Big Compute - The Computational Science of Drug Development

Episode Date: November 15, 2022

At the beginning of 2020, while a pandemic of epic proportions shut down most of the world, the life sciences industry was kicked into high gear, pushing to do what had never been done before – create a vaccine in less than four years.  Thankfully, modern day computational science lended a hand, making the previously impossible, possible.  In this episode, we speak to someone on the front lines of vaccine and drug development – Steve Mehrman of Johnson & Johnson, who harnesses computational power on a daily basis to elevate one of the most important aspects of our lives – human health.

The virus, virus, the virus was here. Ernest, don't get attacked by the virus. Hi, everyone. I'm Jolie Hales. And I'm Ernest DeLeon. And welcome to the Big Compute Podcast. Here we celebrate innovation in a world of virtually unlimited compute, and we do it one important story at a time. We talk about the stories behind scientists and engineers who are embracing the power

of high performance computing to better the lives of all of us. From the products we use every day to the technology of tomorrow, computational engineering plays a direct role in making it all happen, whether people know it or not. Ernest! Jolie! So much has happened to you since we last recorded. Oh my gosh. I mean, do you, I don that my wife and I were expecting our second baby, our son. We have a daughter already. But what was unexpected was when he came, he came six weeks early.

And as a result, it was all kinds of chaos because my wife made like this list of all these things that needed to get done before he was born. And the doctor had told us this is his due date, which was like the end of May. So we had planned all of this around at earliest two weeks before, because, you know, that's exactly when our daughter was born. He was born six weeks early. So. Oh, man. What was the original due date? I want to say the 28th of May. Oh, my gosh. We're so close together. And he was born on the 21st of April. Obviously, there were complications. So he had to be born early. But the situation was, from my perspective, absolutely crazy. It was one of those where she had gone in to see her doctor for a normal appointment on that Thursday, just to check up. They did some tests and they said,

you know what? Something looks a little off. We're going to send you over to the lab to get another test done. That's never comforting. No. So she texts me and says, hey, they're going to send me for another test. And by the way, they did the same thing with our daughter. Right. So this was not out of the ordinary. OK. OK. So she gets the test done and she texts me like a couple hours later saying they want to keep me overnight for observation. Oh, this is the same thing that happened with our daughter, mind you. Oh, really? I didn't know that. Yeah. So, you know, we thought nothing of it. She texts me and asked me to bring her like, you know, an overnight bag basically. So I said, okay, no problem. So I pack everything up. I head over to the hospital during the period when I started packing that bag and arrived at the hospital. I walk into the room she's in and

she tells me they're going to take him today. Oh my gosh. And I'm sitting here like they're going to take what? And she's like, he's going to be born today. And so like the doctor's in there, the nurses are in there and the doctor's like, yes, he needs to come out today. He's a little early, but it's not a big deal. He's already almost full size, all that kind of stuff. So I'm sitting there thinking like, oh Lord, we had already bought two sets of flights for my mother-in-law who was going to be the one to come help us. Oh yeah. Especially when you have like the toddler at home. Right. The first thing I had to figure out was what were we going to do about our daughter?

Because this was completely unplanned for me. Where was your daughter at this moment? At home with her nanny. Oh, with the nanny. So I called the nanny and I told her what happened. I said, this, you know, it's an emergency, blah, blah, blah. She says, don't worry about it. I'll stay with her overnight.

Aw. Do what you need to do. Go nanny. The best nanny. So then I immediately got on my phone and booked a flight for my mother-in-law to come the next morning. I was going back and forth and back and forth every day, like three, four times a day between the hospital and home, trying to keep both sides kind of balanced. Right. And of course, he was born early, so he had to go to the NICU and so it was insane and the chaos and then you're like trying to figure out work I'm sure on the side they said we're gonna put her in at 9 30.

This is p.m right p.m so I ran back home and grabbed the actual hospital bag that we were going to use for labor and delivery and grabbed a little plastic container I had and put some Texas oil in it. Oh that's right you did You did this with your daughter. Oh my gosh. Yes. And I ran back to the hospital. So I had everything now. I had the overnight bag, all the supplies we needed for all of that, put it into her room. And then I had that little thing in my pocket. Sure enough, 9.30 rolls around. The doctor's like, okay, it's time. And they wheel her in there and they're getting her all prepped and they make me sit outside the door. Is this for a C-section?

Yeah. And so once everything was ready, then they let me in and then they do the procedure. So I come in there and I'm sitting there with my wife and I pull a little vial out of my pocket and I spread the Texas soil on the ground. And just a little bit. Like, it's obviously an OR, so you're not supposed to. I was going to say, like, what were the doctors thinking when you were doing this? They didn't see it, right? They're like, this is a sterile environment.

Why are you pouring dirt on the ground? It's just a little bit of dust and it's on the ground and it's not going to harm anybody. But it wasn't like I was bringing in, you know, horrible bacteria and throwing it all over the place. This was just like a little bit. So he's born and it was probably collectively, if I look at like that Thursday to let's say the following, maybe Thursday, collectively the most chaotic week I've ever had in my entire life. Oh man, that would throw me for a loop as well. Yeah. So wait, I have a couple of questions here. I knew the story about you pouring the Texas soil underneath your baby

with your daughter, with your first child so that she could be born over Texas soil. But was she a C-section too? She was. So I never, okay, that makes the story that much more hilarious because, I mean, they must have seen you like squatting on the ground for a minute. Nope, they didn't. In the operating room, Ernest. For some reason, I always pictured this being in just typical labor and delivery, you know, where there's all kinds of chaos going on. But in like the actual operating room,

while they're cutting your wife open, you're sprinkling dirt on the ground. Oh, yeah. Because I know that when something like that is going on, all of their attention is on the procedure. So they're not paying attention to what I'm doing at all. So I can do essentially whatever I want. So what does this tube do? What does this go to? I had it in my pocket and I just kind of reached in there and took a little pinch and did what I needed to do. Oh my gosh. My wife like saw me do it and she was laughing about it. Oh, she did. Oh yeah. She knew, she knew I was going to do it. There was like no question about it. So, so why over Texas soil? soil why it's a concept right of being a native texan and you cannot be a native texan if you weren't born over texas soil that's just how it works so

i think that's so funny yeah if push ever comes to shove they were born over texas soil there's no question about it that is like that is the best look pat, Patrick, I'm Texas. Howdy, y'all. I'm Texas, too. Howdy, y'all. Get a dog, little one. So wait, so it's been like three weeks since the baby was born. Is he home now?

Oh, yeah. He was in the NICU for a little under two weeks. Then we brought him home. So he's been home now for a while. And how's your daughter taking to the new? Oh, she loves him. Really? Oh, yeah.

Oh, that's so sweet. That's what we're hoping for in our house too. Yeah. So speaking of like all of this excitement and chaos and whatever, as I understand, obviously, because my wife and you were both on pretty much the same schedule, you have your own exciting arrival on its way. I do. And so far I've already made it past more weeks pregnant than your poor wife. I'm currently 37 weeks pregnant with a scheduled C-section at 39. So in less than two weeks. And just like you, this will be baby number two for us.

