ended up on a Ford Foundation fellowship over a summer to work in a lab.

I mean, I'm a typical kid. I played a lot of sports. I liked animals, but I didn't really have a strong sense of direction, I would say. And I think, you know, a little bit of it, I wanted, you know, I liked animals. I'd seen a veterinarian on TV. Had a friend whose dad was a vet. Thought that was pretty cool. Until I got into it. Did a bunch of other jobs along the way that were, you know, less interesting jobs. You know, I worked at a Hickory Farms which sells sausages. I worked as a janitor one summer. And so I had a lot of it put in perspective. I knew what I didn't want to do. As I got into college, I was sort of thinking, you know, veterinary school, Cornell is a good place to go for that. I was in-state so also we didn't, you know, I didn't grow up with a lot of money. And so having an in-state school was important for our family. I had two older brothers and a younger sister, all whom I, you know, parents wanted to take through college. You know, my dad was the first in his family to go to college. My mom never went.

So, my dad was an engineer at General Electric. And so he didn't know what he wanted to do after high school. Again, we didn't, you know, didn't come from a lot of wealth and so he ended up on an internship program at Union College through GE. And there they would fund you as long as you got, you know, at least C's. And so he, you know, worked his way through there, you know, sort of day and night. He wasn't that good at math. He's an engineer, not that good at math. Managed to actually get through the math class by washing dishes for his math professor. So he washed dishes and then the guy would tutor him. And that's, you know, because if you got, you know, below a C, you're out of the program. And so his whole life is changed by that one math professor who helped him, you know, get through that time.

Willing to put in the work and hustle. And he taught me a lot about putting in the work and hustling.

Yeah, it's a big school. I think that was Ivy League or no Ivy League, it was just a very big school for me. So, the first, you know, year or two was an adjustment. You know, you don't have any, you know, there's no safety zone. And so, I did okay. I don't, you know, I didn't do tremendously well. I think my worst subject was probably biology, interestingly enough. But you know, that changed over time.

So you know, after I worked that one summer for a vet, the next summer I got this, I had applied and got this Ford Foundation fellowship. They would pay you over the summer to work in labs. And so I got a job back at Albany Medical Center with a guy named Brian Freid. And so he was kind enough to take me into his lab. And there, I didn't know it at the time, but they had me matching patients with transplants. And so I was working with somebody who was trying to figure out, you know, whether organs were compatible. And I thought that was really cool. And from there, you know, ended up going back to Cornell and finding different labs to work at. And I sort of caught the laboratory bug, as they call it.

Hands-on science that appealed to me. Doing things with my hands that always appealed to me. And a little bit of the intrigue around, wow, you actually can put, you know, organs from one person into another person, right? And that doesn't always work. And so, you got to figure out how to do that and how to not have the immune system, you know, attack the organs as foreign.

Yeah, I mean I started to obviously when you're that age, you know, you're just trying to figure things out and, you know, get the right things in the right wells at the end of the day. But that was, you know, it was a fascinating time. And then, you know, I ended up working in a Drosophila lab, so studying fruit flies. And then eventually decided to go off to grad school. I thought about, you know, an MD-PhD. At the end of the day, probably going to be expensive and wasn't sure whether my grades would quite get me in. Ended up doing well in the GREs and ended up at grad school at Princeton. And so I had a couple of choices to make there. I could have gone to Princeton or Harvard or a couple of other places and ended up choosing Princeton because it was smaller actually as compared to Cornell.

So there, I wandered from lab to lab and wasn't quite sure what I wanted to do. And I'm not sure I was anybody's first choice to take into their lab at the time. You know, wasn't a stellar graduate student academically, but on the other hand, ended up in a C. elegans genetics lab. And that's, you know, studying these tiny soil worms that are clear and where somebody had counted every cell and where it ended up. And what I liked about it is I could visualize, I'm very visual as a person and so looking under a microscope and studying, you know, cells as they divide and seeing them turn into an organism was pretty cool to me. And then the genetics part of it was, you know, sort of a side thing for me. I ended up doing molecular biology in a genetics lab.

So I graduated from high school in 1985 and then so I was in, you know, graduate school, you know, after that. I mean, I went to college. So 1989 and then from 1989, you know, till I don't know, '95-ish.

Not until like when I was a postdoc and then I got over to, we can fast forward to, I started my first job in industry was at Bristol-Myers Squibb and that was when the whole genomics thing started to hit. So we can talk about that. Seemed like it could be a wave but at the time no, it was just, you know, it was interesting. I mean, interesting genetics, interesting things to do. I was getting a PhD, you know, I was playing basketball at noon. You know, doing lab work, which I liked. And I was probably one of two kind of molecular biologists in a geneticist lab. So, that was an interesting place to be where people were talking about, you know, mating animals together in different screens to find, you know, genetic mutations. And I was more engineering and figuring out how to make transgenic animals and injecting DNA into them and doing a lot of the molecular biology of that lab.

