New drug for schizophrenia could meet desperate need for better treatments
Schizophrenia is a debilitating mental health condition that affects around 24 million people worldwide. Patients experience hallucinations and delusions when they develop schizophrenia, with experts referring to these new thoughts and behaviors as positive symptoms. They also suffer from negative symptoms in which they lose important functions, suffering from dulled emotions, lack of purpose and social withdrawal.
Currently available drugs can control only a portion of these symptoms but, on August 8th, Karuna Therapeutics announced its completion of a phase 3 clinical trial that found a new drug called KarXT could treat both positive and negative symptoms of schizophrenia. It could mean substantial progress against a problem that has stymied scientists for decades.
A long-standing problem
Since the 1950s, antipsychotics have been used to treat schizophrenia. People who suffer from it are thought to have too much of a brain chemical called dopamine, and antipsychotics work by blocking dopamine receptors in the brain. They can be effective in treating positive symptoms but have little impact on the negative ones, which can be devastating for a patient’s quality of life, making it difficult to maintain employment and have successful relationships. About 30 percent of schizophrenia patients don't actually respond to antipsychotics at all. Current drugs can also have adverse side effects including elevated cholesterol, high blood pressure, diabetes and movements that patients cannot control.
The recent clinical trial heralds a new treatment approach. “We believe it marks an important advancement for patients given its new and completely different mechanism of action from current therapies,” says Andrew Miller, COO of Karuna.
Scientists have been looking to develop alternatives. However, “the field of drug treatment of schizophrenia is currently in the doldrums,” says Peter McKenna, a senior researcher at FIDMAG Research Foundation in Spain which specialises in mental health.
In the 2000s there was a major push to target a brain receptor for a chemical called glutamate. Evidence suggested that this receptor is abnormal in the brains of schizophrenia patients, but attempts to try glutamate failed in clinical trials.
After that, many pharmaceutical companies dropped out of the race for a more useful treatment. But some companies continued to search, such as Karuna Therapeutics, led by founder and Chief Operating Officer Andrew Miller and CEO Steve Paul. The recent clinical trial suggests their persistence has led to an important breakthrough with their drug, KarXT. “We believe it marks an important advancement for patients given its new and completely different mechanism of action from current therapies,” Miller says.
How it works
Neurotransmitters are chemical messengers that pass signals between neurons. To work effectively, neurotransmitters need a receptor to bind to. A neurotransmitter called acetylcholine seems to be especially important in schizophrenia. It interacts with sites called muscarinic receptors, which are involved in the network of nerves that calm your body after a stressful event. Post mortem studies in people with schizophrenia have shown that two muscarinic receptors in the brain, the M1 and M4 receptors, are activated at unusually low levels because they don’t receive enough signals from acetylcholine.
The M4 receptor appears to play a role in psychosis. The M1 receptor is also associated with psychosis but is primarily thought to be involved in cognition. KarXT, taken orally, works by activating both of these receptors to signal properly. It is this twofold action that seems to explain its effectiveness. “[The drug’s] design enables the preferential stimulation of these muscarinic receptors in the brain,” Miller says.
How it developed
It all started in the early 1990s when Paul was at pharmaceutical company Eli Lilly. He discovered that Xanomeline, the drug they were testing on Alzheimer's patients, had antipsychotic effects. It worked by stimulating M1 and M4 receptors, so he and his colleagues decided to test Xanomeline on schizophrenia patients, supported by research on the connection between muscarinic receptors and psychosis. They found that Xanomeline reduced both positive and negative symptoms.
Unfortunately, it also caused significant side effects. The problem was that stimulating the M1 and M4 receptors in the brain also stimulated muscarinic receptors in the body that led to severe vomiting, diarrhea and even the temporary loss of consciousness.
In the end, Eli Lilly discontinued the clinical trials for the drug, but Miller set up Karuna Therapeutics to develop a solution. “I was determined to find a way to harness the therapeutic benefit demonstrated in studies of Xanomeline, while eliminating side effects that limited its development,” Miller says.
He analysed over 7,000 possible ways of mixing Xanomeline with other agents before settling on KarXT. It combines Xanomeline with a drug called Trospium Chloride, which blocks muscarinic receptors in the body – taking care of the side effects such as vomiting – but leaves them unblocked in the brain. Paul was so excited by Miller’s progress that he joined Karuna after leaving Eli Lilly and founding two previous startups.
“It's a very important approach,” says Rick Adams, Future Leaders Fellow in the Institute of Cognitive Neuroscience and Centre for Medical Image Computing at University College London. “We are in desperate need of alternative drug targets and this target is one of the best. There are other alternative targets, but not many are as close to being successful as the muscarinic receptor drug.”
Clinical Trial
Following a successful phase 2 clinical trial in 2019, the most recent trial involved 126 patients who were given KarXT, and 126 who were given a placebo. Compared to the placebo, patients taking KarXT had a significant 9.6 point reduction in the positive and negative syndrome scale (PANSS), the standard for rating schizophrenic symptoms.
KarXT also led to statistically significant declines in positive and negative symptoms compared to the placebo. “The results suggest that KarXT could be a potentially game-changing option in the management of both positive and negative symptoms of schizophrenia,” Miller says.
Robert McCutcheon, a psychiatrist and neuroscientist at Oxford University, is optimistic about the side effects but highlights the need for more safety trials.
McKenna, the researcher at FIDMAG Foundation, agrees about the drug’s potential. “The new [phase 3] study is positive,” he says. “It is reassuring that one is not dealing with a drug that works in one trial and then inexplicably fails in the next one.”
Robert McCutcheon, a psychiatrist and neuroscientist at Oxford University, said the drug is an unprecedented step forward. “KarXT is one of the first drugs with a novel mechanism of action to show promise in clinical trials.”
Even though the drug blocks muscarine receptors in the body, some patients still suffered from adverse side effects like vomiting, dizziness and diarrhea. But in general, these effects were mild to moderate, especially compared to dopamine-blocking antipsychotics or Xanomeline on its own.
McCutcheon is optimistic about the side effects but highlights the need for more safety trials. “The trial results suggest that gastrointestinal side effects appear to be manageable,” he says. “We know, however, from previous antipsychotic drugs that the full picture regarding the extent of side effects can sometimes take longer to become apparent to clinicians and patients. Careful ongoing assessment during a longer period of treatment will therefore be important.”
The Future
The team is currently conducting three other trials to evaluate the efficacy and long-term safety of KarXT. Their goal is to receive FDA approval next year.
Karuna is also conducting trials to evaluate the effectiveness of KarXT in treating psychosis in patients suffering from Alzheimer’s.
The big hope is that they will soon be able to provide a radically different drug to help many patients with schizophrenia. “We are another step closer to potentially providing the first new class of medicine in more than 50 years to the millions of people worldwide living with schizophrenia,” says Miller.
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.