Tiny, tough “water bears” may help bring new vaccines and medicines to sub-Saharan Africa
Microscopic tardigrades, widely considered to be some of the toughest animals on earth, can survive for decades without oxygen or water and are thought to have lived through a crash-landing on the moon. Also known as water bears, they survive by fully dehydrating and later rehydrating themselves – a feat only a few animals can accomplish. Now scientists are harnessing tardigrades’ talents to make medicines that can be dried and stored at ambient temperatures and later rehydrated for use—instead of being kept refrigerated or frozen.
Many biologics—pharmaceutical products made by using living cells or synthesized from biological sources—require refrigeration, which isn’t always available in many remote locales or places with unreliable electricity. These products include mRNA and other vaccines, monoclonal antibodies and immuno-therapies for cancer, rheumatoid arthritis and other conditions. Cooling is also needed for medicines for blood clotting disorders like hemophilia and for trauma patients.
Formulating biologics to withstand drying and hot temperatures has been the holy grail for pharmaceutical researchers for decades. It’s a hard feat to manage. “Biologic pharmaceuticals are highly efficacious, but many are inherently unstable,” says Thomas Boothby, assistant professor of molecular biology at University of Wyoming. Therefore, during storage and shipping, they must be refrigerated at 2 to 8 degrees Celsius (35 to 46 degrees Fahrenheit). Some must be frozen, typically at -20 degrees Celsius, but sometimes as low -90 degrees Celsius as was the case with the Pfizer Covid vaccine.
For Covid, fewer than 73 percent of the global population received even one dose. The need for refrigerated or frozen handling was partially to blame.
The costly cold chain
The logistics network that ensures those temperature requirements are met from production to administration is called the cold chain. This cold chain network is often unreliable or entirely lacking in remote, rural areas in developing nations that have malfunctioning electrical grids. “Almost all routine vaccines require a cold chain,” says Christopher Fox, senior vice president of formulations at the Access to Advanced Health Institute. But when the power goes out, so does refrigeration, putting refrigerated or frozen medical products at risk. Consequently, the mRNA vaccines developed for Covid-19 and other conditions, as well as more traditional vaccines for cholera, tetanus and other diseases, often can’t be delivered to the most remote parts of the world.
To understand the scope of the challenge, consider this: In the U.S., more than 984 million doses of Covid-19 vaccine have been distributed so far. Each one needed refrigeration that, even in the U.S., proved challenging. Now extrapolate to all vaccines and the entire world. For Covid, fewer than 73 percent of the global population received even one dose. The need for refrigerated or frozen handling was partially to blame.
Globally, the cold chain packaging market is valued at over $15 billion and is expected to exceed $60 billion by 2033.
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Freeze-drying, also called lyophilization, which is common for many vaccines, isn’t always an option. Many freeze-dried vaccines still need refrigeration, and even medicines approved for storage at ambient temperatures break down in the heat of sub-Saharan Africa. “Even in a freeze-dried state, biologics often will undergo partial rehydration and dehydration, which can be extremely damaging,” Boothby explains.
The cold chain is also very expensive to maintain. The global pharmaceutical cold chain packaging market is valued at more than $15 billion, and is expected to exceed $60 billion by 2033, according to a report by Future Market Insights. This cost is only expected to grow. According to the consulting company Accenture, the number of medicines that require the cold chain are expected to grow by 48 percent, compared to only 21 percent for non-cold-chain therapies.
Tardigrades to the rescue
Tardigrades are only about a millimeter long – with four legs and claws, and they lumber around like bears, thus their nickname – but could provide a big solution. “Tardigrades are unique in the animal kingdom, in that they’re able to survive a vast array of environmental insults,” says Boothby, the Wyoming professor. “They can be dried out, frozen, heated past the boiling point of water and irradiated at levels that are thousands of times more than you or I could survive.” So, his team is gradually unlocking tardigrades’ survival secrets and applying them to biologic pharmaceuticals to make them withstand both extreme heat and desiccation without losing efficacy.
Boothby’s team is focusing on blood clotting factor VIII, which, as the name implies, causes blood to clot. Currently, Boothby is concentrating on the so-called cytoplasmic abundant heat soluble (CAHS) protein family, which is found only in tardigrades, protecting them when they dry out. “We showed we can desiccate a biologic (blood clotting factor VIII, a key clotting component) in the presence of tardigrade proteins,” he says—without losing any of its effectiveness.
