Medical Breakthroughs Set to be Fast-Tracked by Innovative New Health Agency
In 2007, Matthew Might's son, Bertrand, was born with a life-threatening disease that was so rare, doctors couldn't diagnose it. Might, a computer scientist and biologist, eventually realized, "Oh my gosh, he's the only patient in the world with this disease right now." To find effective treatments, new methodologies would need to be developed. But there was no process or playbook for doing that.
Might took it upon himself, along with a team of specialists, to try to find a cure. "What Bertrand really taught me was the visceral sense of urgency when there's suffering, and how to act on that," he said.
He calls it "the agency of urgency"—and patients with more common diseases, such as cancer and Alzheimer's, often feel that same need to take matters into their own hands, as they find their hopes for new treatments running up against bureaucratic systems designed to advance in small, steady steps, not leaps and bounds. "We all hope for a cure," said Florence "Pippy" Rogers, a 65-year-old volunteer with Georgia's chapter of the Alzheimer's Association. She lost her mother to the disease and, these days, worries about herself and her four siblings. "We need to keep accelerating research."
We have a fresh example of what can be achieved by fast-tracking discoveries in healthcare: Covid-19 vaccines.
President Biden has pushed for cancer moonshots since the disease took the life of his son, Beau, in 2015. His administration has now requested $6.5 billion to start a new agency in 2022, called the Advanced Research Projects Agency for Health, or ARPA-H, within the National Institutes of Health. It's based on DARPA, the Department of Defense agency known for hatching world-changing technologies such as drones, GPS and ARPANET, which became the internet.
We have a fresh example of what can be achieved by fast-tracking discoveries in healthcare: Covid-19 vaccines. "Operation Warp Speed was using ARPA-like principles," said Might. "It showed that in a moment of crisis, institutions like NIH can think in an ARPA-like way. So now the question is, why don't we do that all the time?"
But applying the DARPA model to health involves several challenging decisions. I asked experts what could be the hardest question facing advocates of ARPA-H: which health problems it should seek to address. "All the wonderful choices lead to the problem of which ones to choose and prioritize," said Sudip Parikh, CEO of the American Association for the Advancement of Science and executive publisher of the Science family of journals. "There is no objectively right answer."
The Agency of Urgency
ARPA-H will borrow at least three critical ingredients from DARPA: goal-oriented project managers, many from industry; aggressive public-private partnerships; and collaboration among fields that don't always interact. The DARPA concept has been applied to other purposes, including energy and homeland security, with promising results. "We're learning that 'ARPA-ism' is a franchisable model," said Might, a former principal investigator on DARPA projects.
The federal government already pours billions of dollars into advancing research on life-threatening diseases, with much of it channeled through the National Institutes of Health. But the purpose of ARPA-H "isn't just the usual suspects that NIH would fund," said David Walt, a Harvard biochemist, an innovator in gene sequencing and former chair of DARPA's Defense Science Research Council. Whereas some NIH-funded studies aim to gradually improve our understanding of diseases, ARPA-H projects will give full focus to real-world applications; they'll use essential findings from NIH research as starting points, drawing from them to rapidly engineer new technologies that could save lives.
And, ultimately, billions in healthcare costs, if ARPA-H lives up to its predecessor's track record; DARPA's breakthroughs have been economic game-changers, while its fail-fast approach—quickly pulling the plug on projects that aren't panning out—helps to avoid sunken costs. ARPA-H could fuel activities similar to the human genome project, which used existing research to map the base pairs that make up DNA, opening new doors for the biotech industry, sparking economic growth and creating hundreds of thousands of new jobs.
Despite a nearly $4 trillion health economy, "we aren't innovating when it comes to technological capabilities for health," said Liz Feld, president of the Suzanne Wright Foundation for pancreatic cancer.
Individual Diseases Ripe for Innovation
Although the need for innovation is clear, which diseases ARPA-H should tackle is less apparent. One important consideration when choosing health priorities could be "how many people suffer from a disease," said Nancy Kass, a professor of bioethics and public health at Johns Hopkins.
That perspective could justify cancer as a top objective. Cancer and heart disease have long been the two major killers in the U.S. Leonidas Platanias, professor of oncology at Northwestern and director of its cancer center, noted that we've already made significant progress on heart disease. "Anti-cholesterol drugs really have a wide impact," he said. "I don't want to compare one disease to another, but I think cancer may be the most challenging. We need even bigger breakthroughs." He wondered whether ARPA-H should be linked to the part of NIH dedicated to cancer, the National Cancer Institute, "to take maximum advantage of what happens" there.
Previous cancer moonshots have laid a foundation for success. And this sort of disease-by-disease approach makes sense in a way. "We know that concentrating on some diseases has led to treatments," said Parikh. "Think of spinal muscular atrophy or cystic fibrosis. Now, imagine if immune therapies were discovered ten years earlier."
