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.
Basecamp Research is using portable labs like this one to gather samples from ecosystems around the world.
Clark works for Basecamp Research, a biotech company headquartered in London, and his job is to collect samples from ecosystems around the world. By extracting DNA from soil, water, plants, microbes and other organisms, Basecamp is building an extensive database of the Earth’s proteins. While DNA itself isn’t a protein, the information stored in DNA is used to create proteins, so extracting, sequencing, and annotating DNA allows for the discovery of unique protein sequences.
Using what they’re finding in the middle of the Atlantic and beyond, Basecamp’s detailed database is constantly growing. The outputs could be essential for cleaning up the damage done by toxic chemicals and finding alternatives to these chemicals.
Catalysts for change
Proteins provide structure and function in all living organisms. Some of these functional proteins are enzymes, which quite literally make things happen.
“Industrial chemistry is heavily polluting, especially the chemistry done in pharmaceutical drug development. Biocatalysis is providing advantages, both to make more complex drugs and to be more sustainable, reducing the pollution and toxicity of conventional chemistry," says Ahir Pushpanath, who heads partnerships for Basecamp.
“Enzymes are perfectly evolved catalysts,” says Ahir Pushpanath, a partnerships lead at Basecamp. ”Enzymes are essentially just a polymer, and polymers are made up of amino acids, which are nature’s building blocks.” He suggests thinking about it like Legos — if you have a bunch of Lego pieces and use them to build a structure that performs a function, “that’s basically how an enzyme works. In nature, these monuments have evolved to do life’s chemistry. If we didn’t have enzymes, we wouldn’t be alive.”
In our own bodies, enzymes catalyze everything from vision to digesting food to regrowing muscles, and these same types of enzymes are necessary in the pharmaceutical, agrochemical and fine chemical industries. But industrial conditions differ from those inside our bodies. So, when scientists need certain chemical reactions to create a particular product or substance, they make their own catalysts in their labs — generally through the use of petroleum and heavy metals.
These petrochemicals are effective and cost-efficient, but they’re wasteful and often hazardous. With growing concerns around sustainability and long-term public health, it's essential to find alternative solutions to toxic chemicals. “Industrial chemistry is heavily polluting, especially the chemistry done in pharmaceutical drug development,” Pushpanath says.
Basecamp is trying to replace lab-created catalysts with enzymes found in the wild. This concept is called biocatalysis, and in theory, all scientists have to do is find the right enzymes for their specific need. Yet, historically, researchers have struggled to find enzymes to replace petrochemicals. When they can’t identify a suitable match, they turn to what Pushpanath describes as “long, iterative, resource-intensive, directed evolution” in the laboratory to coax a protein into industrial adaptation. But the latest scientific advances have enabled these discoveries in nature instead.
Marlon Clark, a research scientist at Basecamp Research, looks for novel biochemistries in the Azores.
Glen Gowers
Enzyme hunters
Whether it’s Clark and a colleague setting off on an expedition, or a local, on-the-ground partner gathering and processing samples, there’s a lot to be learned from each collection. “Microbial genomes contain complete sets of information that define an organism — much like how letters are a code allowing us to form words, sentences, pages, and books that contain complex but digestible knowledge,” Clark says. He thinks of the environmental samples as biological libraries, filled with thousands of species, strains, and sequence variants. “It’s our job to glean genetic information from these samples.”
“We can actually dream up new proteins using generative AI," Pushpanath says.
Basecamp researchers manage this feat by sequencing the DNA and then assembling the information into a comprehensible structure. “We’re building the ‘stories’ of the biota,” Clark says. The more varied the samples, the more valuable insights his team gains into the characteristics of different organisms and their interactions with the environment. Sequencing allows scientists to examine the order of nucleotides — the organic molecules that form DNA — to identify genetic makeups and find changes within genomes. The process used to be too expensive, but the cost of sequencing has dropped from $10,000 a decade ago to as low as $100. Notably, biocatalysis isn’t a new concept — there have been waves of interest in using natural enzymes in catalysis for over a century, Pushpanath says. “But the technology just wasn’t there to make it cost effective,” he explains. “Sequencing has been the biggest boon.”
AI is probably the second biggest boon.
“We can actually dream up new proteins using generative AI,” Pushpanath says, which means that biocataylsis now has real potential to scale.
Glen Gowers, the co-founder of Basecamp, compares the company’s AI approach to that of social networks and streaming services. Consider how these platforms suggest connecting with the friends of your friends, or how watching one comedy film from the 1990s leads to a suggestion of three more.
“They’re thinking about data as networks of relationships as opposed to lists of items,” says Gowers. “By doing the same, we’re able to link the metadata of the proteins — by their relationships to each other, the environments in which they’re found, the way those proteins might look similar in sequence and structure, their surrounding genome context — really, this just comes down to creating a searchable network of proteins.”
