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
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.