I wasn't thinking that far ahead. I knew I was going to do a postdoc and so I knew I wanted at the time to get closer to medicine, right? And that was one thing I figured out along the way. And C. elegans were great, but, you know, you was writing grants and saying, and then this is going to cure cancer. And, you know, and I worked on some interesting receptors that were involved in cell proliferation. I was characterizing the Notch pathway as one of the first individuals to characterize that the intracellular domain of a receptor by itself could signal because back in the day, everybody thought the receptor had to be intact. And what I was able to do in molecular biology was overexpress just the intracellular domain and show that it was constitutively active. And that was a non-cell paper way back in the day. And that was something I was pretty proud of. And that's involved in a lot of different kinds of tumors, but it still always seemed to me I wanted to get closer. And so when I was looking for a postdoc, I wanted to at least go from, you know, a C. elegans, you know, to some mammalian system. And so I ended up working for Phil Leder at Harvard Medical School doing transgenic mice and studied cancer.

I was there about two and a half years as a postdoc. And that got cut a little bit short. I was doing well and at that point in time thought maybe I'd want to be a professor, you know, because that's what everybody did. Biotech, you know, Millennium was, you know, kind of starting up in Boston and they were, but it wasn't the same and people were discouraged from going into biotech. And so I was a couple years in, you know, that lab was great at making transgenic animals and so we had RAS knockouts, we had P-53 knockouts, we were crossing them together. I did a screen trying to find genes that would cause breast cancer. And so as part of that screen, you know, and I had studied Notch signaling, you know, in C. elegans for a number of years, I was happy I was done with that, but I pay attention to the worm field and I, you know, did this screen and came up with Notch in mammalian cells that's causing breast cancer. So I started with Notch, I ended with Notch again. That will be a recurring theme as I go to Bristol-Myers Squibb as well as that same pathway. But so I'm there for a couple of years. I'm, you know, publishing some papers doing pretty well. And I got a call from a friend of mine who had graduated, was in my graduate class, had graduated and had gone into executive recruiting. And she was calling because there was this position to be filled. She was looking for names of people. So she kept calling me and I would give her names of people who might be interested and by the third conversation she's like, actually think you're perfect for the job and I was like, not interested in Bristol-Myers Squibb, not really interested. So but she's a persistent lady and she kept calling me.

I still was thinking I was going to go the professor academia route. I didn't know much about industry or what it was all about and so, you know, and I was pretty busy with what I was doing.

Yeah, I mean I was interested in the pathways that I was studying and learning things about how, you know, was involved in angiogenesis and so it was a very, you know, Phil Leder created a really nice environment for people. He was Howard Hughes. So he had the funding, you know, he had patented the RAS mouse, so they had additional funding coming in from pharmaceutical companies. And so we, it was a little bit of a science playground where you could feel free to explore. And a lot of his people have gone on to his credit to be quite successful because, you know, when you came into the lab, he would say, 'All right, this is what the lab works on, and you are welcome to work on it, but this is what my graduate students work on. So you can work on this and publish because there's a lot here, but you can't take that with you. But anything else that you do, you can take with you and you can start your own lab on it if you so choose.' And so lots of people left with, you know, whole pathways and reagent sets and everything to go be successful and that made him happy. And so but he had also interestingly placed, you know, not more than a handful of people in industry at that time which was unusual. And so he also knew because he had, you know, he had a patent on the RAS mouse. And so he was a little bit into that business. So he knew the whole, you know, pharmaceutical business better than I think a lot of the professors did. And so he was not, you know, opposed to people thinking about that either. And there were people who had left that lab like Bob Tupper and others who were quite successful.

Well, not in that lab, although there was always that undercurrent, right, within academia that it was initially where scientists who couldn't make it went. You know, I think that changed a lot and maybe, you know, flipped it on its head after a while. So, you know, she kept calling me and so finally she said, 'Look, how about I'll put it to you this way. You still have friends down in Princeton from graduate school?' I was like, 'Yeah, there's a couple people settled there.' She's like, 'So, I'm going to give you a three-day all-expense-paid vacation to Princeton, New Jersey. I'm going to put you up at the nice hotel in the center of town, and all you have to do is go give a talk at Bristol-Myers.' And I thought about it, said, 'I can do that.' And so I went down, you know, long story about the interview because I, you know, back in those days, we didn't have GPS and they gave me directions. There's two Bristol-Myers sites. They gave me directions to the wrong site and then when they gave me directions to the right site, I had a detour in the middle. So, I ended up flagging down a FedEx truck. But they got me to the interview. And long story short, I came back with a job offer. And it was a new genomics department that was being started by, you know, one of my now still mentors, Elliot Scolnick. And he was starting a new department, you know, offered me a job. And then I had a dilemma. And so I went to Phil's office. You know, it took me a couple days to have the courage to go in and talk to him and said, 'Well, I have this job offer. You know, what should I do?' And I'll never forget, Phil looked at me and he said, 'Well, as your mentor, it's my job to see that you get to do what you want to do. That's my job. And you clearly don't know. So, why don't you go down and try it for six months? If you don't like it, come back. You've been on a Leukemia Society fellowship. You haven't cost me any money. I'll put you on a Howard Hughes and just go try it.' And so that sort of relieved the pressure. And then on the way out the door, he turned around and he said with a giant smile, he said, 'One more thing.' And I said, 'Well, what?' He's like, 'Well, if you come back, you come back at your old salary, of course.'