And for us, this is another boy. We have a boy who actually turns three on Sunday. Oh, yeah. And then, yeah, so it'll be almost exactly three years apart. So have you all been like taking a look at minivans and stuff like that? You know, it's funny you should ask because right now we have this like hatchback Hyundai accent that I hate. It's my husband's. I'm like, you've got to sell that. But he loves that stupid car. And then I drive my Tacoma, but my Tacoma is a 2004. So it's pre the larger models.

Right. So instead of doing the practical thing and buying a van, we just bought a bigger Tacoma because I have always wanted a new Tacoma. And I don't know. It was it can hold the family right now. So that's how I justify it. Wow. My husband's like, why don't we get a van or like, you know, a 4Runner even. That's like a Tacoma, but with more room for family.

And I'm like, because I want to be selfish and I love Tacomas. Yeah. Did you guys get another car? No, that's the problem. I have a Jeep. My wife has a car, right? It's a Tesla Model 3. That's actually what we're using as the family car. And let me tell you, it's a great car,

but it's super tight for us. And the Cybertruck was announced several years ago. I know. I haven't seen a single one on the road. I was asking my husband. I'm like, is that even available? Is that even an option? No, it's not available. It was meant to start going into production early in the pandemic. Obviously, the pandemic hit. It threw everything back. Kind of changed things a bit. The latest we've gotten from them is the first half of 2023. And so I pre-ordered that thing the day it announced.

Yeah. Yeah, I put down a deposit on it. You put down a deposit on a Cybertruck? Yeah. That's amazing. You should throw a rock at the window when you get it. Could you try to break this glass, please? Oh, my...

Well, maybe that was a little too hard. And I've been waiting for that thing, and that thing will easily fit the entire family

because it's a full-size truck. I mean, you'll have to tell me when you actually get it and what you think of it. Pickup truck buyers tend to be pretty conservative in what they like. It needs to look like a pickup truck, you know, that kind of thing. And this doesn't look anything like a pickup truck.

Yeah. But it immediately makes pickup trucks look old fashioned. Also, it's been really interesting how having a second kid, it's like with the first kid, we made an entire music video, like announcing the first kid i'm gonna be a mama we had like you know baby showers and like lists of things and buying all of these things and then with the second kid i'm like what literally like yesterday i said oh uh we should probably get some diapers and i bought some pacifiers and that's it. But I mean, it's interesting how having a second kid really did change our vehicle situation.

Like just like what you're talking about, that was a thing that we had to pay attention to. And that was more expensive than all the stuff combined with the first one. But first kid gets a music video. Second kid, basically like a Facebook post-it note. Yeah, same thing for us. Announcing the first one. Yeah. But first kid gets a music video. Second kid, basically like a Facebook post-it note. Yeah, same thing for us. Announcing the second kid. And I probably should say, too, there is a gripe I must express. So obviously I've always looked forward to meeting this cute little guy.

But OK, so a few weeks ago, I was diagnosed with gestational diabetes. And that basically means that I can't eat any sugar. And I can only seldom eat carbs. And I've got to tell you, Ernest, it is the worst. I can imagine. It is the absolute worst. Right, we've talked about my obsession over marshmallow peeps. And then I'm diagnosed literally a a week before Easter and everybody's eating

them but anyway it's like been the worst thing for me in a first world problem sense at least like I mean comparatively like in a global sense not so much but I really am such a whiner about this diet I have to be on because I've never been on a diet before I'm a marathoner I don't need a diet you know like it's yeah no I can imagine what it's like having, you know, your sugar taken away. Especially, it has to be one of those things where, like, your body becomes, like, chemically dependent on it. Yeah, and I definitely was. There's no doubt.

And then when you rip it away. It's like this withdrawal. Yeah, especially all of a sudden. Right before Easter. What the heck? Right. Instead of, like, a gradual, like, hey, we're going to gradually ease you off this addictive substance.

They just, like, drop the hammer on you one day that that has to hurt. Yeah. Oh, and of course, I'm being dramatic about it because I'm dramatic about things sometimes in my expression. But it's just been really interesting because gestational diabetes is it's different than a lot of other kinds of diabetes in some ways. I mean, so I'm still physically active, right, in my third trimester. But because I have gestational diabetes, for whatever reason, pregnancy has decided to cause my body to develop this sort of insulin resistance. So normally, if I eat sugar or carbs, then it goes into the cells, is broken down, turned into energy. But because I now have this resistance, which comes from hormones produced by the placenta

or something, I don't really understand it a hundred percent. Yeah. So now if I eat any sugar or carbs, the sugar just stays in my blood and it's not like absorbed into the cells. It's not converted into energy. And then when my blood is full of sugar, that sugar then passes on to the baby who doesn't have insulin resistance. So the baby's body converts it into tons of fat. And then it ends up making you birth a massive whale baby with an excessive sugar addiction, which I did the first time. That's why I had to have an emergency C-section. It's so funny. I was in labor for like 36 hours and he wasn't coming out. And they're like, oh, well, maybe he's just like a big baby, like eight and a half pounds. No, ten and a half pounds. Oh, yeah. Emergency C-section. He was a whale. Adorable. But he totally looked like a sumo wrestler when he came out. And it was because I had developed gestational diabetes in that pregnancy, but it went undiagnosed. You didn't have to go do that sugar drink thing, that test?

No, I did. They gave me that three-hour sugar drink test. Yeah, that thing. Yeah. Yeah. And when I saw the results, you know, I was Googling it because I do that. And I could tell that, like, even though I technically didn't have it, I was extremely close. Very close, yeah.

Like a doctor could have called it either way, right? But they said, nope, you're good. And then they never tested me again. And that was probably like 25 weeks or something. And so they should have tested me again a few weeks later, but they didn't. This time, same exact thing happened. But since I had had a whale for a baby the first time, I was like, five weeks later, I actually asked for another test.

And the doctor said, oh, I guess we could do another one. And it came back positive. What would have happened if I had not requested that? But just to give you like an idea too, I'm five foot three, five foot four-ish, and I normally weigh 110 pounds. So I mean the first baby and like all this stuff that came with it was basically more than 10% of me, right? Or you could say at church, like he was tithing for me. And I looked like I was going to have twins. So I don't know.

It's so funny that the doctors missed my gestational diabetes because I swear a photographer could have diagnosed me. But you know, whatever. We're all doing the best we can in life. And now he's a healthy three-year-old, still addicted to sugar. But you know, what are you going to do? Yeah, there's nothing you can do. In my wife's case, it was preeclampsia, right?

Right, which is also a common condition. And that's when your blood pressure goes up, and then there's no other way to cure it except to take the baby out. Right, also caused by something in the hormone of the placenta and whatever. So twice she's had it. She had it with our daughter, and that's why she was born two weeks early, and then obviously had it with our son six weeks early. Her blood pressure was running high, and they came back and said, yeah, you've got preeclampsia and it's severe. So we need to take him out. Oh, man.