The researchers mixed the tardigrade protein with the blood clotting factor and then dried and rehydrated that substance six times without damaging the latter. This suggests that biologics protected with tardigrade proteins can withstand real-world fluctuations in humidity.
Furthermore, Boothby’s team found that when the blood clotting factor was dried and stabilized with tardigrade proteins, it retained its efficacy at temperatures as high as 95 degrees Celsius. That’s over 200 degrees Fahrenheit, much hotter than the 58 degrees Celsius that the World Meteorological Organization lists as the hottest recorded air temperature on earth. In contrast, without the protein, the blood clotting factor degraded significantly. The team published their findings in the journal Nature in March.
Although tardigrades rarely live more than 2.5 years, they have survived in a desiccated state for up to two decades, according to Animal Diversity Web. This suggests that tardigrades’ CAHS protein can protect biologic pharmaceuticals nearly indefinitely without refrigeration or freezing, which makes it significantly easier to deliver them in locations where refrigeration is unreliable or doesn’t exist.
The tricks of the tardigrades
Besides the CAHS proteins, tardigrades rely on a type of sugar called trehalose and some other protectants. So, rather than drying up, their cells solidify into rigid, glass-like structures. As that happens, viscosity between cells increases, thereby slowing their biological functions so much that they all but stop.
Now Boothby is combining CAHS D, one of the proteins in the CAHS family, with trehalose. He found that CAHS D and trehalose each protected proteins through repeated drying and rehydrating cycles. They also work synergistically, which means that together they might stabilize biologics under a variety of dry storage conditions.
“We’re finding the protective effect is not just additive but actually is synergistic,” he says. “We’re keen to see if something like that also holds true with different protein combinations.” If so, combinations could possibly protect against a variety of conditions.
Commercialization outlook
Before any stabilization technology for biologics can be commercialized, it first must be approved by the appropriate regulators. In the U.S., that’s the U.S. Food and Drug Administration. Developing a new formulation would require clinical testing and vast numbers of participants. So existing vaccines and biologics likely won’t be re-formulated for dry storage. “Many were developed decades ago,” says Fox. “They‘re not going to be reformulated into thermo-stable vaccines overnight,” if ever, he predicts.
Extending stability outside the cold chain, even for a few days, can have profound health, environmental and economic benefits.
Instead, this technology is most likely to be used for the new products and formulations that are just being created. New and improved vaccines will be the first to benefit. Good candidates include the plethora of mRNA vaccines, as well as biologic pharmaceuticals for neglected diseases that affect parts of the world where reliable cold chain is difficult to maintain, Boothby says. Some examples include new, more effective vaccines for malaria and for pathogenic Escherichia coli, which causes diarrhea.
Tallying up the benefits
Extending stability outside the cold chain, even for a few days, can have profound health, environmental and economic benefits. For instance, MenAfriVac, a meningitis vaccine (without tardigrade proteins) developed for sub-Saharan Africa, can be stored at up to 40 degrees Celsius for four days before administration. “If you have a few days where you don’t need to maintain the cold chain, it’s easier to transport vaccines to remote areas,” Fox says, where refrigeration does not exist or is not reliable.
Better health is an obvious benefit. MenAfriVac reduced suspected meningitis cases by 57 percent in the overall population and more than 99 percent among vaccinated individuals.
Lower healthcare costs are another benefit. One study done in Togo found that the cold chain-related costs increased the per dose vaccine price up to 11-fold. The ability to ship the vaccines using the usual cold chain, but transporting them at ambient temperatures for the final few days cut the cost in half.
There are environmental benefits, too, such as reducing fuel consumption and greenhouse gas emissions. Cold chain transports consume 20 percent more fuel than non-cold chain shipping, due to refrigeration equipment, according to the International Trade Administration.
A study by researchers at Johns Hopkins University compared the greenhouse gas emissions of the new, oral Vaxart COVID-19 vaccine (which doesn’t require refrigeration) with four intramuscular vaccines (which require refrigeration or freezing). While the Vaxart vaccine is still in clinical trials, the study found that “up to 82.25 million kilograms of CO2 could be averted by using oral vaccines in the U.S. alone.” That is akin to taking 17,700 vehicles out of service for one year.
Although tardigrades’ protective proteins won’t be a component of biologic pharmaceutics for several years, scientists are proving that this approach is viable. They are hopeful that a day will come when vaccines and biologics can be delivered anywhere in the world without needing refrigerators or freezers en route.
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.