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
The Case for an Outright Ban on Facial Recognition Technology
[Editor's Note: This essay is in response to our current Big Question, which we posed to experts with different perspectives: "Do you think the use of facial recognition technology by the police or government should be banned? If so, why? If not, what limits, if any, should be placed on its use?"]
In a surprise appearance at the tail end of Amazon's much-hyped annual product event last month, CEO Jeff Bezos casually told reporters that his company is writing its own facial recognition legislation.
The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct.
It seems that when you're the wealthiest human alive, there's nothing strange about your company––the largest in the world profiting from the spread of face surveillance technology––writing the rules that govern it.
But if lawmakers and advocates fall into Silicon Valley's trap of "regulating" facial recognition and other forms of invasive biometric surveillance, that's exactly what will happen.
Industry-friendly regulations won't fix the dangers inherent in widespread use of face scanning software, whether it's deployed by governments or for commercial purposes. The use of this technology in public places and for surveillance purposes should be banned outright, and its use by private companies and individuals should be severely restricted. As artificial intelligence expert Luke Stark wrote, it's dangerous enough that it should be outlawed for "almost all practical purposes."
Like biological or nuclear weapons, facial recognition poses such a profound threat to the future of humanity and our basic rights that any potential benefits are far outweighed by the inevitable harms.
We live in cities and towns with an exponentially growing number of always-on cameras, installed in everything from cars to children's toys to Amazon's police-friendly doorbells. The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct. It's a world where nearly everything we do, everywhere we go, everyone we associate with, and everything we buy — or look at and even think of buying — is recorded and can be tracked and analyzed at a mass scale for unimaginably awful purposes.
Biometric tracking enables the automated and pervasive monitoring of an entire population. There's ample evidence that this type of dragnet mass data collection and analysis is not useful for public safety, but it's perfect for oppression and social control.
Law enforcement defenders of facial recognition often state that the technology simply lets them do what they would be doing anyway: compare footage or photos against mug shots, drivers licenses, or other databases, but faster. And they're not wrong. But the speed and automation enabled by artificial intelligence-powered surveillance fundamentally changes the impact of that surveillance on our society. Being able to do something exponentially faster, and using significantly less human and financial resources, alters the nature of that thing. The Fourth Amendment becomes meaningless in a world where private companies record everything we do and provide governments with easy tools to request and analyze footage from a growing, privately owned, panopticon.
Tech giants like Microsoft and Amazon insist that facial recognition will be a lucrative boon for humanity, as long as there are proper safeguards in place. This disingenuous call for regulation is straight out of the same lobbying playbook that telecom companies have used to attack net neutrality and Silicon Valley has used to scuttle meaningful data privacy legislation. Companies are calling for regulation because they want their corporate lawyers and lobbyists to help write the rules of the road, to ensure those rules are friendly to their business models. They're trying to skip the debate about what role, if any, technology this uniquely dangerous should play in a free and open society. They want to rush ahead to the discussion about how we roll it out.
We need spaces that are free from government and societal intrusion in order to advance as a civilization.
Facial recognition is spreading very quickly. But backlash is growing too. Several cities have already banned government entities, including police and schools, from using biometric surveillance. Others have local ordinances in the works, and there's state legislation brewing in Michigan, Massachusetts, Utah, and California. Meanwhile, there is growing bipartisan agreement in U.S. Congress to rein in government use of facial recognition. We've also seen significant backlash to facial recognition growing in the U.K., within the European Parliament, and in Sweden, which recently banned its use in schools following a fine under the General Data Protection Regulation (GDPR).
At least two frontrunners in the 2020 presidential campaign have backed a ban on law enforcement use of facial recognition. Many of the largest music festivals in the world responded to Fight for the Future's campaign and committed to not use facial recognition technology on music fans.
There has been widespread reporting on the fact that existing facial recognition algorithms exhibit systemic racial and gender bias, and are more likely to misidentify people with darker skin, or who are not perceived by a computer to be a white man. Critics are right to highlight this algorithmic bias. Facial recognition is being used by law enforcement in cities like Detroit right now, and the racial bias baked into that software is doing harm. It's exacerbating existing forms of racial profiling and discrimination in everything from public housing to the criminal justice system.
But the companies that make facial recognition assure us this bias is a bug, not a feature, and that they can fix it. And they might be right. Face scanning algorithms for many purposes will improve over time. But facial recognition becoming more accurate doesn't make it less of a threat to human rights. This technology is dangerous when it's broken, but at a mass scale, it's even more dangerous when it works. And it will still disproportionately harm our society's most vulnerable members.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites.
We need spaces that are free from government and societal intrusion in order to advance as a civilization. If technology makes it so that laws can be enforced 100 percent of the time, there is no room to test whether those laws are just. If the U.S. government had ubiquitous facial recognition surveillance 50 years ago when homosexuality was still criminalized, would the LGBTQ rights movement ever have formed? In a world where private spaces don't exist, would people have felt safe enough to leave the closet and gather, build community, and form a movement? Freedom from surveillance is necessary for deviation from social norms as well as to dissent from authority, without which societal progress halts.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites. Drawing a line in the sand around tech-enhanced surveillance is the fundamental fight of this generation. Lining up to get our faces scanned to participate in society doesn't just threaten our privacy, it threatens our humanity, and our ability to be ourselves.
[Editor's Note: Read the opposite perspective here.]
Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
Applied mathematician Sara del Valle works at the U.S.'s foremost nuclear weapons lab: Los Alamos. Once colloquially called Atomic City, it's a hidden place 45 minutes into the mountains northwest of Santa Fe. Here, engineers developed the first atomic bomb.