Today. It's crazy. But it's kind of weird how like modern medicine has made the infant survival rate much better than what it used to be 50, 100 years ago. Oh, incredibly better. But like the increase in the amount of conditions. Has skyrocketed. Has skyrocketed. Has skyrocketed, like you said. So it's almost like the science innately knows that babies are going to need to be born earlier and earlier because of whatever condition is causing this.

And so the science has to advance to the point where it can keep them alive. Like I said, our son was in the NICU and he was born six weeks early but was of full weight and height for his time. How much did he weigh? Can I know? He was almost six weeks early, but was of full weight and height for his time. How much did he weigh? Can I know? He was almost six pounds. Oh. Yeah. But we saw some in there who were born at like two pounds.

Yeah. It's so crazy. One of them had been in there since like December. So the fact that our son got to come home in less than two weeks was great. And I'm so glad it worked out for you. And to me, it's interesting how we have this fantastic technology, especially in the NICU, right, to take care of these babies who are two pounds, three pounds, you know. But then we don't have the medical technology to tell a pregnant woman she's going to birth a 10 and a half pound baby. It's like, did nobody?

Yeah, it's coming. The technology is available now for a lot of this stuff, but it's just not, you know, having to go through the FDA and all that. So, yeah, there's a lot of red tape. I get it. The goal for this baby for me is like, I don't know, eight something pounds, you know, at least not a full size whale. And there's a point to all this TMI about my gestational diabetes, I swear. So in being diagnosed with gestational diabetes, I'm having to do the finger prick thing and then give myself these insulin shots. And, you know, I have to take a look at all the medications that go along with diabetes and really consider what the diabetes is, how medications treat it. And I even sat down with my stepbrother

who has type one diabetes. And I was so fascinated by like how he manages it. And also how like before insulin injections existed in the early 1900s, people born with type 1 diabetes just didn't survive very long. Yeah. Which is, you know, crazy. And I mean, this condition has really opened up my eyes to this world that I hadn't really known much about. And there are a number of medications used to treat diabetes, right? There's insulin, right? Which is considered the best treatment method in gestational diabetes because it doesn't pass through the placenta to the baby, whereas

other medications do. But there are other treatments that are popular in type 1 and type 2 diabetes, including drugs like, I don't know if you've heard of any of these, metformin. My dad takes metformin. Oh, and then I don't know if you've heard of any of these, metformin. My dad takes metformin. Oh, and then I don't know how to say this one. Glimepiride. Yeah, it sounds like it's. It looks like I just spelled that wrong in the script. Glimepiride or something.

I don't know. Glimepiride. And then another drug called canagliflozin, which is sold under the brand name Invokana. Invokana, yeah. I'm slaughtering brand name Invokana. Invokana, yeah. I'm slaughtering these. Invokana. And then there's a bunch of other ones. And in doing a bit of research on the subject,

I actually got to talk to one of the people who developed that very drug, Canagliflozin. Oh, nice. It's an oral treatment. It's a small molecule. That's Steve Merman, our undercover superhero for this episode. I was involved with kind of the original piece when it first came in as a candidate development and I helped build the process that scaled it up to metric tons. And then I changed positions and went in and it was in solids dosage formulation development and developed the process and the formulation and scaled it up.

So I was involved with that one almost end-to-end to make the tablets from the API. But we used a lot of fundamental modeling to design the studies for that, as well as do the scale-up. We did a lot of process analytical technology, in-line measures and making decisions about the process in real time. Also a lot of batch modeling, where we were looking at batch multivariate analysis projections to ensure that we had the right tech transfer in the product all the time. So it's safe to say that Steve uses a lot of high performance computing to do this modeling. Exactly. And this particular diabetes drug was created through Steve's work with Janssen, which is the pharmaceutical part of

Johnson & Johnson, who we've all heard of, because not only do they develop a lot of beneficial medications and products, but they were also one of the three U.S. companies to come up with a COVID-19 vaccine. This morning, Johnson & Johnson out with new data that shows its single dose vaccine is working to provide long lasting protection. Yeah, Johnson & Johnson is a very big company. As a matter of fact, right down the street from me here, they leased a building and it says like Johnson and Johnson Medical or something on there. Maybe it says surgical. I don't remember. And I'm in Silicon Valley, right?

So something to do with tech and medical in that facility. I've never been to it, so I have no idea, but I just, I passed by it on the highway. Ooh, maybe it's top secret. Well, I mean, I found it, so it's clearly not that secret. Touche. And I found it by just driving by on the highway. It wasn't like I was looking for it. There's a sign on the door. Maybe they don't have that many secrets going on. Yeah. Big old sign on the top of the building, Johnson and Johnson Medical or whatever. But it's proof, though, Johnson and Johnson is a very

popular brand. It's very well known in the United States and beyond. My sister actually and her family were recipients of that Johnson & Johnson COVID vaccine. So, I mean, it's hit my family personally in that way. And Steve actually helped develop that vaccine. So it's interesting to see how medications like these can affect our lives so personally. And then for all I know, maybe my stepbrother is even taking canagliflozin. I don't know, you know. Maybe. It's possible. But before we dive into the kind of big compute work that Steve does for Johnson & Johnson, let's learn a little bit more about Steve. I work in Johnson Pharmaceuticals in the biotherapeutics development area. I'm a director of data and analytics. He's been with Johnson & Johnson for 23 years.

I'm also an adjunct professor at Temple School of Pharmacy. I've been teaching there for about seven years. He also has a PhD in organic chemistry, and he's always been passionate about applied data science to generate explicit knowledge. In fact, this is where in my conversation with Steve, we got into a bit of a deeper discussion about explicit knowledge versus tacit knowledge. And I don't know, Ernest, are you savvy with the different knowledge definitions? No, I can make an inference from explicit and tacit, but I don't know exactly what the difference is. So in simplified terms, much more than what's in Steve's brainy head, tacit knowledge, as I understand it, is knowledge gained from personal experience that is more difficult to express. Like for me, this would be knowledge of how to frame the best shot in a film or how to move the camera to complement the tone of a scene.

Right. Like I feel like I just know how to do it based on my experience and the tone I'm trying to set. Whereas with explicit knowledge, that would be knowledge that's more easy to articulate, like something you can write down, you can share, like maybe the answers to trivia questions or like a math equation or something. Or according to bloomfire.com, quote, when data is processed, organized, structured, and interpreted, the result is explicit knowledge. And then there's implicit knowledge, which is the application of explicit knowledge. Yeah, there's a component of distillation and a component of extrapolation to all of this stuff.

If you say so. And as the director of data and analytics, I can see why Steve would gravitate toward explicit knowledge, which really comes into play throughout his career. Explicit knowledge, in my mind, comes down to functional logic. It comes down to a model with an actionable output. And Steve is hoping to push his field toward being as purely explicit as possible. So the knowledge is completely operable. So it's obvious how you need to make decisions, right? And furthermore... My vision is to really enable the world's best operator

to run any process anywhere in the world 24-7, 365 through the use of data and modeling so we can generate that explicit knowledge to manufacture the best products for the patients. I mean, it makes sense, right? If you can get to the level of explicit knowledge, your ability to create something from that is much easier and much more refined or targeted than something where you don't have that level of clarity.