And that made me align. So, he kind of knew it would be hard. But I did end up working with a graduate student who was very happy because he inherited all my projects and published quickly and got out of grad school faster than he would have. But I helped him and we published some stuff out of there. So then I ended up at Bristol-Myers Squibb.

I was there for a few years. You know, I was in a genomics department. It was pretty exciting around the time the genome was, you know, starting to get sequenced. We were working on early technologies like high-content imaging. You know, Affymetrix chips back in the day were the first transcriptional profiling but even before that we did profiling on filter paper. And we were really trying to, our group, one of the aspects was to take drugs that had some sort of a toxicity profile that was unexpected and figure out why. And so that was one aspect of the job. So it was a little bit, I like it, was kind of like an investigator, kind of detective-like, you're trying to figure out using all these different technologies, you know, proteomics and high-content imaging and other things to try and figure out what the hell's wrong with this compound. And so I remember, you know, one of the first compounds they gave us was this Alzheimer's compound that had showed up with this weird tox. And so we put it through various models. We fed it to fruit flies. We gave it to worms. We did proteomics. We did transcriptomics and very clearly it turned out that they were gamma-secretase inhibitors and they were also inhibiting Notch and Notch was the problem. So common theme, I'm right back to Notch. And so, you know, so we did those kinds of investigations. Was involved in trying to repurpose some drugs to say, okay, there was an immunology drug that had failed and we figured out that maybe it would be better as an oncology drug and so we were able to do that and that drug is now on the market. So, you know, interesting things like that over the years.

No, I was doing well there. I, you know, Elliot actually, Elliot Scolnick who had hired me, he actually went on to be the CSO very quickly at Bristol-Myers. You know, I couldn't quite hold onto his cape as he flew up. But, you know, it was a good, it was actually a very good group and it was an exciting time to be there. You know, the company wasn't doing great at the time they were reinventing themselves, you know, they had a couple of drugs come off patent, had a couple of failures and so, you know, the company would keep reorganizing here and there. But our little group was, you know, quite fun to work with and, you know, we brought all these technologies to bear.

So, you know, science is small and biotech is smaller, you know, and so I got actually a call from the person who worked across from me in Phil's lab. And so, he was an MD-PhD named Kristoff Westfall who said, 'Well, here's this RNAi technology thing.' And I'd been following RNAi because we were actually, you know, I kept following the worm field. And of course, the Nobel Prize came out of Fire and Mello's lab. And I was working with RNAi at, you know, at Bristol-Myers and so I knew the technology and, you know, he was starting this new company and wanted to know if I'd be interested. But I hadn't been at Bristol-Myers for very long and so I was like, well yeah, not right now. When Kristoff was starting the company, this was like 2001 or 2001, yeah. So I've been there about two and a half years at Bristol-Myers. Had had some interaction with him because he'd gone to McKinsey, one of his McKinsey clients was Bristol-Myers. And so we'd kept in contact. And so I turned him down, you know, turned him down. I actually I think I had one phone call with one of the founding members. But had kept an eye on the tech and about four years later. So my wife had been a postdoc in Boston in an immunology lab and so she took a job in Boston. And I was, we were getting tired of going back and forth. There's no easy way to get from, you know, Hopewell, New Jersey to, you know, Boston, Massachusetts. It's six hours no matter what you do, whether, you know, trains, automobiles, it don't matter. And so I started looking around at that time. And, you know, sort of by coincidence, right around that time, Alnylam had come to pitch Bristol-Myers Squibb on RNAi and what the future was. And so I, you know, noticed that they had an opening. You know, I applied to a bunch of places in Boston, but noticed I had an opening and sent it in to their business guy who I'd struck up a conversation with, a guy named Nagashe. And I'd struck up a conversation while he was at Bristol-Myers and wasn't sure if he'd remember me. But he turned out he did remember me. And so I got a call about an interview pretty quickly.

There's probably 90-some I think. I don't remember honestly. Probably between 90 and 110. Well, maybe a little fewer.

