BioRevolution: Hacking the Code of Life

Hi everyone. Thank you for joining today's office hours on the Bio Revolution: Hacking the Code of Life, where you will hear from WisdomTree's Chris Gannatti, Global Head of Research, who will host Jamie Metzl, Founder and Chair at OneShared.World.

Chris Gannatti:

One of the first things I was thinking, Jamie, was just in case anyone in the audience they may not have seen you on various podcasts or other formats. They may not have yet read Hacking Darwin. Your very nice book on this topic. If you wanted to maybe introduce a bit of your background for the audience, that could be interesting.

Jamie Metzl:

Sure. Thanks so much, Chris. And thank you, Irene, and hello to everybody. I can't see you, but I know you're there, and I feel your presence. So yes, Chris mentioned I'm the author of the book Hacking Darwin: Genetic Engineering and the Future of Humanity. I just finished my service as a member of the World Health Organization expert advisory committee on human genome editing and have been deeply involved in this world of genetics and biotechnology for many, many years on the Scientific Advisory Board for a number of different biotechnology companies.

Background, I have an educational background undergraduate from Brown, Ph.D. from Oxford, and a law degree from Harvard Law School. And as Irene mentioned, I'm founder and chair of the global social movement OneShared.World, which is based around the idea that technologies like the ones that we're going to be talking about today have such profound implications not just for our economies but for the world around us, that we need to make sure that we have values based frameworks that can help us maximize the benefits and minimize any associated harms connected with these and all revolutionary technologies like AI.

Chris Gannatti:

So in our preparation for the session, we kind of laid out some of the bigger topics in the space. And I figured it could make sense to start there. I know just recently, for example, in the United States, the newer vaccines for the specific Omicron variant have become available, but I think it's a mistake because the vaccines get so much attention. We might think mRNA is only about vaccines and maybe only about COVID-19 vaccines. And Jamie, I know you know a lot about this topic and that really the vaccines that we're seeing in recent years, it might just be the beginning, isn't that right?

Jamie Metzl:

Absolutely. So forgive me the audience if I'm going to go back a little bit to just give the background on these mRNA vaccines, but I think they're a very important metaphor. I mean, they're real things, they work, but they're also a metaphor for the broader themes of what we're talking about today, unlike the polio vaccine, for example, that first used in the 1950s, that those vaccines were either dead or weakened versions of the polio virus. And there were extremely helpful, but there were a number of people, it's a very small number of people who were harmed by those vaccines, and the mRNA vaccines are just a very different way of thinking about how immunity happens. With the polio, our bodies recognize these viral invaders and mounted an immune response.

With the mRNA vaccines, what we're essentially doing is hacking our biology because rather than exposing our cells to the full virus as with polio, what we are doing is synthetically generating a set of RNA instructions and cell biology. Our genome is inside the nucleus of our cells, which is the egg yolk, and it passes instructions using messenger RNA to the ribosomes, which are in the cytoplasm, which is the egg white. And those are the protein producers of our bodies, and proteins are the building blocks of all of life.

So what we're doing at the mRNA vaccines is hacking that process by giving an alternate set of instructions to our cells to manufacture something that our bodies are not originally designed to manufacture. In this case, that is an equivalent of these effects in exact fact, similarly of the spike protein on that surrounds the SARS-CoV-2 virus.

Then our bodies recognize this foreign object that we ourselves have created, and that inspires them to mount their immunological response. So this is many decades of incredible science has made this possible. We had to understand what RNA was. We had to understand mRNA. We had to develop the tools to synthesize mRNA. We had to develop the ability to wrap these mRNAs in little balls of fat, these lipids. And then, we had to genetically alter the messenger RNAs so that they would not be seen and attacked by our immune system. So it's really just incredible.

Today, I am actually going just after this call to get my booster. And as I think many of you know, at least in the United States, the new mRNA boosters are out. And this is even more incredible because as the virus variated, as viruses do, there was this new Omicron variant that emerged around Thanksgiving of last year.

Earlier this year, the FDA authorized a process for Moderna and Pfizer to develop a booster that would specifically target Omicron and the first variant, which was known as BA.1. So they did that. There were human trials. It was determined that it was safe and effective. And just when the machinery was being put in place to distribute these boosters, which had already been produced, these new variants, the BA.4 and BA.5 emerged, and pretty much at least here in the United States and in many parts of the world completely displaced the BA.1 variant.

