Researchers Are Testing a New Stem Cell Therapy in the Hopes of Saving Millions from Blindness
Of all the infirmities of old age, failing sight is among the cruelest. It can mean the end not only of independence, but of a whole spectrum of joys—from gazing at a sunset or a grandchild's face to reading a novel or watching TV.
The Phase 1 trial will likely run through 2022, followed by a larger Phase 2 trial that could last another two or three years.
The leading cause of vision loss in people over 55 is age-related macular degeneration, or AMD, which afflicts an estimated 11 million Americans. As photoreceptors in the macula (the central part of the retina) die off, patients experience increasingly severe blurring, dimming, distortions, and blank spots in one or both eyes.
The disorder comes in two varieties, "wet" and "dry," both driven by a complex interaction of genetic, environmental, and lifestyle factors. It begins when deposits of cellular debris accumulate beneath the retinal pigment epithelium (RPE)—a layer of cells that nourish and remove waste products from the photoreceptors above them. In wet AMD, this process triggers the growth of abnormal, leaky blood vessels that damage the photoreceptors. In dry AMD, which accounts for 80 to 90 percent of cases, RPE cells atrophy, causing photoreceptors to wither away. Wet AMD can be controlled in about a quarter of patients, usually by injections of medication into the eye. For dry AMD, no effective remedy exists.
Stem Cells: Promise and Perils
Over the past decade, stem cell therapy has been widely touted as a potential treatment for AMD. The idea is to augment a patient's ailing RPE cells with healthy ones grown in the lab. A few small clinical trials have shown promising results. In a study published in 2018, for example, a University of Southern California team cultivated RPE tissue from embryonic stem cells on a plastic matrix and transplanted it into the retinas of four patients with advanced dry AMD. Because the trial was designed to test safety rather than efficacy, lead researcher Amir Kashani told a reporter, "we didn't expect that replacing RPE cells would return a significant amount of vision." Yet acuity improved substantially in one recipient, and the others regained their lost ability to focus on an object.
Therapies based on embryonic stem cells, however, have two serious drawbacks: Using fetal cell lines raises ethical issues, and such treatments require the patient to take immunosuppressant drugs (which can cause health problems of their own) to prevent rejection. That's why some experts favor a different approach—one based on induced pluripotent stem cells (iPSCs). Such cells, first produced in 2006, are made by returning adult cells to an undifferentiated state, and then using chemicals to reprogram them as desired. Treatments grown from a patient's own tissues could sidestep both hurdles associated with embryonic cells.
At least hypothetically. Today, the only stem cell therapies approved by the U.S. Food and Drug Administration (FDA) are umbilical cord-derived products for various blood and immune disorders. Although scientists are probing the use of embryonic stem cells or iPSCs for conditions ranging from diabetes to Parkinson's disease, such applications remain experimental—or fraudulent, as a growing number of patients treated at unlicensed "stem cell clinics" have painfully learned. (Some have gone blind after receiving bogus AMD therapies at those facilities.)
Last December, researchers at the National Eye Institute in Bethesda, Maryland, began enrolling patients with dry AMD in the country's first clinical trial using tissue grown from the patients' own stem cells. Led by biologist Kapil Bharti, the team intends to implant custom-made RPE cells in 12 recipients. If the effort pans out, it could someday save the sight of countless oldsters.
That, however, is what's technically referred to as a very big "if."
The First Steps
Bharti's trial is not the first in the world to use patient-derived iPSCs to treat age-related macular degeneration. In 2013, Japanese researchers implanted such cells into the eyes of a 77-year-old woman with wet AMD; after a year, her vision had stabilized, and she no longer needed injections to keep abnormal blood vessels from forming. A second patient was scheduled for surgery—but the procedure was canceled after the lab-grown RPE cells showed signs of worrisome mutations. That incident illustrates one potential problem with using stem cells: Under some circumstances, the cells or the tissue they form could turn cancerous.
"The knowledge and expertise we're gaining can be applied to many other iPSC-based therapies."
Bharti and his colleagues have gone to great lengths to avoid such outcomes. "Our process is significantly different," he told me in a phone interview. His team begins with patients' blood stem cells, which appear to be more genomically stable than the skin cells that the Japanese group used. After converting the blood cells to RPE stem cells, his team cultures them in a single layer on a biodegradable scaffold, which helps them grow in an orderly manner. "We think this material gives us a big advantage," Bharti says. The team uses a machine-learning algorithm to identify optimal cell structure and ensure quality control.
It takes about six months for a patch of iPSCs to become viable RPE cells. When they're ready, a surgeon uses a specially-designed tool to insert the tiny structure into the retina. Within days, the scaffold melts away, enabling the transplanted RPE cells to integrate fully into their new environment. Bharti's team initially tested their method on rats and pigs with eye damage mimicking AMD. The study, published in January 2019 in Science Translational Medicine, found that at ten weeks, the implanted RPE cells continued to function normally and protected neighboring photoreceptors from further deterioration. No trace of mutagenesis appeared.
Encouraged by these results, Bharti began recruiting human subjects. The Phase 1 trial will likely run through 2022, followed by a larger Phase 2 trial that could last another two or three years. FDA approval would require an even larger Phase 3 trial, with a decision expected sometime between 2025 and 2028—that is, if nothing untoward happens before then. One unknown (among many) is whether implanted cells can thrive indefinitely under the biochemically hostile conditions of an eye with AMD.
"Most people don't have a sense of just how long it takes to get something like this to work, and how many failures—even disasters—there are along the way," says Marco Zarbin, professor and chair of Ophthalmology and visual science at Rutgers New Jersey Medical School and co-editor of the book Cell-Based Therapy for Degenerative Retinal Diseases. "The first kidney transplant was done in 1933. But the first successful kidney transplant was in 1954. That gives you a sense of the time frame. We're really taking the very first steps in this direction."
Looking Ahead
Even if Bharti's method proves safe and effective, there's the question of its practicality. "My sense is that using induced pluripotent stem cells to treat the patient from whom they're derived is a very expensive undertaking," Zarbin observes. "So you'd have to have a very dramatic clinical benefit to justify that cost."
Bharti concedes that the price of iPSC therapy is likely to be high, given that each "dose" is formulated for a single individual, requires months to manufacture, and must be administered via microsurgery. Still, he expects economies of scale and production to emerge with time. "We're working on automating several steps of the process," he explains. "When that kicks in, a technician will be able to make products for 10 or 20 people at once, so the cost will drop proportionately."
Meanwhile, other researchers are pressing ahead with therapies for AMD using embryonic stem cells, which could be mass-produced to treat any patient who needs them. But should that approach eventually win FDA approval, Bharti believes there will still be room for a technique that requires neither fetal cell lines nor immunosuppression.
And not only for eye ailments. "The knowledge and expertise we're gaining can be applied to many other iPSC-based therapies," says the scientist, who is currently consulting with several companies that are developing such treatments. "I'm hopeful that we can leverage these approaches for a wide range of applications, whether it's for vision or across the body."
NEI launches iPS cell therapy trial for dry AMD
Podcast: The Friday Five weekly roundup in health research
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Covered in this week's Friday Five:
- Sex differences in cancer
- Promising research on a vaccine for Lyme disease
- Using a super material for brain-like devices
- Measuring your immunity to Covid
- Reducing dementia risk with leisure activities
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.