So, so when I joined one of the things that I had clearly in my mind is I wanted to do programs, right? I had done the technology aspect of things at Bristol-Myers. You know, I'd done the Affy chips, I'd done the screening, you know, we did sequencing. I knew all of the tech side of it and automation and I really wanted to do programs and again getting closer to medicines. And so that was my idea coming to Alnylam was that they, you know, we had a lipid nanoparticle technology out of MIT at that time that they were talking about. They just published a couple of papers on delivery because we, you know, looking at it, I knew that it was going to be an engineering problem, you know, not a biology problem because RNAi and siRNA, you know, I'm a geneticist. It's conserved across all species and cells, right? So, it's a little bit like, you know, higher species and antibodies. It's a naturally occurring mechanism that's everywhere. So, if you could co-opt it, I knew it would work. And it would change medicine. But, you're going to have to engineer around these RNAs. They're big. They're charged. They don't look like drugs. They don't smell like drugs. So, there's an engineering problem. How do you get this big charged 14,000 molecular weight molecule inside the cell in the right compartment?

Yeah. I mean, there's a whole immune system that's designed to recognize foreign RNA and DNA and not allow it to do its thing. And it's also too big to just sort of like a small molecule. It's not going to diffuse across the membrane and it's highly charged so it's also not going to diffuse. So it's got to get carried into the cell or, you know, or driven in somehow. So delivery of this therapeutic cargo into the cell was the core, the key challenge that was initially what we thought the key challenge would be. It was one of the challenges, right, of delivery and there were several. One was you needed to get it inside. You needed to then get it into the right compartment and you needed to keep the molecule stable long enough to transverse all of those compartments. And so, you know, my first project here, we had this lipid nanoparticle. And so, one of the first programs I worked on was something called PCSK9. Which at the time, you know, had just come out of, you know, UT Southwestern and their fabulous work there as a genetically validated target for cholesterol. And it's one of those perfect targets where there are gain-of-function mutations in people where you have gain of function, you have too much cholesterol, it's bad for you, you get heart attacks early, you have a, you know, partial loss of function, so you only have one copy of the normal gene. You're protected from cardiovascular disease. And there are people that have no copies of the gene, and they're perfectly fine. They have an LDL of like 20. So that's a human genetic validation which, you know, coming out of the genetics group and the other thing that I did at Bristol-Myers was work with the, you know, we had some collaborations with, you know, Eric Lander's group and we're following the whole Human Genome Project and the whole thought of genetically validated targets has been near and dear to my heart. And so as we've thought about targets at Alnylam, you know, every one of them has been with that lens of, you know, is it genetically validated and this is a classic example target where, you know, that you can't go too low, right? Especially with a knockout, you know, knockdown technology, a rapid knockdown technology that we have. So the floor is okay. You know that a gain of function is bad. You know that a partial loss of function is good. So all of that lines up for this kind of a technology. And it also happens to be mainly expressed in the liver, which is where we solve delivery first. And it's secreted. So you have a biomarker that you can measure in clinic. So all of those things line up for that target and your tool.

Yes.

Yes. And it would be helpful. Yeah. And that's, you know, they've done a study on people who have half the amount and they were 88%, you know, protected from, you know, getting cardiovascular disease and heart attacks. So, you know, the technology itself, like I said, was a challenge in terms of delivery, but the molecules, you know, they're, you know, they're a technology where you make these small RNAs. They're about, you know, we make 19 to 23-mers, etc., or 21-23-mers and the body does this naturally. So there are things called microRNAs that use this same system in order to modulate whether genes go up or down and so all we're doing is co-opting that system and designing our own molecule that's specific for one gene and one messenger RNA and then we're able to then just go in and lower that one thing. So in this case we lowered PCSK9. We could do that in animal models and show that when we do that LDL goes down just as you would expect from the genetics and then we went on to show that in people.

So it was interesting when we started. No. And so we started like I said with a lipid nanoparticle and so I took our first molecule for PCSK9 put it in this lipid nanoparticle which was going to be delivered intravenous and it wasn't safe and so we did a toxicity study didn't turn out well and so we really hadn't solved delivery. So we had to go back to the drawing board. And at that time we decided to, we had two different efforts. One was on these things called lipid nanoparticles that would be IV infusions but the other was a delivery system that could be, you know, we hoped would be delivered subcutaneously in the skin. And so we were working on both of those over the years. And while we were working on those, the monoclonal antibodies started coming along. And so that changed our perspective over time to say, okay, an IV infusion of this is not going to be good enough. And so the whole lipid nanoparticle approach to PCSK9 was off the table. And what we really wanted to do was to make a molecule that would be very long-acting and be subcutaneously delivered and safe. And so now why would you want that? So if you think about diseases like hypercholesterolemia, I call them silent diseases. And so they're silent and they're silent and they're silent until you have a heart attack and they try and kill you. And so when you have silent diseases, even if there are therapies like statins, people don't take them. And so drugs don't work when people don't take them. And so if you take, you know, people who've had a heart attack, you know, and I've heard this from cardiologists and you ask them, you know, here's a statin that's going to lower your LDL, that's going to take some of your risk off the table. Are you going to take it? Yes, doctor, I will.