And so, the decision was made by the FDA to not use the variant that had been tested in human trials but to use instead to target the variant of the BA.4 and BA.5 based only on laboratory tests and not on human trials.

And so this is a demonstration of how far our AI systems have gone in being able to predict what would be an effective way to target this virus. And I believe our level of comfort with moving forward now that these vaccines and the mRNA delivery system more broadly has normalized.

So, Chris, your question was about, well, what are the other targets? And really, pretty much any type of certainly a viral infection, a bacteriological infection, is potentially targetable. Anything that our body has some kind of innate ability to fight, we can essentially use these mRNA platforms to begin training our bodies to do a better job. And so, to do that, we need to say, "Well, what is the thing that we're protecting against?" And whether it's Zika virus or malaria or tuberculosis or cancer, or any of those things, and then analyze, well, what is it that's specific about those attackers?

And then to say, "Well, what would need to be triggered in our immune system?" So it's not just the same with the SARS-CoV-2 virus. We're not introducing a whole virus, which could be dangerous. We're just inserting... We're just having our bodies produce a little piece of something that mounts, forces that inspires, I should say, our body to mount an immunological response. And then we are able to have better protection.

So, as a matter of fact, even before the COVID-19 outbreak, Moderna was focusing most of its effort on a personalized cancer vaccine. And so we're going to see over the coming years certainly, a lot of progress of mRNA vaccines targeting the things that I've mentioned, cancer, Zika, malaria, tuberculosis, and the list really goes on and on. So this isn't just a vaccine platform. It's a platform for just a different way of thinking about how we introduce healthcare and beyond human health, how we begin our process of influencing life. So there are lots of agricultural applications and animal applications of these same technologies, sorry for the long answer, but I think it's interesting.

Chris Gannatti:

No, no, that was excellent. And something your response made me think of, and I'm curious to see how you would address this part of it in the sense that you sort of said a foundation needed to be laid. You're building a delivery platform, and you could do all this interesting stuff once you have the platform. And the first interesting, scalable use case has been SARS-CoV-2. Do you foresee this as a process to get that other very interesting set of tools, whether it's for cancer or malaria or some of these other things, which are certainly important as well, is this a year's type of process? Is this a 10-year, possibly 20? I mean, how should we think of the amount of time between right now, when we know Moderna is doing the research to when they could actually be getting people out there the help that they need?

Jamie Metzl:

So, one way of thinking about this is to say that the SARS-CoV-2 virus was to biotechnology, kind of like what World War II was to electronics and space travel. It was a massive accelerant. It got lots of time, energy, and money focused on this set of problems. And so I do think that it will be single-digit numbers of years before we're going to see mRNA applications targeting these other indications. This is healthcare. And particularly with human healthcare, we want our regulators to slow things down. We want to have processes, and we were able to go from... It only took 11 months from when Moderna and Pfizer, BioNTech had the sequence genome of the SARS-CoV-2 virus to when the vaccine started being inoculated and under emergency use in the United States and around the world. We never could have had that speed absent the pandemic. So that catalyst is still happening.

And so we will see certainly cancer and some of these other areas, we will see it. We don't want it to go too fast because we want our regulators to do their jobs. And we have, in many ways, a higher standard for these kinds of newer interventions than we have for some of the older ones. That we've grown accustomed to a level of complication with anything and taking a Tylenol that we've just internalized and we haven't yet for these kinds of vaccines.

And so that's why we've seen a lot of scrutiny on the health and the safety record of these vaccines. I think they're extremely high, and we'll have that same level, but I think it's single digits number of years for some, I believe, very significant applications.

Chris Gannatti:

And we actually had a question come in from the audience. I'll paraphrase in the sense that, as with anything in medicine, there are side effects. We're talking about Advil before we got on the line here, and Advil would have side effects for a certain percentage of the global population. So in the case of mRNA, there were certain inflammation responses that people experienced, and when we have these other applications and, in a sense, an even wider scale usage, maybe of mRNA because maybe some people didn't want to deal with it for COVID purposes, but they would want it for cancer purposes. What assurances are the regulators giving, or are you seeing in terms of the companies in terms of the overall safety for these types of technologies?