On an Azores island, this volcanic opening may harbor organisms that can help scientists identify enzymes for biocatalysis to replace toxic chemicals.
Emma Bolton
Uwe Bornscheuer, professor at the Institute of Biochemistry at the University of Greifswald, and co-founder of Enzymicals, another biocatalysis company, says that the development of machine learning is a critical component of this work. “It’s a very hot topic, because the challenge in protein engineering is to predict which mutation at which position in the protein will make an enzyme suitable for certain applications,” Bornscheuer explains. These predictions are difficult for humans to make at all, let alone quickly. “It is clear that machine learning is a key technology.”
Benefiting from nature’s bounty
Biodiversity commonly refers to plants and animals, but the term extends to all life, including microbial life, and some regions of the world are more biodiverse than others. Building relationships with global partners is another key element to Basecamp’s success. Doing so in accordance with the access and benefit sharing principles set forth by the Nagoya Protocol — an international agreement that seeks to ensure the benefits of using genetic resources are distributed in a fair and equitable way — is part of the company's ethos. “There's a lot of potential for us, and there’s a lot of potential for our partners to have exactly the same impact in building and discovering commercially relevant proteins and biochemistries from nature,” Clark says.
Bornscheuer points out that Basecamp is not the first company of its kind. A former San Diego company called Diversa went public in 2000 with similar work. “At that time, the Nagoya Protocol was not around, but Diversa also wanted to ensure that if a certain enzyme or microorganism from Costa Rica, for example, were used in an industrial process, then people in Costa Rica would somehow profit from this.”
An eventual merger turned Diversa into Verenium Corporation, which is now a part of the chemical producer BASF, but it laid important groundwork for modern companies like Basecamp to continue to scale with today’s technologies.
“To collect natural diversity is the key to identifying new catalysts for use in new applications,” Bornscheuer says. “Natural diversity is immense, and over the past 20 years we have gained the advantages that sequencing is no longer a cost or time factor.”
This has allowed Basecamp to rapidly grow its database, outperforming Universal Protein Resource or UniProt, which is the public repository of protein sequences most commonly used by researchers. Basecamp’s database is three times larger, totaling about 900 million sequences. (UniProt isn’t compliant with the Nagoya Protocol, because, as a public database, it doesn’t provide traceability of protein sequences. Some scientists, however, argue that Nagoya compliance hinders progress.)
“Eventually, this work will reduce chemical processes. We’ll have cleaner processes, more sustainable processes," says Uwe Bornscheuer, a professor at the University of Greifswald.
With so much information available, Basecamp’s AI has been trained on “the true dictionary of protein sequence life,” Pushpanath says, which makes it possible to design sequences for particular applications. “Through deep learning approaches, we’re able to find protein sequences directly from our database, without the need for further laboratory-directed evolution.”
Recently, a major chemical company was searching for a specific transaminase — an enzyme that catalyzes a transfer of amino groups. “They had already spent a year-and-a-half and nearly two million dollars to evolve a public-database enzyme, and still had not reached their goal,” Pushpanath says. “We used our AI approaches on our novel database to yield 10 candidates within a week, which, when validated by the client, achieved the desired target even better than their best-evolved candidate.”
Basecamp’s other huge potential is in bioremediation, where natural enzymes can help to undo the damage caused by toxic chemicals. “Biocatalysis impacts both sides,” says Gowers. “It reduces the usage of chemicals to make products, and at the same time, where contamination sites do exist from chemical spills, enzymes are also there to kind of mop those up.”
So far, Basecamp's round-the-world sampling has covered 50 percent of the 14 major biomes, or regions of the planet that can be distinguished by their flora, fauna, and climate, as defined by the World Wildlife Fund. The other half remains to be catalogued — a key milestone for understanding our planet’s protein diversity, Pushpanath notes.
There’s still a long road ahead to fully replace petrochemicals with natural enzymes, but biocatalysis is on an upward trajectory. "Eventually, this work will reduce chemical processes,” Bornscheuer says. “We’ll have cleaner processes, more sustainable processes.”
In an initial study, Canary analyzed speech recordings with AI and identified early stage Alzheimer’s with 96 percent accuracy.
Arnold was diagnosed sooner than many others. It's important to find out early, when interventions can make the most difference. One promising avenue is looking at how people talk. Research has shown that Alzheimer’s affects a part of the brain that controls speech, resulting in small changes before people show other signs of the disease.
Now, Canary Speech, a company based in Utah, is using AI to examine elements like the pitch of a person’s voice and their pauses. In an initial study, Canary analyzed speech recordings with AI and identified early stage Alzheimer’s with 96 percent accuracy.
Developing the AI model
Canary Speech’s CEO, Henry O’Connell, met cofounder Jeff Adams about 40 years before they started the company. Back when they first crossed paths, they were both living in Bethesda, Maryland; O’Connell was a research fellow at the National Institutes of Health studying rare neurological diseases, while Adams was working to decode spy messages. Later on, Adams would specialize in building mathematical models to analyze speech and sound as a team leader in developing Amazon's Alexa.