Yeah, you see a seesaw up and down. Or they just stop taking them altogether because they, you know, they feel better, right? Or they don't know that that disease process is continuing to progress. And so, you know, what we're imagining for this as well as other of these sort of silent diseases is, you know, a prophylactic, you know, or a treatment that's once every six months. You go in, you get an injection, and then you forget about it.

Yes. Yes. We were thinking, you know, we didn't know whether we could get there. I was going to be happy with a monthly at that particular point in time. And so, you know, the profile that we put on the table was it had to be monthly to quarterly. But we were pretty far from that when we started that project. And so but we got there. But the subcutaneous injection that's, you know, pretty easy for the patient to administer, very little pain. And there were preceding products that were out there that were quite successful.

And, you know, you can have it done in a healthcare setting. You know, or, you know, depending on the...

I think there were two. So we had program TTR with our second lipid nanoparticle where we had several versions of a lipid nanoparticle. I think one of the key things for us is we were not afraid to take things into clinic and learn in clinic and then iterate on the technology from what you learned in clinic. So if you look at our early molecules, we took very different versions of lipid nanoparticles and then with the conjugates we took different versions of the conjugates, continually improving them. We learned how they behaved in human beings. We obviously did our safety studies for all of them up front, but then we learned how they behaved in terms of efficacy and then we're able to come back and re-engineer them and go back in. And so, we had one patient in our early TTR trial that showed an 80% knockdown. It was one out of like a bunch that did nothing. But that one we hung on to to say look that's proof that RNAi can work in a human being if we get this delivery thing right. I think the other was our first data with conjugates even though we were dosing them pretty high doses and pretty frequently at that time we knew then that RNAi works, it's going to be a drug class and we have some more engineering to do but it's not a matter at that point of if, it's just when.

Sure. So the lipid nanoparticle was designed so every time we eat a meal we have natural lipid nanoparticles that are called, everybody's heard of the LDL cholesterol, the good cholesterol, the bad cholesterol. Some of those particles are cleared into the liver and so the whole concept is could we sort of mimic one of these particles but then jam an siRNA inside sort of as a Trojan horse. And so that worked pretty well. We could hitch a ride to the liver. So you basically hide this double-stranded RNA inside this lipid blob and the body thinks it's one of these natural particles. It actually when you inject it, it coats itself, it coats with proteins that are in your bloodstream and rides its way into the liver and then releases. And so from there, it goes into hepatocytes and works. And so that was a lipid particle, but that was IV because those particles are too big to be injected into the skin. And so the other is you forget about hiding it. Instead of hiding it, you chemically engineer the RNAi itself. So we call it RNA, but there's no RNA left. It's all chemically modified so that it survives in the bloodstream because there are things called endo and exonucleases that just love to chop up RNA. And so you protect it from that and then we put on it a ligand for another receptor that's highly expressed in the liver called asialoglycoprotein receptor. It's about a million copies per cell and the whole purpose of that receptor in the liver again it's a big trash can. So it pulls in platelets that are spent. It pulls in proteins that have improper sugars on them. So things that are abnormal go in through this receptor and get jammed into the lysosome for destruction. And so again it's a little bit of a Trojan horse approach. We tag that with a ligand that makes it look like one of those things and the receptor grabs it and pulls it in.

In the beginning it was mostly focused on diseases of the liver because as you said that's where these things were naturally being and that's where we were able to engineer them to go. They had some natural proclivity to go there but between the lipid nanoparticles which were engineered to go there and the GalNAc which was engineered as a ligand for a receptor highly expressed in liver on hepatocytes they were both engineered to go there. So then we put our lens to what diseases can be cured by lowering a gene's activity and lowering a gene in the liver, rapidly knocking a gene down.

So this is interesting. In the early days, a lot of our biologists including myself who were working on those programs, we became the project leaders of those programs. So we took them into clinic leading the teams that went into clinic with them. And so I took PCSK9 all the way through phase one. We did a partnership at that point because we needed to in terms of cash and so that partner then picked it up in phase two but a lot of the other programs that we moved into clinic, we stayed with them and stayed either as leaders of those programs or as sort of the research representatives on those programs in those early days. And those became givosiran and lumasiran and some of our other drugs.

Yeah. And so, we put a large number of ideas into clinic from 2014 through 2016, and most of them ended up getting across the finish line.