Right, like there's more of a formulaic way of doing something. It's written down. It just makes it more concrete. And when Steve isn't pondering how to generate and record more and more explicit knowledge, he's enjoying the great outdoors through walking, biking, hiking, and paddleboarding. I don't watch a lot of TV. People always ask me, like, do you see this show? Do you binge this? I'm like, no.

Which seems to be a common thread among science and engineering types. I mean, perhaps they can't just sit and let their brains rest long enough to consume slow moving fictional entertainment. I don't know. As opposed to you, Ernest, and your fascination with, how do we put it? High end film. Garbage movies. I saw spiders the size of men raining on hoods and spitting fire out of their mouths.

I don't watch regular TV either. So yeah, neither do we. And for me, it's more of a when I see some of these shows on TV, like if someone else is watching them, like they actively make me angry because it is impossible for me to accept that there are people this stupid in our society. And it actually makes perfect sense why we have so many problems in the world today because we just have such a glut of morons in the world. That's such an optimistic viewpoint.

Your favorite dish. My favorite dish. I like mugs because they're very comfortable in your hand and they hold the hot things that you don't have to touch. What really bugs me is every now and then Netflix comes out with something good. And this happens all the time. Whenever a studio makes something completely brain dead, like a Michael Bay Transformers movie, it explodes. It's so true.

Everyone is watching it. It makes billions of dollars. But then like the really good cerebral stuff is like canned after two weeks because the general audience just doesn't want to see that. Yeah. They can't even process it. Right. This is one of the reasons like pretty much all I watch is YouTube. I just watch documentaries. But high end film or garbage movies or pinnacle cinema, as I call it. Yeah, I love that. I still haven't gotten to see Rubber. I've been trying to find some time. This is what our killer looks like. Tire. In the back of my mind, I think there's something always more valuable that I can be

doing. Maybe he'd change his mind if he watched Lois and Clark. Oh, or Ernest, did you ever watch the series Sherlock with Benedict Cumberbatch? So I saw like one or two episodes maybe, but I didn't like watch the whole series. Who are you? What do you do? What do you think? I'd say private detective. When the police are out of their depth, they consult me. So the series Sherlock is so good, except then it gets to season four and it plummets into a crap hole and dies a thousand deaths.

Come on, be sensible. No, I don't think so. Maybe you'd enjoy season four. I might. But anyway, going back to Steve and what he does participate in, Steve, actually, he dabbles in woodworking. I do quite a bit of cabinetry. I have probably four built-ins I built in my house that are just, you know, super fit for purpose.

So I see the space and I'd be like, what would be the most functional use of that space almost, right? The coolest. And then I draw it up and get approval from my wife. Then I go on a mission to build it. And you can kind of see that logical mind and explicit knowledge at work in just how he even describes how he builds cabinets. I mean, he looks at a space and then he envisions the most functional use of that space. But anyway, so Steve works in the pharmaceutical industry,

and I've always been curious about, like for instance, COVID vaccine development. So we've talked to some COVID scientists in the past on this podcast who have used high-performance computing to basically do a lot of amazing things and contribute to some critical breakthroughs. But I hadn't yet spoken to somebody who had a direct hand in developing

one of the COVID vaccines until I spoke to Steve. And Steve said that he first learned about COVID-19 toward the end of 2019, probably before most of us had ever really heard about it. China has identified the cause of the mysterious pneumonia outbreak in Wuhan City, and it's from the same family that caused the deadly SARS epidemic 17 years ago. It's a new type of coronavirus. I was reading a little bit of global news and seeing some things and you're like, you know, you got to pay attention. You don't know what's going to happen to it, but I was thinking it might be something that would just kind of come up and go away. After all, new viruses

had come and gone a number of times over the years, with some affecting the United States more than others. I kind of remember the moment of realizing, you years, with some affecting the United States more than others. I kind of remember the moment of realizing, you know, hey, something's going on, right? But there's been a lot of something going on for a while, like MERS, SARS, right? And you're just like, it was regionally contained. So is this another piece of that, right?

You didn't know. But then as 2020 started, weeks went by, then a few months, and suddenly the virus was here on our doorstep, and everything was locking down. A SARS-like virus which has infected hundreds in China has now reached the United States. Today the World Health Organization officially announced that this is a global pandemic. The breaking news, stay at home, that is the order tonight from four state governors as the coronavirus pandemic spreads. It was surreal for me. I was actually on a golf trip

with one of my buddies and we were hanging out at the clubhouse after a round. You know, the news was, man, in about midweek, it got crazy. In March of that year, it was like a Hollywood script, sitting there watching the TV, the news, the maps, right? And soon life had veered onto a path that no one was familiar with. It was uneasy. It was one of those pieces you always like to be able to think that you're going to know what you need to do. But I had no predictive model at that point in my head around what's next. And then there was his job. Remember, Steve worked on the research and development team at Johnson or Johnson and Johnson, a company and a team whose mission is to, quote, transform individual lives and

fundamentally change the way diseases are managed, interpreted and prevented. Close quote. Transform lives with things like vaccines. The team that I have just we support the projects through kind of that data systems and, you know, the analytics, the modeling pieces, right? So we support all the projects, all the different modalities. So I had a feeling we were going to get involved somehow, right, because we have so many touch points. And now that there was a new virus on their doorstep, threatening not just the lives of people across the globe, but those of their neighbors, their friends and their families, it was really personal. All the people that work in the pharmaceutical industry are people, and we have families.

And we all suffer from the same horrible diseases that everybody does. So we have the vested interest, but we also have the passion and the skill sets by design to help ourselves and everyone. So we're in this together. And so the company stepped into action. It was time to do anything they could to do what hadn't been done before, create a safe, life-saving vaccine in record time. It really came out of some strong leadership that we had and passion for giving back, right? Creating that product for unmet need. J&J has always been philanthropic in this area. So I think it was just the right time and right opportunity. So leadership was, we're absolutely going to do this, you know, all hands on deck. And I think with combining that leadership drive with,

you know, the fantastic domain expertise, passionate scientists, yeah, it's a great combination to get stuff done. A lot of the early work on the vaccine was done on a small team from Steve's Pennsylvania location, while also collaborating with a talented group of people in the Netherlands. After someone recommended Steve as this great resource to bring on board the project because he had this extensive knowledge and experience with computational modeling. I was in a meeting shortly after that and was basically recruiting a few people from my team. And we were embedded into several of the development workflows at that point to help really brainstorm even the potential for modeling. How can it help? How quickly can we get this done? And kind of triage the modeling

opportunities to accelerate everything from designing the experiments to scaling up to tech transfer to how fast can we run this plant theoretically. So we were answering a lot of really cool questions. While safety is always number one at Janssen, a critical factor in the development of this particular vaccine would be speed. I mean, the virus was already here and spreading faster than a grass fire, and the world needed a vaccine right then. But in the past, vaccines took an average of, get this, 10 to 12 years to develop. I mean, before this point in time, the fastest developed vaccine of anything took around four and a half years, and that was with a great amount of historical data already

known and organized beforehand. But the truth of the matter was, I mean, with COVID, they didn't have that kind of time. Safety is number one. And what risks can you take? Where do we think that we can take the lowest risk per se, right? And the fastest time that's going to accelerate it. So that's really what it came down to. So we leveraged a lot of the domain knowledge and really pushed the envelope on. If we had to accelerate this by six weeks, could we do it? Some things you can, some you can't. You just had to design better studies, do them as quick as you could, learn fast, and just run the next set of studies so you can de-risk as quickly as possible. So data played a huge role in that. Modeling and simulation played a huge role in that. Modeling, simulation, and high-performance computing.

