How mRNA Could Revolutionize Medicine
In November 2020, messenger RNA catapulted into the public consciousness when the first COVID-19 vaccines were authorized for emergency use. Around the same time, an equally groundbreaking yet relatively unheralded application of mRNA technology was taking place at a London hospital.
Over the past two decades, there's been increasing interest in harnessing mRNA — molecules present in all of our cells that act like digital tape recorders, copying instructions from DNA in the cell nucleus and carrying them to the protein-making structures — to create a whole new class of therapeutics.
Scientists realized that artificial mRNA, designed in the lab, could be used to instruct our cells to produce certain antibodies, turning our bodies into vaccine-making factories, or to recognize and attack tumors. More recently, researchers recognized that mRNA could also be used to make another groundbreaking technology far more accessible to more patients: gene editing. The gene-editing tool CRISPR has generated plenty of hype for its potential to cure inherited diseases. But delivering CRISPR to the body is complicated and costly.
"Most gene editing involves taking cells out of the patient, treating them and then giving them back, which is an extremely expensive process," explains Drew Weissman, professor of medicine at the University of Pennsylvania, who was involved in developing the mRNA technology behind the COVID-19 vaccines.
But last November, a Massachusetts-based biotech company called Intellia Therapeutics showed it was possible to use mRNA to make the CRISPR system inside the body, eliminating the need to extract cells out of the body and edit them in a lab. Just as mRNA can instruct our cells to produce antibodies against a viral infection, it can also teach them to produce one of the two components that make up CRISPR — a cutting protein that snips out a problem gene.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies."
In Intellia's London-based clinical trial, the company applied this for the first time in a patient with a rare inherited liver disease known as hereditary transthyretin amyloidosis with polyneuropathy. The disease causes a toxic protein to build up in a person's organs and is typically fatal. In a company press release, Intellia's president and CEO John Leonard swiftly declared that its mRNA-based CRISPR therapy could usher in a "new era of potential genome editing cures."
Weissman predicts that turning CRISPR into an affordable therapy will become the next major frontier for mRNA over the coming decade. His lab is currently working on an mRNA-based CRISPR treatment for sickle cell disease. More than 300,000 babies are born with sickle cell every year, mainly in lower income nations.
"There is a FDA-approved cure, but it involves taking the bone marrow out of the person, and then giving it back which is prohibitively expensive," he says. It also requires a patient to have a matched bone marrow done. "We give an intravenous injection of mRNA lipid nanoparticles that target CRISPR to the bone marrow stem cells in the patient, which is easy, and much less expensive."
Cancer Immunotherapy
Meanwhile, the overwhelming success of the COVID-19 vaccines has focused attention on other ways of using mRNA to bolster the immune system against threats ranging from other infectious diseases to cancer.
The practicality of mRNA vaccines – relatively small quantities are required to induce an antibody response – coupled with their adaptable design, mean companies like Moderna are now targeting pathogens like Zika, chikungunya and cytomegalovirus, or CMV, which previously considered commercially unviable for vaccine developers. This is because outbreaks have been relatively sporadic, and these viruses mainly affect people in low-income nations who can't afford to pay premium prices for a vaccine. But mRNA technology means that jabs could be produced on a flexible basis, when required, at relatively low cost.
Other scientists suggest that mRNA could even provide a means of developing a universal influenza vaccine, a goal that's long been the Holy Grail for vaccinologists around the world.
"The mRNA technology allows you to pick out bits of the virus that you want to induce immunity to," says Michael Mulqueen, vice president of business development at eTheRNA, a Belgium-based biotech that's developing mRNA-based vaccines for malaria and HIV, as well as various forms of cancer. "This means you can get the immune system primed to the bits of the virus that don't vary so much between strains. So you could actually have a single vaccine that protects against a whole raft of different variants of the same virus, offering more universal coverage."
Before mRNA became synonymous with vaccines, its biggest potential was for cancer treatments. BioNTech, the German biotech company that collaborated with Pfizer to develop the first authorized COVID-19 vaccine, was initially founded to utilize mRNA for personalized cancer treatments, and the company remains interested in cancers ranging from melanoma to breast cancer.
One of the major hurdles in treating cancer has been the fact that tumors can look very different from one person to the next. It's why conventional approaches, such as chemotherapy or radiation, don't work for every patient. But weaponizing mRNA against cancer primes the immune cells with the tumor's specific genetic sequence, training the patient's body to attack their own unique type of cancer.
"It means you're able to think about personalizing cancer treatments down to specific subgroups of patients," says Mulqueen. "For example, eTheRNA are developing a renal cell carcinoma treatment which will be targeted at around 20% of these patients, who have specific tumor types. We're hoping to take that to human trials next year, but the challenge is trying to identify the right patients for the treatment at an early stage."
Repairing Damaged mRNA
While hopes are high that mRNA could usher in new cancer treatments and make CRISPR more accessible, a growing number of companies are also exploring an alternative to gene editing, known as RNA editing.
In genetic disorders, the mRNA in certain cells is impaired due to a rogue gene defect, and so the body ceases to produce a particular vital protein. Instead of permanently deleting the problem gene with CRISPR, the idea behind RNA editing is to inject small pieces of synthetic mRNA to repair the existing mRNA. Scientists think this approach will allow normal protein production to resume.
Over the past few years, this approach has gathered momentum, as some researchers have recognized that it holds certain key advantages over CRISPR. Companies from Belgium to Japan are now looking at RNA editing to treat all kinds of disorders, from Huntingdon's disease, to amyotrophic lateral sclerosis, or ALS, and certain types of cancer.
"With RNA editing, you don't need to make any changes to the DNA," explains Daniel de Boer, CEO of Dutch biotech ProQR, which is looking to treat rare genetic disorders that cause blindness. "Changes to the DNA are permanent, so if something goes wrong, that may not be desirable. With RNA editing, it's a temporary change, so we dose patients with our drugs once or twice a year."
Last month, ProQR reported a landmark case study, in which a patient with a rare form of blindness called Leber congenital amaurosis, which affects the retina at the back of the eye, recovered vision after three months of treatment.
"We have seen that this RNA therapy restores vision in people that were completely blind for a year or so," says de Boer. "They were able to see again, to read again. We think there are a large number of other genetic diseases we could go after with this technology. There are thousands of different mutations that can lead to blindness, and we think this technology can target approximately 25% of them."
Ultimately, there's likely to be a role for both RNA editing and CRISPR, depending on the disease. "I think CRISPR is ideally suited for illnesses where you would like to permanently correct a genetic defect," says Joshua Rosenthal of the Marine Biology Laboratory in Chicago. "Whereas RNA editing could be used to treat things like pain, where you might want to reset a neural circuit temporarily over a shorter period of time."
Much of this research has been accelerated by the COVID-19 pandemic, which has played a major role in bringing mRNA to the forefront of people's minds as a therapeutic.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies," says Mulqueen. "In the future, I would not be surprised if many of the top pharma products are mRNA derived."
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.