Yeah. So, I think there was a period of time, and I think this happens in a lot of technologies. And our former CEO, John Maraganore, used to talk about the bloom on the rose. Which is you have a brand new technology and a platform and everybody loves you and everybody loves it. But then you got to get to the hard work of actually making it happen. And there are going to be ups and downs because you're doing engineering and you're iterating and people with a short time horizon will give up on you. And so we had a number of relationships with Novartis interestingly enough who later went on to purchase The Medicines Company and a couple of other companies that they gave up on the technology and decided to get out of it that it wasn't going to be as broadly applicable as they had hoped. And we were, it's interesting because on the outside all of this was happening but we knew that the science was moving and it wasn't an aha, but it was clear progress day after day, week after week that we were moving towards the goal line.

I didn't actually because I did, obviously we had ups and downs and good days and bad days. And especially after we did our second restructuring, it was mentally very hard. But I did believe in the technology and what we were trying to accomplish which is to create a whole new class of medicines.

We were making progress. It wasn't like the data was coming in and we could see our way towards Onpattro for instance with the LNP and we could see our way towards our conjugates actually getting there.

So I mean, one of the people ask me why did you stay? Part of it for me was I was enabled, I was always able to learn something new. So I came to run a project, I was able to be a project leader for a little while, probably was not a good project leader but able to learn how to be a good project leader and I was in charge of our delivery technologies for a while in the beginning. And so I got experience with programs and was actually running a group that had a lot of project leaders. And so we were learning from each other. So I was always learning something new. And then the other secret is you just if you stay long enough then all your bosses leave and then you can get promoted. So I've been persistent and I think over time you gather a knowledge base and I talk to people about careers all the time because they think that they're linear and they're not. They're step functions and if you gather enough experience that's useful to people and you learn enough at the company then eventually you'll get put in charge of a bigger and bigger piece of it. And so I think that's been sort of my journey over time. And so I got to become the head of research when the head of research left, interestingly right before we had one of those upsets. And so just over time I've had to learn how to manage bigger and bigger groups. And I think the key for me is trying to still be a content manager and not just a process manager. And so I'm still into the data, into some of the day-to-day, but also can manage sort of the logistics of a bigger organization.

Yeah, I mean I think there's luck, but I do think that our approach to having genetically validated targets that have biomarkers where you can very early in clinical trials know that your drug is engaging the target. It's rapidly knocking down the target you intended. So that allows you to get the doses right. So why do drugs fail? They fail because you picked the wrong target. You don't know enough of the biology or you're relying on an animal model like a mouse that isn't a human being. So, the human genetics really help you sort of get at least some good idea that this is going to go in the right direction in a disease. If you have a biomarker you can follow, that allows you to get the dosing right because drugs fail because you don't get them to the right dose. You don't know that the target's been engaged. So, if you could take some of those off of the table, that really helps create more luck because then you're just asking a fundamental question about, and luckily for us, the safety profile of the platform is good. So, for instance, when I was back at Bristol Myers, we'd lose a lot of drugs in GLP tox, these safety studies along the way. They would never make it. Small molecules. Which are more promiscuous. They have different flavors to them where RNAi is a little bit more templated and we don't generally lose drugs along the way because of that, especially our liver drugs that are well characterized, we know exactly how they behave. So you start to take a lot of the risk off of the table. You can't do that for every target because the genetics is only so good, it only usually tells you what happens when half of it's gone not when 90% of a target is gone and it doesn't always tell you everything that you have to look out for from a safety perspective, but it takes a lot of the risk off. So, I think when I look at the targets that we chose, we chose wisely.

Historically, we've shown that yes, there is. You could take all of those. I mean, if you look at our historical success rate, it's a lot higher than industry average. Now, will we come back a little bit towards the mean? Maybe a little bit as we get into diseases that are not monogenic. So if you notice we started out with TTR which is a single protein that causes disease. We started out with PCSK9 which is again there was good genetics around a single. Now as you get into some diseases that are multiple genes are involved or more than one cell type not just hepatocytes, we can talk about how we're now in brain cell types and a lot of things. Let's get there a second but standard industry rule of thumb is something like one out of 10 programs that enter the clinic will make it all the way to an FDA approval. Now that will vary a bit by indication and then rare disease versus common disease etc. But call it one out of 10.

I mean, I think we're close to probably I'd say five, six out of 10, maybe a little higher depending on how you count. One of the other things that we try to do is, we'll learn things early. And so that's the kind of data that you want to see as a CSO. I want to see phase one data that I can make a decision on. And but you're always going to have incomplete data. You always probably want more when you're making these decisions, but how do you know that you have enough of a line of sight? I think if you design your trial right and if you look at some of how we design our trials, we will have optional cohorts in a lot of them where we're actually taking a quick look maybe in, PCSK9 is a good example, we do healthy volunteers we could see LDL lowering but we really wanted to see was LDL lowering in people who have heterozygous FH or they have disease where they have too much LDL. And so we did a small look-see in a small number of patients and the technology is the dose responses are generally pretty tight once you get to an efficacious dose and so you can learn a lot from a small number of individuals with disease. So generally we'll have a quick look even as early as phase one in a disease setting and some optional cohorts that give us a lot of information about whether this is likely to work or not.