We did a lot of computational fluid dynamic in the rescale environment to help accelerate that and design the studies. And Steve's approach is to simulate, then run. I want to generate the best knowledge, design the best set of experiments before I physically go in and run them. And before I go deeper into the COVID vaccine process, I do want to talk a little bit about how vaccines and other medications are made. Typically, the process starts with a discovery phase. At Janssen, there is a physical team dedicated to discovery. And this is the team that searches high and low for a potential core ingredient, otherwise known as an API or an active pharmaceutical ingredient,

which is the ingredient in a drug that produces the intended effects. otherwise known as an API or an active pharmaceutical ingredient, which is the ingredient in a drug that produces the intended effects. Discovery's job is really identify that target and discover that modality to affect that target to create a positive clinical outcome, i.e. help people. In fact, drugs are typically made up of two main components. So that API as one of them, the main ingredient that's going to generate the immune response or maybe tackle some bug or whatever. And then the excipient, which is the non-drug substance that helps deliver the medication to where it needs to go in the human body. So excipients are typically not chemically active. They can be lactose or mineral oil or something, but it's that active pharmaceutical ingredient, that API, that really

counts. They've got, you know, different ways of testing that receptor that they find or that target and then figuring out if they can tune it in a way that's going to help everyone. But APIs can be difficult to discover. In fact, Ernest, do you remember at the end of 2020 when we spoke to Jerome Baudry about using high performance computing to do this thing. Right. His team was the one looking at natural substances that could potentially be used to help fight COVID. Yeah. So in the episode, The Power of Plants to Pulverize Coronavirus, we spoke to a researcher who was heading up this very discovery process in hopes of finding, like you said, natural ingredients in substances like plants that could eventually become APIs in COVID therapeutics. And he was using an HPE Cray supercomputer to do it. He, through simulation, was able to run models to calculate how 200,000 different chemical compounds would potentially react with COVID-19. But he was quickly able to narrow down the 200,000

compounds down to just 125 that showed promise. And that was all done, you know, with computers. And from there, the 125 compounds were to be physically tested in a lab and then narrowed down to probably five or 10. In fact, I'm curious if any of those compounds actually panned out for Jerome. I really need to email him. I couldn't find anything online. Anyway, so this is an example of the drug discovery phase, right? In many instances, drugs are discovered slowly without the aid of high performance computing. And we talk about that in the episode two. But in Jerome's case, the chemical compounds were narrowed down using high performance computing, shaving years and millions of dollars off of traditional discovery processes.

Right. And this is the exact same thing we see repeated throughout every industry. Right. Things like Boom Supersonic running tons of simulations to get to a prototype that they think will function in the way they want rather than building thousands or millions of prototypes at millions of dollars. Doing wind tunnels hundreds of times over. Yeah, this is the same thing that's happening across all the industries, right? It's not telling you exactly what the right thing is. It's helping you eliminate tons and tons of what the wrong thing would be. Exactly. From supersonic jets to personalized medicine, industry leaders are bringing incredible innovations to market with unprecedented speed and efficiency by using

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Go to aka.ms.hpc.vestas to read more. So this discovery process, so whether it's done using high performance computing or physical experimentation or maybe some combination of both, it's part of creating every drug and vaccine. Right. And at Janssen, they use computational simulation and modeling during the discovery process as much as possible. And after a target API is discovered, it's then passed to the development organization of which Steve is a part of at Janssen. You know, when they say, hey, you know what? Here's kind of it. This is what it looks like. And they've done some initial work on it to kind of characterize it. So at least we have something we can be like, OK, we just need to make that. Right. Sure. So then it comes into development.

And the job in development really is to build the best process at first launch. So now that the active pharmaceutical ingredient has been identified, Steve's development team is tasked with figuring out how to get that drug to interact with the human cells the way it's supposed to. So like how much of the API needs to be combined with what amount of excipient ingredient to be the most effective, whether that's in like a pill or a fluid or whatever. So really our job is to take that identified modality and make it for human ingestion and prepare it for commercial manufacturing. And they use high-performance computing to run models to figure out what path will be the most likely to succeed,

like you were talking about, you know, eliminating the wrong paths, right? So how to make that drug the best it can be at the highest quality in the shortest amount of time possible. And they also need to figure out how to physically make a lot of the API. The discovery team may have identified that critical ingredient, but unless they have a way to duplicate it, there obviously just won't be enough to go around. So how do we do that?

Well, we leverage host cells for the most part. Why try to make machines or something do the duplicating work when cells can do it for us? What we do is we codify these cells so that they can produce that protein for us. Because the cells, they're great. They're the best organic chemists, right? They can put together the fantastic construct of these proteins. So we let the machinery of that cell make the product for us. So what we need to do is understand how to make that cell make our product. And then we need to make lots of those cells making a lot of product.

In other words, the research and development team has to find the cells that will make the highest quality API in the best way possible. And then they have to physically duplicate those cells so that they have this sort of army of cells that can all make a ton of product or that important ingredient at the same time. We have a cell bank. So that stores those codified cells, right? So we go through this piece around cell line development, finding the best cell that we transfected with that codification to make our product. And then we screen through those and we find the clone, that's monoclonal, that makes that product the best. And once they find the ideal cell,

they grow thousands and then millions of these cells. That's our starting material, right? That's the machinery. That's the chef that's going to make that product for us. Yeah, this is kind of the inevitable path of where I see humanity going in general, which is we are bioelectrical machines ourselves. We use electrical machines outside of us, computers, to do all of this computation, high performance computing and everything. But then we go back and use the biomechanical or bioelectrical machine to generate these type of things. So inevitably, I think what's going to happen is there's going to be a fusion at some point of what we would consider traditional computing electronics into humans, cyborgs, essentially. And biology. Mind equals blown.

Oh, and I should mention that while computational modeling comes into play a little bit during this part of the process, it is mostly done physically at this point. Not quite in a mix of physical human tissue and robot put together like you're talking about. But that day's coming, man. It's coming. It's close. But once the project development team grows and expands these cells. Then we inoculate them in a bioreactor, so a vessel, right? Typically we start 250 mils or 5 liters in the development area.