Jamie Metzl:

Yeah, it's a really important question because, especially when we're moving this quickly, there's a balance between speed and careful regulation. There was so much of a push for these mRNA vaccines. Even though, as I mentioned, I think they're extremely safe that it frightened people because on one hand, if there was a real chance in the earliest days of the pandemic, there would be no vaccine.

There's still no vaccine for HIV. There's no vaccine for malaria, even though they've both killed way more people than COVID-19 has. And so if that time, if we still didn't have a vaccine and we were just living the way that we lived in 2020, I think people would be angry at our regulators and our governments for not delivering a vaccine. If we have the fast vaccines, and prior to the COVID-19 vaccines, the fastest, this was 11 months, the record it usually been measured in decades, the record rare was four years for the mumps vaccine prior to COVID-19.

But I think it's right that we needed to be extra careful. And there was, it was a very small but meaningful increased risk in particularly teenage boys of myocarditis. And that there was a lot of debate around that. Some of which I was involved in. People like Paul Offit at Children's Hospital of Philadelphia, we're a little more cautious, not on the first two doses, but on the booster shots for that reason. And there can be no complete guarantees, but that's why we have regulators. That's why we need to empower our regulators to do their jobs, and their job is not making sure that everything is 100% perfect because we would have literally no treatments for anything if that was the standard, but their job is to make sure that the benefits significantly outweigh the costs and any downsides can be minimized. And that's certainly where we are.

With cancer vaccines, I mean, there are a lot of people with untreatable cancers, and that's why I think lots of people in oncology hospitals and cancer centers around the country and around the world, they agree to be part of trials because the tools that we have aren't able to solve in some cases deadly problems that they have. But the short answer is now this is new science, and all new science needs to be regulated, and we need to have strong regulation and make sure that we do it well. But these technologies, like with the mRNA vaccines for COVID, have a possibility to do a lot of good.

Chris Gannatti:

Something that I was thinking of as we've been talking about new science mRNA, another example of new science that got a lot of attention in recent years, probably just a bit before the pandemic, was CRISPR. So in a sense, we're talking about mRNA. You're delivering genetic instructions into human cells. We're talking about CRISPR. We're using enzymes and processes to better understand and read specific parts and isolate specific parts of a given genome.

So as you're thinking of those two technologies side by side, mRNA is in the news all the time. It's been a bit quieter on CRISPR. What are some of the things happening right now that we may not know about in CRISPR where you're assuming there's a bunch of different companies trying to commercialize and go in that direction with therapeutics?

Jamie Metzl:

So let me just broaden that. So we are now at this revolutionary transformational stage. I mean, we're talking about biotechnology, but it's really about at the intersection of biology, biotechnology, in other words, engineered biology, and artificial intelligence. And so these incredible toolkits are making all sorts of new things possible. I mean, none of, pretty much none of the technologies that we talk about in biotechnology are even imaginable outside of the context of AI and machine learning. The human brain is not sophisticated enough to understand the complexity of our biology.

And so the answer to that question about, well, how is AI being used in biotechnology? How is genome editing and CRISPR being used in technology? Those are drivers of the entire field. And so you can look across the board at human health applications. There are thousands, many, many thousands of clinical trials well underway using CRISPR to treat various diseases both in vitro, outside in the lab, where you take cells out of the body and engineer them and put them back into the body. An example of this are the CAR T therapies for treating cancer, where you take out somebody's cells, you engineer them to boost their innate cancer-fighting abilities, and then you put them back.

But there are also In-vivo applications where people's cells are being edited inside of their bodies. And that's being used now preliminarily to address certain specific blood and liver disorders. So we really are, and it's connected to our theme and our fund. The WDNA [WisdomTree BioRevolution Fund] is about this recognition that we're at this transformational moment in how humans interact with the living world.

I mean, we've been managing and manipulating the world around us, certainly for the last 10,000 years, since the early days of agriculture and plant and animal agriculture. But now we have the tools to read, write and hack the source code of life. And that is having just huge implications, not just for human health but for how we think about food, agriculture, energy, raw materials so many other things.