It wasn't until 2015 that they decided to make use of the fit between their backgrounds. ““We established Canary Speech in 2017 to build a product that could be used in multiple languages in clinical environments,” O'Connell says.
The need is growing. About 55 million people worldwide currently live with Alzheimer’s, a number that is expected to double by 2050. Some scientists think the disease results from a buildup of plaque in the brain. It causes mild memory loss at first and, over time, this issue get worse while other symptoms, such as disorientation and hallucinations, can develop. Treatment to manage the disease is more effective in the earlier stages, but detection is difficult since mild symptoms are often attributed to the normal aging process.
O’Connell and Adams specialize in the complex ways that Alzheimer’s effects how people speak. Using AI, their mathematical model analyzes 15 million data points every minute, focusing on certain features of speech such as pitch, pauses and elongation of words. It also pays attention to how the vibrations of vocal cords change in different stages of the disease.
To create their model, the team used a type of machine learning called deep neural nets, which looks at multiple layers of data - in this case, the multiple features of a person’s speech patterns.
“Deep neural nets allow us to look at much, much larger data sets built out of millions of elements,” O’Connell explained. “Through machine learning and AI, we’ve identified features that are very sensitive to an Alzheimer’s patient versus [people without the disease] and also very sensitive to mild cognitive impairment, early stage and moderate Alzheimer's.” Based on their learnings, Canary is able to classify the disease stage very quickly, O’Connell said.
“When we’re listening to sublanguage elements, we’re really analyzing the direct result of changes in the brain in the physical body,” O’Connell said. “The brain controls your vocal cords: how fast they vibrate, the expansion of them, the contraction.” These factors, along with where people put their tongues when talking, function subconsciously and result in subtle changes in the sounds of speech.
Further testing is needed
In an initial trial, Canary analyzed speech recordings from phone calls to a large U.S. health insurer. They looked at the audio recordings of 651 policyholders who had early stage Alzheimer’s and 1018 who did not have the condition, aiming for a representative sample of age, gender and race. They used this data to create their first diagnostic model and found that it was 96 percent accurate in identifying Alzheimer’s.
Christian Herff, an assistant professor of neuroscience at Maastricht University in the Netherlands, praised this approach while adding that further testing is needed to assess its effectiveness.
“I think the general idea of identifying increased risk for cognitive impairment based on speech characteristics is very feasible, particularly when change in a user’s voice is monitored, for example, by recording speech every year,” Herff said. He noted that this can only be a first indication, not a full diagnosis. The accuracy still needs to be validated in studies that follows individuals over a period of time, he said.
Toby Walsh, a professor of artificial intelligence at the University of New South Wales, also thinks Canary’s tool has potential but highlights that Canary could diagnose some people who don’t really have the disease. “This is an interesting and promising application of AI,” he said, “but these tools need to be used carefully. Imagine the anxiety of being misdiagnosed with Alzheimer’s.”
As with many other AI tools, privacy and bias are additional issues to monitor closely, Walsh said.
Other languages
A related issue is that not everyone is fluent in English. Mahnaz Arvaneh, a senior lecturer in automatic control and systems engineering at the University of Sheffield, said this could be a blind spot.
“The system may not be very accurate for those who have English as their second language as their speaking patterns would be different, and any issue might be because of language deficiency rather than cognitive issues,” Arvaneh said.
The team is expanding to multiple languages starting with Japanese and Spanish. The elements of the model that make up the algorithm are very similar, but they need to be validated and retrained in a different language, which will require access to more data.
Recently, Canary analyzed the phone calls of 233 Japanese patients who had mild cognitive impairment and 704 healthy people. Using an English model they were able to identify the Japanese patients who had mild cognitive impairment with 78 percent accuracy. They also developed a model in Japanese that was 45 percent accurate, and they’re continuing to train it with more data.
The future
Canary is using their model to look at other diseases like Huntington’s and Parkinson’s. They’re also collaborating with pharmaceuticals to validate potential therapies for Alzheimer’s. By looking at speech patterns over time, Canary can get an indication of how well these drugs are working.
Dave Arnold and his wife dance at his nephew’s wedding in Rochester, New York, ten years ago, before his Alzheimer's diagnosis.
Dave Arnold
Ultimately, they want to integrate their tool into everyday life. “We want it to be used in a smartphone, or a teleconference call so that individuals could be examined in their home,” O’Connell said. “We could follow them over time and work with clinical teams and hospitals to improve the evaluation of patients and contribute towards an accurate diagnosis.”
Arnold, the patient with early stage Alzheimer’s, sees great promise. “The process of getting a diagnosis is already filled with so much anxiety,” he said. “Anything that can be done to make it easier and less stressful would be a good thing, as long as it’s proven accurate.”