Aubrey de Grey at the World Stem Cell Summit in Miami on January 25, 2018.
Aging is not a mystery, says famed researcher Dr. Aubrey de Grey, perhaps the world's foremost advocate of the provocative view that medical technology will one day allow humans to control the aging process and live healthily into our hundreds—or even thousands.
"The cultural attitudes toward all of this are going to be completely turned upside down by sufficiently promising results in the lab, in mice."
He likens aging to a car wearing down over time; as the body operates normally, it accumulates damage which can be tolerated for a while, but eventually sends us into steep decline. The most promising way to escape this biological reality, he says, is to repair the damage as needed with precise scientific tools.
The bad news is that doing this groundbreaking research takes a long time and a lot of money, which has not always been readily available, in part due to a cultural phenomenon he terms "the pro-aging trance." Cultural attitudes have long been fatalistic about the inevitability of aging; many people balk at the seemingly implausible prospect of indefinite longevity.
But the good news for de Grey—and those who are cheering him on—is that his view is becoming less radical these days. Both the academic and private sectors are racing to tackle aging; his own SENS Research Foundation, for one, has spun out into five different companies. Defeating aging, he says, "is not just a future industry; it's an industry now that will be both profitable and extremely good for your health."
De Grey sat down with Editor-in-Chief Kira Peikoff at the World Stem Cell Summit in Miami to give LeapsMag the latest scoop on his work. Here is an edited and condensed version of our conversation.
Since your book Ending Aging was published a decade ago, scientific breakthroughs in stem cell research, genome editing, and other fields have taken the world by storm. Which of these have most affected your research?
They have all affected it a lot in one way, and hardly at all in another way. They have speeded it up--facilitated short cuts, ways to get where we're already trying to go. What they have not done is identified any fundamental changes to the overall strategy. In the book, we described the seven major types of damage, and particular ways of going about fixing each of them, and that hasn't changed.
"Repair at the microscopic level, one would be able to expect to do without surgery, just by injecting the right kind of stem cells."
Has any breakthrough specifically made the biggest impact?
It's not just the obvious things, like iPS (induced pluripotent stem cells) and CRISPR (a precise tool for editing genes). It's also the more esoteric things that applied specifically to certain of our areas, but most people don't really know about them. For example, the identification of how to control something called co-translational mitochondrial protein import.
How much of the future of anti-aging treatments will involve regeneration of old tissue, or wholesale growth of new organs?
The more large-scale ones, regenerating whole new organs, are probably only going to play a role in the short-term and will be phased out relatively rapidly, simply because, in order to be useful, one has to employ surgery, which is really invasive. We'll want to try to get around that, but it seems quite likely that in the very early stages, the techniques we have for repairing things at the molecular and cellular level in situ will be insufficiently comprehensive, and so we will need to do the more sledgehammer approach of building a whole new organ and sticking it in.
Every time you are in a position where you're replacing an organ, you have the option, in principle, of repairing the organ, without replacing it. And repair at the microscopic level, one would be able to expect to do without surgery, just by injecting the right kind of stem cells or whatever. That would be something one would expect to be able to apply to someone much closer to death's door and much more safely in general, and probably much more cheaply. One would expect that subsequent generations of these therapies would move in that direction.
Your foundation is working on an initiative requiring $50 million in funding—
Well, if we had $50 million per year in funding, we could go about three times faster than we are on $5 million per year.
And you're looking at a 2021 timeframe to start human trials?
That's approximate. Remember, because we accumulate in the body so many different types of damage, that means we have many different types of therapy to repair that damage. And of course, each of those types has to be developed independently. It's very much a divide and conquer therapy. The therapies interact with each other to some extent; the repair of one type of damage may slow down the creation of another type of damage, but still that's how it's going to be.
And some of these therapies are much easier to implement than others. The easier components of what we need to do are already in clinical trials—stem cell therapies especially, and immunotherapy against amyloid in the brain, for example. Even in phase III clinical trials in some cases. So when I talk about a timeframe like 2021, or early 20s shall we say, I'm really talking about the most difficult components.
What recent strides are you most excited about?
Looking back over the past couple of years, I'm particularly proud of the successes we've had in the very most difficult areas. If you go through the 7 components of SENS, there are two that have absolutely been stuck in a rut and have gotten nowhere for 15 to 20 years, and we basically fixed that in both cases. We published two years ago in Science magazine that essentially showed a way forward against the stiffening of the extracellular matrix, which is responsible for things like wrinkles and hypertension. And then a year ago, we published a real breakthrough paper with regard to placing copies of the mitochondria DNA in the nuclear DNA modified in such a way that they still work, which is an idea that had been around for 30 years; everyone had given up on it, some a long time ago, and we basically revived it.
A slide presented by Aubrey de Grey, referencing his collaboration with Mike West at AgeX, showing the 7 types of damage that he believes must be repaired to end aging.
(Courtesy Kira Peikoff)
That's exciting. What do you think are the biggest barriers to defeating aging today: the technological challenges, the regulatory framework, the cost, or the cultural attitude of the "pro-aging" trance?