And because we're at this early stage of this revolution, like in the early days of the internet revolution, it's hard to predict who will be the winner. We don't know, but we do know that there is a whole new toolkit that will transform how the most innovative companies in the world that are touching life sciences in any way how they will function. So certainly, the idea behind W-DNA is to say, "Well, what's the smartest bet on this sector?" And by this sector, we don't just mean healthcare. We mean all of the sectors that have the potential to be significantly transformed by these underlying new capabilities.

Chris Gannatti:

In speaking of a transformative event, I was sitting there on the, I think it was the 28th of July, and a big announcement came out. If you have a sense of history, you remember Mendel and the stories about just breeding the peas, you want the taller plant, you want a different color, a different size. You're basically flying blind just doing trial and error approaches, but DeepMind comes out on July 28th of this year. And basically, for free creates a database that anyone can access. And its sort of the mother lode of structural biology in the sense that you can predict because you're basically talking about these different things that are really just instructions for assembling amino acids into different proteins and then ultimately different proteins together.

And the other aspect is, well, what's the shape of the protein and how are they going to react with different things? Now, DeepMind puts out, I think it's 214 million different proteins, essentially, almost every known protein with a prediction of the shape. What do you think this is going to mean? Do you think, Jamie, all the companies that we're discussing today, are basically benefiting from this type of dump on the internet of such significance?

Jamie Metzl:

The answer is yes. And then let me give a little bit of background. So you talked about it about proteins and protein structures. So you're right. As I said before, genetics instruct our cells to make proteins. Proteins are the building blocks of life. Proteins are made up of amino acids. If you sequence a protein, you get a string of essentially letters that's in our translation, but of letters that are the sequenced genome of the protein. But if that's all the information you have, you're really missing a lot because it's not just the order of the letters but the shape of the protein that determines its function.

So let me talk a little bit about AI. In 2016 and maybe some of you remember this, the world was shocked when DeepMind, the same company that Chris mentioned when their algorithm defeated Lee Sedol, who at the time was the world champion Go player in four out of five games in a high-profile match in Korea. And prior to that, people had thought, most people had thought that, yes, we understand that AI can beat Garry Kasparov and chess champions. But chess is a game of brute force like the mathematics of chess made it computationally possible for the old models of computer. I don't even use the word thinking, computer functioning to beat grandmasters. And it got to the point where everybody's the program on your phone could beat the world's greatest grandmasters.

But Go, which has a much greater complexity, people thought was decades off. In 2016 the AlphaGo created by DeepMind defeated Lee Sedol, and the training data set that they had used for AlphaGo had been the entire universe of digitized Go games by Go masters. And so basically, it was mining. The algorithm was mining pooled human expertise and then saying, "Well, what can we learn from all of these Go masters?"

So it was almost like all the Go masters, plus the AI in competition against this one player Lee Sedol. The next year, 2017, DeepMind introduced its next program called AlphaZero. Rather than feeding AlphaZero, the entire training set of all of these games, they just fed it the very simple instructions of the game of Go and instructed AlphaZero to start playing games against itself and to learn lessons from about what worked and what didn't work.

Three days later, three days after starting that process, AlphaZero defeated AlphaGo, who a year before had defeated the world's greatest Go player. The next year building on top of this success, Demis Hassabis and the team at DeepMind decided, well, they were going to use this idea to try to solve what had been considered one of the great unsolvable problems of biology, the protein folding problem.

And the question was, could you predict the structure of a protein from the letters? And people had thought it was not possible. There was a competition, a protein folding competition that had been going on for a number of years, and in 2018 DeepMind entered their program into that competition. And they did average, they were 20th place, so it was certainly, they didn't blow anybody away.

And so they went back, and it's a biannual competition. And over the next two years, they reformulated their algorithm. They brought in additional training sets, and in 2020, they trounced everybody else so much so that the journal Nature called the problem of predicting proteins essentially solved. That was 2020. 2021, they then released the predictions of how around 350,000 proteins would fold. And that was certainly useful because most of the proteins in the human body were included in that dump. And that was great. That was a big deal.

This year, as Chris mentioned, they released and made publicly available the predictions of essentially pretty much all there's about 230 proteins known in all of science. And so they released around 200, I'm sorry, 230 million, and they released around 214 million. And when you think in the old ways, which was just a couple of years ago to analyze the structure of a folded protein, that would use a thing called X-ray crystallography where you turned the little protein molecule into a crystal, and then you X-ray it from different angles. And it would take roughly between three to five years to do it.

