Researchers advance drugs that treat pain without addiction
Opioids are one of the most common ways to treat pain. They can be effective but are also highly addictive, an issue that has fueled the ongoing opioid crisis. In 2020, an estimated 2.3 million Americans were dependent on prescription opioids.
Opioids bind to receptors at the end of nerve cells in the brain and body to prevent pain signals. In the process, they trigger endorphins, so the brain constantly craves more. There is a huge risk of addiction in patients using opioids for chronic long-term pain. Even patients using the drugs for acute short-term pain can become dependent on them.
Scientists have been looking for non-addictive drugs to target pain for over 30 years, but their attempts have been largely ineffective. “We desperately need alternatives for pain management,” says Stephen E. Nadeau, a professor of neurology at the University of Florida.
A “dimmer switch” for pain
Paul Blum is a professor of biological sciences at the University of Nebraska. He and his team at Neurocarrus have created a drug called N-001 for acute short-term pain. N-001 is made up of specially engineered bacterial proteins that target the body’s sensory neurons, which send pain signals to the brain. The proteins in N-001 turn down pain signals, but they’re too large to cross the blood-brain barrier, so they don’t trigger the release of endorphins. There is no chance of addiction.
When sensory neurons detect pain, they become overactive and send pain signals to the brain. “We wanted a way to tone down sensory neurons but not turn them off completely,” Blum reveals. The proteins in N-001 act “like a dimmer switch, and that's key because pain is sensation overstimulated.”
Blum spent six years developing the drug. He finally managed to identify two proteins that form what’s called a C2C complex that changes the structure of a subunit of axons, the parts of neurons that transmit electrical signals of pain. Changing the structure reduces pain signaling.
“It will be a long path to get to a successful clinical trial in humans," says Stephen E. Nadeau, professor of neurology at the University of Florida. "But it presents a very novel approach to pain reduction.”
Blum is currently focusing on pain after knee and ankle surgery. Typically, patients are treated with anesthetics for a short time after surgery. But anesthetics usually only last for 4 to 6 hours, and long-term use is toxic. For some, the pain subsides. Others continue to suffer after the anesthetics have worn off and start taking opioids.
N-001 numbs sensation. It lasts for up to 7 days, much longer than any anesthetic. “Our goal is to prolong the time before patients have to start opioids,” Blum says. “The hope is that they can switch from an anesthetic to our drug and thereby decrease the likelihood they're going to take the opioid in the first place.”
Their latest animal trial showed promising results. In mice, N-001 reduced pain-like behaviour by 90 percent compared to the control group. One dose became effective in two hours and lasted a week. A high dose had pain-relieving effects similar to an opioid.
Professor Stephen P. Cohen, director of pain operations at John Hopkins, believes the Neurocarrus approach has potential but highlights the need to go beyond animal testing. “While I think it's promising, it's an uphill battle,” he says. “They have shown some efficacy comparable to opioids, but animal studies don't translate well to people.”
Nadeau, the University of Florida neurologist, agrees. “It will be a long path to get to a successful clinical trial in humans. But it presents a very novel approach to pain reduction.”
Blum is now awaiting approval for phase I clinical trials for acute pain. He also hopes to start testing the drug's effect on chronic pain.
Learning from people who feel no pain
Like Blum, a pharmaceutical company called Vertex is focusing on treating acute pain after surgery. But they’re doing this in a different way, by targeting a sodium channel that plays a critical role in transmitting pain signals.
In 2004, Stephen Waxman, a neurology professor at Yale, led a search for genetic pain anomalies and found that biologically related people who felt no pain despite fractures, burns and even childbirth had mutations in the Nav1.7 sodium channel. Further studies in other families who experienced no pain showed similar mutations in the Nav1.8 sodium channel.
Scientists set out to modify these channels. Many unsuccessful efforts followed, but Vertex has now developed VX-548, a medicine to inhibit Nav1.8. Typically, sodium ions flow through sodium channels to generate rapid changes in voltage which create electrical pulses. When pain is detected, these pulses in the Nav1.8 channel transmit pain signals. VX-548 uses small molecules to inhibit the channel from opening. This blocks the flow of sodium ions and the pain signal. Because Nav1.8 operates only in peripheral nerves, located outside the brain, VX-548 can relieve pain without any risk of addiction.
"Frankly we need drugs for chronic pain more than acute pain," says Waxman.
The team just finished phase II clinical trials for patients following abdominoplasty surgery and bunionectomy surgery.
After abdominoplasty surgery, 76 patients were treated with a high dose of VX-548. Researchers then measured its effectiveness in reducing pain over 48 hours, using the SPID48 scale, in which higher scores are desirable. The score for Vertex’s drug was 110.5 compared to 72.7 in the placebo group, whereas the score for patients taking an opioid was 85.2. The study involving bunionectomy surgery showed positive results as well.
Waxman, who has been at the forefront of studies into Nav1.7 and Nav1.8, believes that Vertex's results are promising, though he highlights the need for further clinical trials.
“Blocking Nav1.8 is an attractive target,” he says. “[Vertex is] studying pain that is relatively simple and uniform, and that's key to having a drug trial that is informative. But the study needs to be replicated and frankly we need drugs for chronic pain more than acute pain. If this is borne out by additional studies, it's one important step in a journey.”
Vertex will be launching phase III trials later this year.
Finding just the right amount of Nerve Growth Factor
Whereas Neurocarrus and Vertex are targeting short-term pain, a company called Levicept is concentrating on relieving chronic osteoarthritis pain. Around 32.5 million Americans suffer from osteoarthritis. Patients commonly take NSAIDs, or non-steroidal anti-inflammatory drugs, but they cannot be taken long-term. Some take opioids but they aren't very effective.
Levicept’s drug, Levi-04, is designed to modify a signaling pathway associated with pain. Nerve Growth Factor (NGF) is a neurotrophin: it’s involved in nerve growth and function. NGF signals by attaching to receptors. In pain there are excess neurotrophins attaching to receptors and activating pain signals.
“What Levi-04 does is it returns the natural equilibrium of neurotrophins,” says Simon Westbrook, the CEO and founder of Levicept. It stabilizes excess neurotrophins so that the NGF pathway does not signal pain. Levi-04 isn't addictive since it works within joints and in nerves outside the brain.
Westbrook was initially involved in creating an anti-NGF molecule for Pfizer called Tanezumab. At first, Tanezumab seemed effective in clinical trials and other companies even started developing their own versions. However, a problem emerged. Tanezumab caused rapidly progressive osteoarthritis, or RPOA, in some patients because it completely removed NGF from the system. NGF is not just involved in pain signalling, it’s also involved in bone growth and maintenance.
Levicept has found a way to modify the NGF pathway without completely removing NGF. They have now finished a small-scale phase I trial mainly designed to test safety rather than efficacy. “We demonstrated that Levi-04 is safe and that it bound to its target, NGF,” says Westbrook. It has not caused RPOA.
Professor Philip Conaghan, director of the Leeds Institute of Rheumatic and Musculoskeletal Medicine, believes that Levi-04 has potential but urges the need for caution. “At this early stage of development, their molecule looks promising for osteoarthritis pain,” he says. “They will have to watch out for RPOA which is a potential problem.”
Westbrook starts phase II trials with 500 patients this summer to check for potential side effects and test the drug’s efficacy.
There is a real push to find an effective alternative to opioids. “We have a lot of work to do,” says Professor Waxman. “But I am confident that we will be able to develop new, much more effective pain therapies.”
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.