One can't really address those independently of each other. The technological side is one thing; it's hard, but we know where we're going, we've got a plan. The other ones are very intertwined with each other. A lot of people are inclined to say, the regulatory hurdle will be completely insurmountable, plus people don't recognize aging as a disease, so it's going to be a complete nonstarter. I think that's nonsense. And the reason is because the cultural attitudes toward all of this are going to be completely turned upside down before we have to worry about the regulatory hurdles. In other words, they're going to be turned upside down by sufficiently promising results in the lab, in mice. Once we get to be able to rejuvenate actually old mice really well so they live substantially longer than they otherwise would have done, in a healthy state, everyone's going to know about it and everyone's going to demand – it's not going to be possible to get re-elected unless you have a manifesto commitment to turn the FDA completely upside down and make sure this happens without any kind of regulatory obstacle.
I've been struggling away all these years trying to bring little bits of money in the door, and the reason I have is because of the skepticism as to regards whether this could actually work, combined with the pro-aging trance, which is a product of the skepticism – people not wanting to get their hopes up, so finding excuses about aging being a blessing in disguise, so they don't have to think about it. All of that will literally disintegrate pretty much overnight when we have the right kind of sufficiently impressive progress in the lab. Therefore, the availability of money will also [open up]. It's already cracking: we're already seeing the beginnings of the actual rejuvenation biotechnology industry that I've been talking about with a twinkle in my eye for some years.
"For humans, a 50-50 chance would be twenty years at this point, and there's a 10 percent chance that we won't get there for a hundred years."
Why do you think the culture is starting to shift?
There's no one thing yet. There will be that tipping point I mentioned, perhaps five years from now when we get a real breakthrough, decisive results in mice that make it simply impossible to carry on being fatalistic about all this. Prior to that, what we're already seeing is the impact of sheer old-school repeat advertising—me going out there, banging away and saying the same fucking thing again and again, and nobody saying anything that persuasively knocks me down. … And it's also the fact that we are making incremental amounts of progress, not just ourselves, but the scientific community generally. It has become incrementally more plausible that what I say might be true.
I'm sure you hate getting the timeline question, but if we're five years away from this breakthrough in mice, it's hard to resist asking—how far is that in terms of a human cure?
When I give any kind of timeframes, the only real care I have to take is to emphasize the variance. In this case I think we have got a 50-50 chance of getting to that tipping point in mice within five years from now, certainly it could be 10 or 15 years if we get unlucky. Similarly, for humans, a 50-50 chance would be twenty years at this point, and there's a 10 percent chance that we won't get there for a hundred years.
"I don't get people coming to me saying, well I don't think medicine for the elderly should be done because if it worked it would be a bad thing. People like to ignore this contradiction."
What would you tell skeptical people are the biggest benefits of a very long-lived population?
Any question about the longevity of people is the wrong question. Because the longevity that people fixate about so much will only ever occur as a side effect of health. However long ago you were born or however recently, if you're sick, you're likely to die fairly soon unless we can stop you being sick. Whereas if you're healthy, you're not. So if we do as well as we think we can do in terms of keeping people healthy and youthful however long ago they were born, then the side effect in terms of longevity and life expectancy is likely to be very large. But it's still a side effect, so the way that people actually ought to be—in fact have a requirement to be—thinking, is about whether they want people to be healthy.
Now I don't get people coming to me saying, well I don't think medicine for the elderly should be done because if it worked it would be a bad thing. People like to ignore this contradiction, they like to sweep it under the carpet and say, oh yeah, aging is totally a good thing.
People will never actually admit to the fact that what they are fundamentally saying is medicine for the elderly, if it actually works, would be bad, but still that is what they are saying.
Shifting gears a bit, I'm curious to find out which other radical visionaries in science and tech today you most admire?
Fair question. One is Mike West. I have the great privilege that I now work for him part-time with Age X. I have looked up to him very much for the past ten years, because what he did over the past 20 years starting with Geron is unimaginable today. He was working in an environment where I would not have dreamt of the possibility of getting any private money, any actual investment, in something that far out, that far ahead of its time, and he did it, again and again. It's insane what he managed to do.
What about someone like Elon Musk?
Sure, he's another one. He is totally impervious to the caution and criticism and conservatism that pervades humanity, and he's getting on making these bloody self-driving cars, space tourism, and so on, making them happen. He's thinking just the way I'm thinking really.
"You can just choose how frequently and how thoroughly you repair the damage. And you can make a different choice next time."
You famously said ten years ago that you think the first person to live to 1000 is already alive. Do you think that's still the case?
Definitely, yeah. I can't see how it could not be. Again, it's a probabilistic thing. I said there's at least a 10 percent chance that we won't get to what I call Longevity Escape Velocity for 100 years and if that's true, then the statement about 1000 years being alive already is not going to be the case. But for sure, I believe that the beneficiaries of what we may as well call SENS 1.0, the point where we get to LEV, those people are exceptionally unlikely ever to suffer from any kind of ill health correlated with their age. Because we will never fall below Longevity Escape Velocity once we attain it.
Could someone who was just born today expect—
I would say people in middle age now have a fair chance. Remember – a 50/50 chance of getting to LEV within 20 years, and when you get there, you don't just stay at biologically 70 or 80, you are rejuvenated back to biologically 30 or 40 and you stay there, so your risk of death each year is not related to how long ago you were born, it's the same as a young adult. Today, that's less than 1 in 1000 per year, and that number is going to go down as we get self-driving cars and all that, so actually 1000 is a very conservative number.
So you would be able to choose what age you wanted to go back to?
Oh sure, of course, it's just like a car. What you're doing is you're repairing damage, and the damage is still being created by the body's metabolism, so you can just choose how frequently and how thoroughly you repair the damage. And you can make a different choice next time.
What would be your perfect age?
I have no idea. That's something I don't have an opinion about, because I could change it whenever I like.
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