Lots of people got their PhDs by doing X-ray crystallography analysis of a single protein. And so if you use the lower number, let's say it's three years average. So three years times 200 million proteins. So let's say just using that math that's 600 million human hours can now be repurposed. It's kind of like in the early days of industrial agriculture, how much human energy could be repurposed from working on farms to doing other things. And this isn't just about protein folding. It's about every problem of equivalent levels of complexity. And there are lots of problems across lots of fields that are less complex than the protein folding problem.

So now everybody who is working in synthetic biology, everybody who is working in fields like drug discovery, thinking about, well, how do we engineer allergy to do a better job of fighting fuel spills? There are just really thousands more than thousands of applications. And so the starting point, it's kind of like the AI tools, kind of like CRISPR, the starting point for solving problems that have any kind of biological basis is now much further along than it was before. That doesn't mean we can magically snap our fingers and solve these problems, but it does mean that the process of not just solving problems, but doing really cool, new interesting things is accelerating across the board in a very exciting way.

Chris Gannatti:

No question. And so Jamie speaking of problems and solving them, I'm sitting in Europe, and we hear every single day, multiple times a day, about energy and how that relates to the situation in Ukraine and Russia and everything going on. But another part of that situation has to do with food and food security. And it's not an evenly distributed problem around the world. Some countries certainly suffer more than others, but it's a big problem. And from everything you're saying and everything we've talked about with regard to the focus here on the bio revolution, a lot can be done that could have significant implications for whether it's climate change, whether it's sustainability, whether it's more productive agriculture. And you had even mentioned, you had experience with the WHO. And so I imagine you also have a sense of how different countries approach the matter, whether it's genetically modified organisms and the like, and so was just wondering the applications that you're seeing of the tools that we've been discussing to possibly helping with the food issues that plague the world today.

Jamie Metzl:

Great. So you're absolutely right. I mean, right now, we have food problems as a result of the War in Ukraine and other things. We have 8 billion humans on Planet Earth. Estimates are by 2050, we'll have nine to 10 billion. Right now, agriculture is if plant and animal agriculture is we're using more than half of all arable land for that if we just continue to do agriculture, the way that we're doing, we scale up exactly what we're doing now in order to feed 10 billion people. And not just 10 billion people, like we all are today. But people in, certainly in other parts of the world who are getting wealthier, who are going to want to eat like us, if we continue on this path we're going to decimate our planet.

Half of all human induced greenhouse gas emissions come from agriculture. So it's thinking differently about agriculture is also part of addressing climate change. And I know people get uncomfortable when we talk about applying biotechnology to food and agriculture, but I always remind people that agriculture itself is one of the most radical biotechnologies in human history.

We didn't show up on Planet Earth. Our ancestors didn't just find agriculture. We invented it, certainly when the last ice age receded, and the conditions made that possible. So certainly, there are many different applications of biotechnology to agriculture. We're seeing, I mean, most of the crops that we eat result from the green revolution. Maybe you're familiar with Norman Borlaug, the Nobel Prize winner, and how those ideas of just of what is almost all of the wheat and the corn and the rice that we eat is a result of that earlier of the new approaches of the earlier Green Revolution.

But now we're in a position to do even more, to think differently about manipulating, for example, the microbiomes in soils and on that lining the roots of plants that can help the plants grow better, perhaps help the plants absorb more sunlight and therefore reduce greenhouse gas emissions. There was an incredible paper that came out from China just about a month ago that showed how just some relatively small edits in the genome of a rice crop increased yield by about 40%. It allowed the rice crop to grow, to arrive at maturity two weeks earlier. And it did a better job of capturing carbon. And so we're going to have to... There are already a lot of companies that are exploring this possibility, and this must be part of the mix and will be part of the mix for how we think differently about feeding 10 billion people.

Animal agriculture is another area. Humans, we now slaughter about 70 billion land animals per year. Most of them, about 75%, are in industrial animal farms, which in addition to the cruelty to the animals, end up being places where there's a lot of widespread use of antibiotics, a lot of big climate impact, to big environmental impact. And so there's a whole new field of thinking differently about how we can grow animal products. And I know people sometimes can get queasy about the idea of cell-cultured meat, but when we think about it, we're not loving cows and chickens that we eat. I mean, certainly the ones that come from these industrial animal farms.