You know, you brush your teeth, you comb your hair, and you inoculate cell banks in a bioreactor. All in a day's work. Exactly. I mean, for those who work in non-chemistry-based industries, a bioreactor is a closed container that is used to produce various chemicals and biological reactions. And they come in small sizes that can sit on a desk.

In fact, I even looked on Amazon and you can buy a three liter bioreactor on Amazon. So there you go, everybody. Jump on your app. They can also be huge vats that are lining the inside of a warehouse, and they can be glass or metal. And they usually have a bunch of tubes and stuff connected to the top that makes them look very scientifically labby. I mean, how's that for a superb scientific description? Excellent. And nowadays, you can pretty much get anything on Amazon.

I would be surprised if you couldn't buy, for that matter, a nuclear reactor on Amazon today. Yikes! I didn't check for nuclear. I didn't either, but I'm just saying, you know. Amazon. So once the development team has duplications of the best cells, they put those millions of cells into a bioreactor. What we want to do is create the best environment for those cells to produce our product.

So then we go into a lot of engineering and modeling and science to understand, you know, how do we feed these? What do we feed these with? Sensitivities that those cells might have. So that's where computational fluid dynamics come in. That's what comes. All the omics, the intracellular omics, right, to understand how that cell is going to behave and how it's the happiest. We need to make sure that, make sure that there's enough oxygen around it to keep it healthy and happy. There's enough nutrients around it to keep it happy and healthy. So that's where all that complex modeling really comes to play as part of the process development piece for the bioreactor. In other words, Steve's team uses computational modeling via fluid dynamics to determine how to treat the cells in the bioreactor so the cells succeed at creating that critical drug ingredient in the best form and timeline possible.

And once they get the answers they're most confident in, they then start running the physical tests based on their modeling. So it's a cooperation of computational simulation and physical testing. Exactly. First simulate, then run the physical tests. We simulate up front. And i.e. simulation in my mind up front is, I don't know if you've heard of this old adage, right? It's a day in the library is worth, what, two weeks in the laboratory, right? You can go to the library and help you design better experiments so that when you go into the lab and execute them, you don't waste time trying to figure out stuff that people already

knew. Then after the cells have created that key ingredient in the bioreactor, the R&D team harvests the drug and then uses more modeling and engineering to figure out how to best clean, purify, and concentrate the product before passing it on to the next team in phase, which is drug development, where they figure out and develop the best physical way to deliver that medication to a patient. So like, what kind of pill or solution and so forth. So this is the way that they find a stabilized, solubilized and deliver it to the patient. And rather than just pass the baton to the product development team,

Steve's team continues to work with them using computational fluid dynamics via Rescale to best figure out how to proceed. There's some really nice models that they have been using a lot of the HPC to look at simulating, you know, how do we stabilize this, right? Or even solubility, pushing the envelope of solubility. And throughout all of this, from discovery to product development, analytical development is helping along the way. They're the ones that are developing the methods

so that we can release the material in the future, understand the variance that we have in it, or what are some of the sensitivities that we can release the material in the future, understand the variants that we have in it, or what are some of the sensitivities that we have, right, to stability, to process conditions that can affect the quality of the product. So there's a lot of things that are very subtle and large molecule that require extensive analytical development. Once the physical drug or vaccine is developed, it moves into the clinical testing phase where it's tested in humans.

And when it makes it through that phase, it's put on the market and hopefully starts really helping the population. It's complicated because the human body is complicated, right? So complicated, in fact, that around 90 percent of drug candidates fail in U.S. clinical trials, which is one of the reasons it typically takes 10 to 15 years and around a billion dollars to develop just one simple drug, or at least it has in the past. I mean, belief is that by continuing to utilize high-performance computing wherever possible in this process, the timeline will and has in many cases shortened. So that's a great area that computational work is going on right now that's going to make a huge difference for the EIML

and understanding, you know, targeting and learning from the clinical. So that's the drug development process in a nutshell. And in the specific case of COVID-19 and the Johnson & Johnson vaccine, this general drug development process was basically followed just under a tighter timeline with a few differences. For instance, because the virus was so new, new data were being discovered and released every day, but there wasn't a lot of historical data available to allow them to crunch large data sets and know exactly what path to take right at first.

And high-performance computing really came into play when trying to figure out how the vaccine would interact with the human cells. We did use it a lot for that computational fluid dynamic piece to really understand shear sensitivity, kind of the optimal gassing strategies that we would have so that those cells that are making that product for it are healthy, so they can make the most product the fastest. Unlike the Pfizer and Moderna vaccines, which use mRNA to carry the code to make the coronavirus spike protein,

which allows the coronavirus to invade human cells, the Johnson & Johnson vaccine uses DNA. The Johnson & Johnson vaccine also uses a modified weakened cold virus to gain entry into human cells. This then triggers an immune response, teaching the body to fight off the real coronavirus. And throughout this process, with COVID-19 raging across the globe, Steve found that there was a unique spirit of cooperation and understanding on all levels, including with federal agencies, which is pretty unique. Everyone understood the urgency of the situation, that a vaccine was needed and needed immediately. And suddenly, rigid old processes were re-examined in favor of new. The agencies were willing to work with us, right? For the kind of first time, they're willing to really, you know, move from what's been written

in stone for how we develop medicines, right? They really understood the need. So we really went down and focused on the core science and not the process, right? Not the red tape. Why are we studying this when we know it has no risk, but we still have to file the paperwork type of thing, right? So I think there's some pieces that we found some balance around. Where can we accelerate, but still not have risk to the patient because safety is always number one.

But that was a huge evolution. And I hope that that's going to really help us accelerate into the future as well. So the rigid old processes, they weren't just ditched because somebody decided they were rigid and old. It was because they were now going to accept the risk of sidelining those because of the circumstances that this needed to happen. And I would not infer from this that this is going to be the norm going forward for normal drug research. Maybe not, but it sounds like to me that a lot of the rigid old processes and a lot of the red tape were based on old procedures and old way of doing things before technology had evolved. So there's probably room to strike down some of that red tape and get rid of it.