Yeah. But you have to make those hard decisions because you can't look at it as a failure, it's only a failure if you didn't learn. So you go back and you say, 'Okay, we've had programs where we've gone in and we've gone in eyes wide open knowing like look, we're going to knock this out in the liver.' But the issue is it's expressed in the liver and it's expressed someplace else. And there's no model in the world that's going to tell me if liver alone is enough. But I'll learn in phase one if liver alone is enough. We had one example where we got in, we got an effect, but it wasn't as big as we wanted because liver alone wasn't enough. And now, who knows, maybe we'll come back to that now that we could target both of those tissues.

So, I'll go back to where I started. RNAi and the components of RNAi mechanism like Ago2, they're in pretty much every cell in the body. And so, you go right back to where we started, which is it's just a delivery challenge to get the molecules where they need to go. So, we were able to engineer them to sneak them into the liver. And now we've been engineering them to go into other cell types like neurons in the brain or to go into muscle cells or to go into adipose or other tissues. So each of them needs a little bit of attention in terms of how you engineer them to go where they're going to go, but we can now drive them into other tissues. And once you get them inside, they work just like they worked in the liver.

Yeah. So you use a similar lens. So the canvas is wider like maybe I got a wide angle lens but I'm still narrowed down to like okay in this tissue or in these two tissues at the same time what do I know about the genetics of these targets? What are the diseases where I can make a life-changing drug? And that's kind of where we start. Is there an unmet need that we can fill? Are there patients that are waiting for the drugs that we're going to make? And then what's the genetics behind the validation of those targets? And then we're also blessed with the ability to use RNAi as a tool also to validate those targets. So we can go in and in some cases in models we can create a model of the disease and then cure the disease both with RNAi. So you can go down, and so I think we then start to then pick apart each tissue and then multiple tissues and look at all right, what are the diseases that are caused by genes that are defective in those tissues and then how can we go in and intervene with RNAi.

Yeah. So we have a program called zilebesiran which is in resistant hypertension. I'll start there only because it was in the clinic beforehand and so going back to that concept of silent killers, hypertension leads to stroke, leads to cardiovascular death which is still the number one cause of death in the US. And again there are drugs that can help with hypertension they have this seesaw effect that don't control your blood pressure overnight sometimes and it's interesting to look at control of blood pressure as you're sleeping is actually quite important for outcomes. And we've designed a drug that's once every three, every six months where the blood pressure comes down and it stays right. And so you're under control that whole time and really blood pressure, a little bit like cholesterol, it's how low can you go over what period of time that helps outcomes. And so it's a drug that we're now in resistant hypertension and we have that in trials and so the data continues to come out looks encouraging but you don't want blood pressure to be too low. And so that's the other you have to look at that and what you can see is that RNAi is changing the tone of the pathway but it's not going too low and that's what you would predict from that pathway and the consistency of the pharmacology. And so then the other program that we have is again something I worked on way back at Bristol-Myers Squibb which is Alzheimer's disease and I talked about gamma secretase inhibitors. Gamma secretase inhibitors controlled the cleavage of a protein called amyloid precursor protein and that protein has been known for many many years genetically to be involved in Alzheimer's disease. So people who have mutations in that protein or people who have mutations in another protein called presenilin, it's interesting because it's connected to the notch pathway and I studied that very early on. People who have mutations in presenilin that impact the cleavage of a number of fragments so APP is a giant protein and there are these protein fragments that come off and they end up in something called plaque in the brain. And so similar to our TTR program where that's a disease that's amyloid-like so you have TTR comes out of the liver it's mutant, it misfolds and it aggregates in tissues and what we're doing in that disease is to rapidly knock that protein down and reestablish and let the plaque clear. So all of these diseases the way that I consider them of Alzheimer's, TTR, all of these amyloids are diseases of deposition outstrips clearance. So you and I are probably making misfolded proteins. We are all the time. But our system is going in and clearing them out before they accumulate. And what happens in these diseases like Alzheimer's and alpha-synuclein and Parkinson's and other places is that the deposition outstrips the clearance. And so the whole concept is can you if you lower the amount of protein that's available for deposition you reestablish the balance of clearance is faster than deposition. And if you can do that over time the body will know exactly what to do and clear that plaque out. But the trick with Alzheimer's is to intervene early because once the accumulation is just the mountain of plaques is too high it's too late. So I think that's the jury's still out there. I think earlier is obviously always better. Same thing with patients with TTR, earlier intervention is going to be better. And that's always the case. You got to figure out how to find the patients early enough, but nobody's really done what we're doing. So, you have antibodies that can bind sort of clear out plaque, but then the protein is still being made. And so, the question just becomes and also with Alzheimer's, I told you it's a giant protein. Well, there's a part that's called the intracellular domain that is also aggregating inside cells. And so that's not a place where an antibody is going to go and be able to clear that. So it clears out the stuff that's sort of secreted out, but it doesn't deal with the stuff that's made sort of cell autonomously or inside the cell in which it's made. So RNAi can do that. So it's a very different mechanism. And so we're now in early onset Alzheimer's and we've, there's a different disease called CAA which I'll talk about in a second, but we're in that, we've been able to show that we can lower that protein up to 90% with a single injection intrathecal in people.