And so if we can grow bio-identical animal proteins that are the same in every way, it's just rather than growing them in a cow or a chicken, we're growing them in a bioreactor. I think we should be open-minded about that possibility, maybe not for the prime rib or the kind of premium product, but a lot of us are eating animal products inside of other products that we don't even recognize. And that's certainly a lot of animal products we consume are like that.

And so long way of saying is that we already live in an engineered world. The products that we consume are pretty much all engineered. Some occasions some things aren't obviously, and so the question is, how can we think about using these applications wisely, carefully, safely? But that is a revolution that's happening, and we're already seeing people becoming more comfortable with it. These essentially engineered plant proteins, whether it's the Impossible Burger or other things that we've now, all I think eaten are something that people 10 years ago would've said, "Well, that sounds weird." You're engineering a plant protein to function, to bleed as if it has hemoglobin.

And it's true. And that's why the Impossible Burger tastes familiar to us. But I think we've kind of overcome that barrier. And there are things that just will seem weird to us today that will seem less weird to us tomorrow.

Chris Gannatti:

I know I was reading just before this presentation how the company Beyond Meat one of their key goals is to basically provide at the same cost or hopefully even lower cost basically the hamburgers and other things that have the same texture, taste as Jamie indicated the coloring, all the same behavior, the sizzle on the pan or the grill. And it's really incredible. The article indicated they're at about $8 per pound.

Jamie Metzl:

Yep.

Chris Gannatti:

Whereas the standard approach might be closer to $5 a pound, roughly speaking. We've had a lot of inflation somewhere. It's a little harder.

Jamie Metzl:

10 years ago, the first of these cell-cultivated burgers cost about $325,000. So the trend lines are really heading in the right direction. And certainly, in the United States, beef is heavily subsidized by the government. And so I do think that yes, they'll be the first time, they'll be the first time somebody eats a cell cultivated hamburger, but then I think people will taste it, and it probably will taste pretty good.

And we have a whole generation of young people who are more vegetarians or more environmentally conscious than certain people of my generation. And I think that it will be welcome to those people. It's not going to be something where everything changes at once. But I think there's an incredible growth trajectory for all of this.

Chris Gannatti:

So as we look, I've got my eye on the clock here to just help with everyone managing the time. I did have a question come in from the audience. So we're going to, for a moment, shift back briefly to mRNA, and when the vaccines initially came out, there were a bunch of different companies, and we referred to Pfizer and BioNTech, and those are sort of two of your mRNA examples, but there was an AstraZeneca, and there was a J&J as well. Jamie, were there any things different to mRNA that were used in some of these different vaccines and specifically the J&J shot?

Jamie Metzl:

So all of the different vaccines, I mean, in the beginning, we didn't know what was going to work. And so what the NIH did was fund all of these different approaches. Some of them were the traditional approaches, and that's certainly the Chinese vaccines were essentially the same technology as the polio vaccines, a weekend dead or weekend version of the SARS-CoV-2 virus.

Others like the Oxford AstraZeneca we're using viral vectors. So basically, it's getting a virus that is capable of delivering a payload that infects humans, but you kind of denature that virus and instead put the instructions that you want. So the delivery is a virus rather than the lipid nanoparticles that we talked about with the mRNA. And I think the world, everybody, nobody knew what was going to work best. It just turned out that the mRNA vaccines worked better than the other ones. And not only that, as we're seeing today, and as I mentioned, the platform for producing the mRNA vaccines is so much more flexible. It's so much more of a plug-in-play model that, for all of the other approaches, it wouldn't have been possible to turn on a dime like we're doing today with these new Omicron boosters.

So I do think that this does not mean that all of the other approaches to vaccination are off the table. They certainly are not. And there may be other use cases where one approach or another works better. But I do think this COVID-19 experience has proven this first use case of the mRNA vaccines. We're seeing now the flexibility of the mRNA vaccines, and the same production facilities that are creating these vaccines with very minimal changes can be used to produce gene therapies, other gene therapies because the mRNA vaccines could arguably be called gene therapies very, very quickly.