And this is my guess that maybe going through the COVID-19 vaccination approval process and cutting some of that tape probably exposed some of those areas where it actually does make sense to make some cuts without actually risking the safety of others. And for eight months, Steve found himself pounding hard on the COVID vaccine project. We were kind of having like agile scrum approach that, okay, what's the latest greatest that happened overnight? You know, these experiments were run. Here we're at. Here's what we're going to do. Here's the plan. Here's what reactor we're going to be in. What can we do? Answering those questions as quickly as we could. And it helped since Janssen

had developed vaccines before, they already had a pretty good idea of how to go about a vaccine development process for COVID, just with a new element involved, that being the COVID virus itself. So it's just a piece of like codifying that part of that vaccine to recognize the COVID. So that's a piece of it. So it's a little bit of just almost engineering at that point, right? One of the things I was super excited about was just to see how fast we could turn around, you know, getting that sequence of that COVID variant and turn that into an applicable target. That was unreal, unprecedented from the discovery side and then, you know, pushing into the pipeline. We start with good news this hour. Minutes ago, Johnson & Johnson released results from its phase three trial of a COVID-19 vaccine, which could be approved for emergency use

as soon as next week. It showed 85% protection against severe cases of the disease, although the overall effective rate is lower. Some advantages, it is a single dose and it can be stored in a regular refrigerator. The vaccine was tested in more than 44,000 people in the U.S., Latin America and South Africa. If authorized, it would be a much needed third vaccine available in the U.S. to meet the demand that outpaces current supply. By the end of 2021, more than 16 million Americans had gotten the Johnson and Johnson vaccine, the very vaccine Steve had been working on for months. And once the vaccine was out, Steve was happy to have fulfilled what he considered to be his duty before moving on to the next life-helping project. It's great that we could help, you know, the way that we did,

jump in. But that's why we're here, right? I'm not one to, you know, toot my horn or whatever. So it's your actions, what you deliver is the value back. So I thought it's just as much helping myself and my family as it is to help everybody else. As far as HPC infrastructure is concerned, for control systems that connect directly to the physical reactors and specific types of equipment, Janssen taps into an on-prem system and then for all of their modeling work, like all of their data sources, those are run on the cloud through Rescale.

One of the things for Rescale for me was bringing MATLAB licenses out there. So that's another thing because I think Rescale's kind of been that platform that's been almost host agnostic, which is great. And we have a lot of need for a diverse global team to have access to tools. That's one of the pieces of that concept of having this model factory where we have, you know, inception of model and then we build it into a product, right, and then realize value.

But one of the first things you have to do is reduce the barrier to get tools in the hands of the people that can do modeling, right? So that's where Rescale has been great. Let us host the licenses up there so that I could deploy those to, deploy those to anybody in the world, anywhere, anytime. And Steve says they don't really need an enormous amount of high-performance computing to run their MATLAB workloads currently. But in the future, like I said, when we need that timeliness, we're going to have to run it even faster. Five minutes might not be good enough. You know, 30 minutes. We can't let it sit there and go for four hours, right? If we can spin up a machine that can answer that question in two minutes,

it's worth buying that time to get that calculation done quickly so that we can react to that state of the process before it's passed. And when you have access to virtually unlimited compute on the cloud, you don't have to rely on expanding and maintaining large and limiting data centers in order to scale up in that way. And I don't think anyone will deny that computational simulation is speeding up the drug discovery process, as it is with the process of developing, you know, state-of-the-art airplanes and fuel-efficient cars and a long list of other products and discoveries. Yeah, absolutely. There's no denying that computational simulation is rapidly evolving so many different areas of our day-to-day lives. And the COVID-19 vaccine development process, too, was faster, much in thanks to computers. I mean, we already know by talking to other scientists before this episode that high-performance

computing played a great role in modeling what the virus looked like and how it moved and acted and why it was so contagious. Right. And then that information was shared with the global scientific community. And then companies like Janssen set out on a mission to develop the vaccine. Yes, it very closely aligns with a previous episode. We had talked about how when modeling tsunamis, they had to use a computational model that simulated an underwater landslide. Yes. And then that was fed as input into the model that showed the tsunami. So it was kind of like one computational model feeding.

Into another. And as parameters into the next one. Totally. And Steve, who works extensively in computational modeling, thinks there's a lot further to go in the realm of digital maturity, which, if you're unfamiliar, simply stated, digital maturity is apparently the ability of an organization to respond and take advantage of technological developments like computational simulation, cloud computing, just to name a couple. We're still going through this digitalization maturity. It's just something that's not native to the culture. We've still got a little bit of, I think, paper based processes in place and acceptance of that's just the way that it is. We need to get away from and have better expectations. So we're slowly going through that. And that's a culture piece too. But once we get through that and we have the data available, then it's going to become the exciting piece, right? We can start making decisions faster, which means that, you know, it's not three days later.

Maybe we can do it, you know, within an hour of running something. So we're only an hour behind. For me, that's like at line. So then we're pretty close, but not quite there yet. And then for me, like in line would be like instantaneous. So if you take a look at like the computational power that you're going to need to go from offline to atline to inline, I'm going to be really interested to see what it's going to take. Because if you're generating high density of data, you're going to need very high computational

capability to return that result in 10 seconds versus 10 minutes or an hour, right? So it's going to be an enormous game changer for us once this data starts flowing and we mature our ability to elicit explicit knowledge from that data. Which just makes me think about how much more important access to enormous amounts of compute through the cloud will be when the pharmaceutical industry in particular has hit that level of digital maturity. Yes, and I think this is across the board, right? As every large company out there reaches that level of digital maturity, you're going to see all kinds of things happening. Improvements at every level from material science to just general operation of these random things.

There's a lot more to go in terms of how much more efficient and how much better we can make things with this type of compute power. Exactly. And while technology, I mean, may be advancing quickly, Steve believes that there's a cultural element and a human component that needs to continue to develop as well in order to really take advantage of that advancing technology. So there's an element to, you know, how do you make a decision from a model, which is a pretty big skill gap in my mind right now, and the ability to believe it and take action. So we have to propagate that not only through, you know, the core scientists and engineers at the bench, that we'd be changing kind of the way that they think about executing, not just go in like, oh, I did this 10 years ago this way. This is the way I'm going to do it now. Right. Like simulate, then run. Right.

And then also propagating that through leadership that it's worth trying to evolve to that state. And going back to tacit versus explicit knowledge, Steve emphasizes the importance of computers granting us more explicit knowledge, right? More ability to learn and advance forward in drug discovery. When we were paper-based, you never learned from anybody else's projects, right? You're not going to go read through their paper notebook, right? Even if you did, how are you going to remember and consume that? Without simulation, without modeling, there's really limited availability to learn from previous projects, current projects. And Steve also emphasized that while having access to large amounts of compute power is important today, the need is going to skyrocket once personalized

medicine becomes a reality. Personalized medicine is going to bring some significant challenges to the development process. You're going to have, you know, a therapeutic area. Then discovery is going to find that target. And then they're going to define a modality to affect that target. And that target and modality relationship in personalized medicine is going to go from one to many, right? So there's going to be populations of patients where, you know, the target's going to be the same, but how they react to that target is going to be tuned by that modality. So maybe there's going to be five different subtypes of, you know, human population that we want to target. So then that's going to be five different therapeutic development targets that we need to develop,

right? Maybe we're going after the same biological target, but then there'd be five modalities. So we just increased our pipeline by five for the same clinical outcome, but we're personalizing it into those buckets of the people so that we know that it'll affect them better. So now we've got five times. That's just one example. It may be more, it may be less, but we're not going to be able to handle that with all the physical experiments we're going to need to run. So we're going to have to rely on more simulation, more learning across, you know, those different five development candidates.

Yeah. And actually, let me tie this back to another previous episode we had where we talked a little bit about Mars exploration and a couple other things. The same thing is actually happening right now with the next generation of rovers that are being designed. Historically, we did all of the heavy lifting and the computation on Earth, and we're uploading things to these rovers. However, the next generations, plural, of those that are coming, NASA is working on being able to embed things like neural engines and HPC capability onto the rover itself. Yes. Obviously not at the scale of like, you know, what we're talking about here with- Like a data center, like strapped to the back of a rover. Like a data center.