But many advocates think ARPA-H should choose projects that don't revolve around any one disease. "It absolutely has to be disease agnostic," said Feld, president of the pancreatic cancer foundation. "We cannot reach ARPA-H's potential if it's subject to the advocacy of individual patient groups who think their disease is worse than the guy's disease next to them. That's not the way the DARPA model works." Platanias agreed that ARPA-H should "pick the highest concepts and developments that have the best chance" of success.
Finding Connections Between Diseases
Kass, the Hopkins bioethicist, believes that ARPA-H should walk a balance, with some projects focusing on specific diseases and others aspiring to solutions with broader applications, spanning multiple diseases. Being impartial, some have noted, might involve looking at the total "life years" saved by a health innovation; the more diseases addressed by a given breakthrough, the more years of healthy living it may confer. The social and economic value should increase as well.
For multiple payoffs, ARPA-H could concentrate on rare diseases, which can yield important insights for many other diseases, said Might. Every case of cancer and Alzheimer's is, in a way, its own rare disease. Cancer is a genetic disease, like his son Bertrand's rare disorder, and mutations vary widely across cancer patients. "It's safe to say that no two people have ever actually had the same cancer," said Might. In theory, solutions for rare diseases could help us understand how to individualize treatments for more common diseases.
Many experts I talked with support another priority for ARPA-H with implications for multiple diseases: therapies that slow down the aging process. "Aging is the greatest risk factor for every major disease that NIH is studying," said Matt Kaeberlein, a bio-gerontologist at the University of Washington. Yet, "half of one percent of the NIH budget goes to researching the biology of aging. An ARPA-H sized budget would push the field forward at a pace that's hard to imagine."
Might agreed. "It could take ARPA-H to get past the weird stigmas around aging-related research. It could have a tremendous impact on the field."
For example, ARPA-H could try to use mRNA technology to express proteins that affect biological aging, said Kaeberlein. It's an engineering project well-suited to the DARPA model. So is harnessing machine learning to identify biomarkers that assess how fast people are aging. Biological aging clocks, if validated, could quickly reveal whether proposed therapies for aging are working or not. "I think there's huge value in that," said Kaeberlein.
By delivering breakthroughs in computation, ARPA-H could improve diagnostics for many different diseases. That could include improving biowearables for continuously monitoring blood pressure—a hypothetical mentioned in the White House's concept paper on ARPA-H—and advanced imaging technologies. "The high cost of medical imaging is a leading reason why our healthcare costs are the highest in the world," said Feld. "There's no detection test for ALS. No brain detection for Alzheimer's. Innovations in detection technology would save on cost and human suffering."
Some biotech companies may be skeptical about the financial rewards of accelerating such technologies. But ARPA-H could fund public-private partnerships to "de-risk" biotech's involvement—an incentive that harkens back to the advance purchase contracts that companies got during Covid. (Some groups have suggested that ARPA-H could provide advance purchase agreements.)
Parikh is less bullish on creating diagnostics through ARPA-H. Like DARPA, Biden's health agency will enjoy some independence from federal oversight; it may even be located hundreds of miles from DC. That freedom affords some breathing room for innovation, but it could also make it tougher to ensure that algorithms fully consider diverse populations. "That part I really would like the government more involved in," Parikh said.
Might thinks ARPA-H should also explore innovations in clinical trials, which many patients and medical communities view as grindingly slow and requiring too many participants. "We can approve drugs for very tiny patient populations, even at the level of the individual," he said, while emphasizing the need for safety. But Platanias thinks the FDA has become much more flexible in recent years. In the cancer field, at least, "You now see faster approvals for more drugs. Having [more] shortcuts on clinical trial approvals is not necessarily a good idea."
With so many options on the table, ARPA-H needs to show the public a clear framework for measuring the value of potential projects. Kass warned that well-resourced advocates could skew the agency's priorities. They've affected health outcomes before, she noted; fundraising may partly explain larger increases in life expectancy for cystic fibrosis than sickle cell anemia. Engaging diverse communities is a must for ARPA-H. So are partnerships to get the agency's outputs to people who need them. "Research is half the equation," said Kass. "If we don't ensure implementation and access, who cares." The White House concept paper on ARPA-H made a similar point.
As Congress works on authorizing ARPA-H this year, Might is doing what he can to ensure better access to innovation on a patient-by-patient basis. Last year, his son, Bertrand, passed away suddenly from his disorder. He was 12. But Might's sense of urgency has persisted, as he directs the Precision Medicine Institute at the University of Alabama-Birmingham. That urgency "can be carried into an agency like ARPA-H," he said. "It guides what I do as I apply for funding, because I'm trying to build the infrastructure that other parents need. So they don't have to build it from scratch like I did."
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.