And so that's what I'm certainly hoping that with all the investment that we are making in the mRNA COVID vaccines, if, and when COVID recedes, I really hope that will maintain our capacity, even if it becomes a little bit inefficient, even if the government needs to subsidize that process because this is a delivery system that can be used for a lot of different things.

Chris Gannatti:

I saw a paper come out, and there was a summary in the MIT technical review within the last few days. And it was alluding to the fact that we spent a lot of time talking about the spike protein that you mentioned. And the thing about that is when there's a mutation, the spike protein changes, and that's ultimately why simplistically speaking, you need a booster because now you have to recognize essentially the new shape, but not every aspect of the virus changes with the same degree of frequency in mutations.

So there was an exploration they called it a mosaic nanoparticle. And the idea was, why don't you guide the "missile," so to speak, the missile, being the human immune system, not towards this thing that changes, but towards this other part of the structure? And it seemed fascinating. And if they're successful, and we'll have to see, this could mean a much more limited need for boosters and that sort of yearly whether it's the influenza, whether it's the COVID, or whether it's something else.

Jamie Metzl:

Yeah, that's exactly right. So they're talking about a pan coronavirus booster, a pan influenza shot, and that's the idea, you don't have to target necessarily the spike protein. You just have to target something to train the human immune system to see something. And that's why, as I was alluding to before, this is an AI challenge because you just need a massive amount of data. You need to understand how these viruses are mutating to try to identify well what are these kind of these core identities all these different viruses will have?

It connects to the question about protein folding. How do you make sure that the missile and the target are going to be able to lock into each other? And if you can do that through a computer simulation, that's going save a lot of time and energy. If you have to do it through trial and error in a wet lab, that's going to take many, many years or decades longer.

So everything is speeding up. And Chris, I know we're running a little bit short on time, so I thought if it's okay, just mention a little bit about cyclicality, which I think is important to what we're talking about here. Since the beginning of the pandemic, we have seen the largest increase in the valuation of biotech companies in history, followed by the largest drop in the valuation of biotech companies in history. And the two things are connected. And I, for one, see this as being something very, very similar to what happened in the early days of the internet revolution. There was so much excitement that the valuations went up and up and up. And in some ways, they were about a potential story that wasn't yet fully realized.

And so there was a correction with the internet, and just as there was now a correction with certainly in the biotechnology and life sciences sector, certainly inflation has put more pressure on the companies that don't have products already in market because it's just increased the cost of being a pre-product company.

And so, for me, the real story here, at least as I see it, which is why I'm excited about doing this. And my association with WisdomTree is nobody can know what this cycle trend is going to look like, but I, for one, I mean it's my life's work. But I, for one, have no doubt that the story of the genetics and biotechnology revolutions and of human-engineered biology is the essential story of the 21st century, just like the 19th century was this century of chemistry and the 20th century, the century of physics. So I have no doubt in my mind about that.

Chris Gannatti:

So in looking at some of our other questions that came in, there were some pre-submitted questions. And I just wanted to put those on the board. I looked up how I would approach some of them as well, but we'll start with Jamie on you've sort of got AI. We've been talking about it all the way through, and you could almost think of it on a spectrum. There are certainly companies in the private space where maybe all they do is AI, and they're trying to solve a very specific need, very specific problem. They might be earlier in their life cycle, and then you can go all the way down the spectrum. And I know you, and I are familiar with companies where it may not be all that they do is AI, but they certainly use AI to improve and enhance their offering.

And so, how confident are you, Jamie, when you're looking at some of the companies that we believe represent the bio revolution, that nearly all of them are aware of AI and machine learning and are using it in some way to help their business models along?

Jamie Metzl:

100% confident because, as I said before, you just can't be in this sector. There is no biotech sector apart from the AI and machine learning revolution. These technologies are of a piece. It's just a shorthand that we use to say, "Oh, there's this thing, computer science, there's this thing, machine learning, there's this thing, biotech." Any company in the biotech sector can only function by using these technologies.