Right. But enough to where the rover can, for example, take a 360 degree photo or video of its environment and have onboard computation to determine what is the optimal path for it to get from one place to another. Whereas before it would take that information, that picture, that photo, send it to Earth. We would crunch it on our side, determine what that path is. Oh, the latency would be unideal. Send it back.

It could be 24, 48 hours before the thing was even able to move. And then you ran into the situation of what happens if it moves, you know, let's say 15 feet in the direction you gave it. And there's some kind of significant change in the terrain because of either, you know, wind or just natural movement of things. Now you have to reanalyze again. So this is kind of going to the personalized medicine thing, being able to have HPC on a micro level, as opposed to a macro level will open all kinds of doors in terms of science and technology for us in the future. Oh, that's so fascinating. And computational simulation and modeling in the case of drug development has come into play in much of the development that Steve and his team has done.

Not just with COVID, but with drugs like the diabetes drug we mentioned earlier, canagliflozin, as well as the drug called Remicade, I think is how you say it, which is a prescription for adults living with moderately to severely active Crohn's disease. And as our listeners probably know, or many of them might, Crohn's disease is a type of inflammatory bowel disease with no cure that is just like not fun when left untreated. So Remicade can be helpful to patients in those situations. Been involved with that one for a number of years. He also worked on the drug called Darzalex. There's a lot of heroic efforts internally for that one. Darzalex is used to treat a type of blood cancer called multiple myeloma, where plasma cells basically grow out of control, which can lead to low blood counts, bone problems, infections, kidney problems. And it comes with a five-year survival rate of just 55 percent.

That's another one that was fast-tracked, and rightfully so. It's a fantastic treatment that went through our department. Used a lot of the skills in our team to help with the data, help with analysis, scale up tech transfers. So we did a lot of work on that in short term. Specifically, Darzalex is a targeted monoclonal antibody that helps slow or even stop the progression of multiple myeloma by attaching itself to multiple myeloma cells in the body and then either directly kills those myeloma cells or allows the immune system to destroy them. And as someone who actually recently lost a loved one to cancer, I say anyone involved in cancer treatments is undoubtedly nothing less than a hero. Absolutely. And cancer is another area where I think we are...

We are on the cusp. We're 50 years or less away from from not that there won't be cancer, but that cancer will be able to be completely eradicated easily. I think everyone listening to this is just praying for that day. Yeah. And so hopefully I see it before I die because likely I'm going to die of cancer. But, you know. I know likely we all are. Who knows at this point, right? Who knows? Yeah.

And as I was wrapping up my conversation actually with Steve, he did want to emphasize that science and engineering is a field really worth being in. I think it's so fundamental and it's a core piece of the innovation of this country and the world. We just need to have more people understand that the opportunities are endless. And it's at that edge of like, you know, creativity. You can make yourself whatever you want to make in the future. So go out there, find out what you need to do and be the best at it you can be. And don't be afraid of science and engineering. It's not putting you in a pigeonhole. So go out there, get a vision, right? And then find a way to make that vision happen,

regardless of what you do. But start with having a vision. get a vision, right? And then find a way to make that vision happen, regardless of what you do. But start with having a vision. Without a vision, then you're not driving towards something that you own. Because that's how these vaccines and life-changing medications begin, with a vision. And I should say before we wrap this up, I know there are people out there who gripe about the pharmaceutical industry in one way or another, whether due to maybe costs of certain medications or maybe some don't work exactly as hoped on some portion of the population and whatnot. But I will say these scientists and these engineers working behind the scenes, they're people just like you and me with families just like you and me, with families just like you and me. And I believe, for the most part, that they sincerely want to develop treatments to help all of us.

We're not only, you know, pharmaceutical employees, we're patients. And despite the imperfections of the industry, as with any industry, I'm grateful for those undercover superheroes who are responsible for the many, many breakthroughs that have not only helped make life better, but I mean, have sustained it for many. And I think that Steve and his team falls into that category deserving of that gratitude. I would agree. There's many areas where there's always a balance, right? Like, yes, there's always some kind of negative aspect to everything. And if you're looking for it, you can find it. But like you said, I think on the whole, scientists and engineers are trying to better society and better

humanity. Right. And OK, let's bring it back to my gestational diabetes because I didn't talk about it enough. Right. So I didn't have treatments for my first pregnancy. Right. As we discussed. And the baby ended up being 10 and a half pounds, causing 36 hours of labor until finally forcing an emergency C-section, time in the NICU, and then a longer than normal recovery time actually for me, among other issues. But this time around, with treatments developed by blessed scientists and engineers, I give myself a few shots a day combined with monitoring my diet, and hopefully it will result in a healthier baby and an overall better experience. So I guess I'll keep you posted.

But of course, if this baby is more than 10 pounds again, I'm going to be really miffed that I stayed away from marshmallow peeps all this time for nothing. Yeah, I mean, that would be the greatest catastrophe, right? Not having the peeps. Hey, it is a sacrifice, Ernest. There's almost nothing we can't do. Just need to be creative and build it, build skills. I love the order like in Star Trek, like my personalized medicine via voice command, right? That's accelerated development in my mind. So shoot for the stars.

That final quote's for you, Ernest. As a welcome back from paternity leave, a fellow Star Trek enthusiast, you're welcome. Yeah, we're kind of a, some would say an annoying bunch. Who would say that? Who? There's nothing wrong with me. Maybe there's something wrong with the universe.

To learn more about Steve Merriman and his work, you can find him on LinkedIn. You can also learn more about Janssen by visiting Janssen.com spelled J-A-N-S-S-E-N.com. And you can learn more about parent company Johnson & Johnson by visiting J&J.com, where you can read articles and watch videos about the company's COVID-19 vaccine. Right, and I would like to point out that Steve is a merman as opposed to a mermaid. I just thank the Lord she didn't live to see her son as a mermaid. Merman. Merman! We're mad. You can also visit bigcompute.org to see pictures, videos, and a long list of articles that go into more depth on all the things we've discussed today.

And you can support us by leaving a rating or review or better yet, telling a friend about us. And that would be on Apple Podcasts. You can also leave a review and a rating on Spotify now. That's right. You could do Spotify. Well, actually, just the rating. I don't think they allow reviews yet. And with that, the lawnmower has officially kicked on. Oh, awesome. Over here, which is like totally good timing.

So I can handle a lawnmower at the very end. Here we go. Don't forget, you can tweet both Jolie and I. We're open to suggestions of all kinds and don't forget to use multi-factor authentication and practice 3-2-1 backup. Thanks for joining us today and stay safe out there. And don't forget to mow your lawns. I was thinking I was like man God has answered my prayers. I'm in my closet. But whenever the landscapers come, it is just, I cannot record. But they came at the very end!

Yay! I consider that a win. Yep. Thank you.