Like AccuWeather, an app for disease prediction could help people alter their behavior to live better lives.
Today, Los Alamos still a small science town, though no longer a secret, nor in the business of building new bombs. Instead, it's tasked with, among other things, keeping the stockpile of nuclear weapons safe and stable: not exploding when they're not supposed to (yes, please) and exploding if someone presses that red button (please, no).
Del Valle, though, doesn't work on any of that. Los Alamos is also interested in other kinds of booms—like the explosion of a contagious disease that could take down a city. Predicting (and, ideally, preventing) such epidemics is del Valle's passion. She hopes to develop an app that's like AccuWeather for germs: It would tell you your chance of getting the flu, or dengue or Zika, in your city on a given day. And like AccuWeather, it could help people alter their behavior to live better lives, whether that means staying home on a snowy morning or washing their hands on a sickness-heavy commute.
Sara del Valle of Los Alamos is working to predict and prevent epidemics using data and machine learning.
Since the beginning of del Valle's career, she's been driven by one thing: using data and predictions to help people behave practically around pathogens. As a kid, she'd always been good at math, but when she found out she could use it to capture the tentacular spread of disease, and not just manipulate abstractions, she was hooked.
When she made her way to Los Alamos, she started looking at what people were doing during outbreaks. Using social media like Twitter, Google search data, and Wikipedia, the team started to sift for trends. Were people talking about hygiene, like hand-washing? Or about being sick? Were they Googling information about mosquitoes? Searching Wikipedia for symptoms? And how did those things correlate with the spread of disease?
It was a new, faster way to think about how pathogens propagate in the real world. Usually, there's a 10- to 14-day lag in the U.S. between when doctors tap numbers into spreadsheets and when that information becomes public. By then, the world has moved on, and so has the disease—to other villages, other victims.
"We found there was a correlation between actual flu incidents in a community and the number of searches online and the number of tweets online," says del Valle. That was when she first let herself dream about a real-time forecast, not a 10-days-later backcast. Del Valle's group—computer scientists, mathematicians, statisticians, economists, public health professionals, epidemiologists, satellite analysis experts—has continued to work on the problem ever since their first Twitter parsing, in 2011.
They've had their share of outbreaks to track. Looking back at the 2009 swine flu pandemic, they saw people buying face masks and paying attention to the cleanliness of their hands. "People were talking about whether or not they needed to cancel their vacation," she says, and also whether pork products—which have nothing to do with swine flu—were safe to buy.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place.
They watched internet conversations during the measles outbreak in California. "There's a lot of online discussion about anti-vax sentiment, and people trying to convince people to vaccinate children and vice versa," she says.
Today, they work on predicting the spread of Zika, Chikungunya, and dengue fever, as well as the plain old flu. And according to the CDC, that latter effort is going well.
Since 2015, the CDC has run the Epidemic Prediction Initiative, a competition in which teams like de Valle's submit weekly predictions of how raging the flu will be in particular locations, along with other ailments occasionally. Michael Johannson is co-founder and leader of the program, which began with the Dengue Forecasting Project. Its goal, he says, was to predict when dengue cases would blow up, when previously an area just had a low-level baseline of sick people. "You'll get this massive epidemic where all of a sudden, instead of 3,000 to 4,000 cases, you have 20,000 cases," he says. "They kind of come out of nowhere."
But the "kind of" is key: The outbreaks surely come out of somewhere and, if scientists applied research and data the right way, they could forecast the upswing and perhaps dodge a bomb before it hit big-time. Questions about how big, when, and where are also key to the flu.
A big part of these projects is the CDC giving the right researchers access to the right information, and the structure to both forecast useful public-health outcomes and to compare how well the models are doing. The extra information has been great for the Los Alamos effort. "We don't have to call departments and beg for data," says del Valle.
When data isn't available, "proxies"—things like symptom searches, tweets about empty offices, satellite images showing a green, wet, mosquito-friendly landscape—are helpful: You don't have to rely on anyone's health department.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place. But del Valle wants more than weekly numbers on a government website; she wants that weather-app-inspired fortune-teller, incorporating the many diseases you could get today, standing right where you are. "That's our dream," she says.
This plot shows the the correlations between the online data stream, from Wikipedia, and various infectious diseases in different countries. The results of del Valle's predictive models are shown in brown, while the actual number of cases or illness rates are shown in blue.
(Courtesy del Valle)
The goal isn't to turn you into a germophobic agoraphobe. It's to make you more aware when you do go out. "If you know it's going to rain today, you're more likely to bring an umbrella," del Valle says. "When you go on vacation, you always look at the weather and make sure you bring the appropriate clothing. If you do the same thing for diseases, you think, 'There's Zika spreading in Sao Paulo, so maybe I should bring even more mosquito repellent and bring more long sleeves and pants.'"
They're not there yet (don't hold your breath, but do stop touching your mouth). She estimates it's at least a decade away, but advances in machine learning could accelerate that hypothetical timeline. "We're doing baby steps," says del Valle, starting with the flu in the U.S., dengue in Brazil, and other efforts in Colombia, Ecuador, and Canada. "Going from there to forecasting all diseases around the globe is a long way," she says.
But even AccuWeather started small: One man began predicting weather for a utility company, then helping ski resorts optimize their snowmaking. His influence snowballed, and now private forecasting apps, including AccuWeather's, populate phones across the planet. The company's progression hasn't been without controversy—privacy incursions, inaccuracy of long-term forecasts, fights with the government—but it has continued, for better and for worse.
Disease apps, perhaps spun out of a small, unlikely team at a nuclear-weapons lab, could grow and breed in a similar way. And both the controversies and public-health benefits that may someday spin out of them lie in the future, impossible to predict with certainty.