I mean, there are lots of traditional companies in the world who are just doing traditional things, and that's great. But even like when I was in my head thinking, well, what are those sectors? I was thinking, well, agriculture. But you look at... I talked about agriculture. Agriculture is changing. This is one thing. It's one megatrend. And then there are these accelerants, and certainly, AI, machine learning, genome editing, all of these are essentially tools. And the world is opening up to thinking creatively about how all these tools can be used. So it's a very exciting time, but for any company to be in the biotech sector, you have to also be heavily invested in AI.

Chris Gannatti:

Yeah. When we saw this question again before we started, we were kind of Googling around different companies again that we believe represent the bio revolution megatrend. Benson Hill was an example that came up. They have something called CropOS®, a technology platform. And for example, they're looking at the complicated genome of the yellow pea, and to do various things with that, Jamie indicated very astutely, there's going to be more people. We're going to need more food. We're going to need more sources of protein.

So with this platform, they're using AI and machine learning to try to say, "Okay, how do we increase the protein content per yield of yellow peas? How do we influence the taste?" Is maybe before influencing the taste, people weren't thinking of this, even as food. And that's just one example of a company attacking, as Jamie mentioned, a key problem, we need more food and using AI to get the job done.

So the final question this was also a pre-submitted question. With AI, you're always using data, and I sit in Europe as of this moment. So you've got GDPR, then in the US, you've got HIPAA. Every country is going to have its own data regulations. But similar, it's important to have concerns about safety and the regulators looking at that. Jamie, have you seen anything that ensures data protection and sensitivity to privacy in thinking about artificial intelligence?

Jamie Metzl:

Oh, yeah. It's essential. This is certainly with AI and the intersection of AI and human health. It's just critically important because, as I mentioned before, to train the AI, we need big data sets. And when we're talking about human health, those data sets are our biologies. And so that's why there's one of the big piece of this is building those data sets. Probably the best genetic data pool in the world is the UK Biobank which is expertly managed by the UK government, researchers who want access to it have to apply and be approved, and there's oversight and regulation.

To make this revolution work, there needs to be a very high level of transparency and accountability, and people need to feel that their privacy is protected. That doesn't mean that we control everything because, for example, every time we go on Netflix, we're getting something back by when we decide what to watch based on the recommendations, but we're giving something back as every decision we make is informing well, what is popular and what is trending. And so there will be that same kind of give and take with our biological information. If everybody only we hoard our individual information, we won't be able to have the data pools that will drive insights about all of this. So it's an essential point.

Chris Gannatti:

For the audience, if you want to Google something interesting along that topic, it's called homomorphic encryption. The concept is we know things like Amazon Web Services, Google Cloud data end up in these places, and you can actually run the calculations, gain the insights that are used in these algorithms to make inferences, to make predictions without needing to decrypt the data.

So essentially, whoever is running those models would not need to unlock. Okay, this is exactly what the data is in any sort of human intelligible way. So it's fascinating. The big companies are thinking about this, and it's fascinating some of the solutions already available to ensure the privacy of data.

So Jamie, any final remarks as we close it out?

Jamie Metzl:

No, it just as I said before, I see this as a transitional moment for just how humans interact with the natural world. It's incredibly exciting. But that doesn't mean there aren't associated dangers like there are with every technology. And so, while I'm incredibly enthusiastic about this sector, the key to having everything work will be not just the technology but making sure that the technology and the values that we apply to guide the technology go hand in hand.

Chris Gannatti:

That's a great way to close it. So thank you, Jamie. Thank you to all of our audience. Thank you, Irene, for helping us put this together, and everybody, have a great day.

Irene:

Thank you, everyone.

Jamie Metzl:

Thanks, everybody. Thanks.

There are risks associated with investing, including possible loss of principal. The Fund invests in BioRevolution companies, which are companies significantly transformed by advancements in genetics and biotechnology. BioRevolution companies face intense competition and potentially rapid product obsolescence. These companies may be adversely affected by the loss or impairment of intellectual property rights and other proprietary information or changes in government regulations or policies. Additionally, BioRevolution companies may be subject to risks associated with genetic analysis. The Fund invests in the securities included in, or representative of, its Index regardless of their investment merit and the Fund does not attempt to outperform its Index or take defensive positions in declining markets. The composition of the Index is governed by an Index Committee and the Index may not perform as intended. Please read the Fund’s prospectus for specific details regarding the Fund’s risk profile.