Yeah. And so we're in early onset and then there's another related disease where this protein accumulates but instead of in the neurons of the brain it accumulates in the blood vessels and it's called CAA. And so there's a genetic version of this that's found in the Netherlands and these families they get strokes very early. So they have a mutation in the APP itself and they get accumulation of A-beta 40 in their blood vessels and then those blood vessels get weak and they start to leak. And so you could follow those patients by imaging their brains and you can see how many bleeds that they've had. And once they've had over four or five of these called microbleeds then they're at a very high risk of having a stroke or a catastrophic event. And so there's a genetic population. And then if you start to look now across people that have just Alzheimer's, there's a fairly big proportion of them that likely instead maybe have CAA and they've had brain bleeds which can also affect cognition.

Yeah. So what we're going to do is again similarly to the thesis behind TTR we are going to lower the production of APP which we're doing and then the hypothesis is that those blood vessels will be able to clear the plaque out and then they'll become stronger and blood vessels are pretty good at sort of reorganizing themselves over time. You can see that with atherosclerosis and other things they can heal.

Yeah, I mean I think when I think about the various technologies, look, I hope they all work because it's good for human medicine, it's good for people, but I do like drugs that have controllable pharmacology. So we can make our drugs to be shorter acting, we can make them be longer acting, but whether they're shorter acting or longer acting, they will wear off. And in fact, we have something called the reverse siRNA technology where for our longer acting drugs, we can actually make them wear off. We can deliver another drug and make it wear off quickly. If you saw a side effect that was troubling, you can basically come in with an antidote. Yes. Exactly. And so that versus sort of an editing one and done. One and done. Like for certain genetic diseases where they're severe, I could see my way towards that. But if there were an alternative where I could do essentially the same thing, but control the pharmacology and if I got into an issue, have a way to have it wear off, that's appealing to me. And that would be, PCSK9 inhibitor would be one example of that. That'd be an example of that. Sure. Control it for 6 months, go back and see your doctor. It's not a permanent intervention. Yeah. And I think it remains to be seen with these permanent interventions whether they're, again I know how long it's taken us to get to really specific drugs with RNAi. These are also oligo-based matching. And so I would like to see safety over the long period of time as to whether they've just done what they've intended to do or did something else as well.

You know what's exciting about this is that it's just the first inning for RNAi. So if you start to think about, we've done one target in the liver at a time. Well, we can now do two targets, something we call Gemini, so I can do gene A and gene B that are involved in disease. I can silence them both at the same time. Or now silencing in other tissues, so there's other diseases. And then you can start to think about I could drive one RNAi to one tissue at the same time I drive one to the other. And so you're starting to get into combinations. And when you can get into combinations, that's where you start to think about, when I think about common disease, I think eventually as all of the genetics is done and we have more and more information and AI starts to analyze all of it, I think these prevalent diseases are actually just a collection of orphan diseases. And I think we'll start to get more specificity and then you'll start to see that these combination approaches are able to just go after these diseases in a very specific fashion.

That's even better, right? And you can do a long acting. So imagine going in to your doctor, say we fast forward, and some of these drugs are approved. Maybe you go in and you get a couple of shots, one for your hypertension, one for your LDL cholesterol, and then you walk away not having to worry about having heart disease for that year. And so, I also think about, so my dad had Parkinson's. And so I would look at his pill box and he was on a lot of drugs. And so when I look at that, I have him take a picture on his phone or have his caregiver do it and like it was a mess. He just could not keep track of what he needed. So I would have much rather had him go to a doctor, have a visiting nurse come and give him an injection every six months and then know that he's covered for that aspect of it.

Yes. And especially for these silent killers that make them, I think the other reason that people don't like to take pills I know myself is because then it's kind of a reminder that maybe there's something wrong with you. And you don't want to be reminded, you just want to be treated and then forget about it.

All right. Thank you. Thanks for having me.