Researchers Get Closer to Gene Editing Treatment for Cardiovascular Disease
Later this year, Verve Therapeutics of Cambridge, Ma., will initiate Phase 1 clinical trials to test VERVE-101, a new medication that, if successful, will employ gene editing to significantly reduce low-density lipoprotein cholesterol, or LDL.
LDL is sometimes referred to as the “bad” cholesterol because it collects in the walls of blood vessels, and high levels can increase chances of a heart attack, cardiovascular disease or stroke. There are approximately 600,000 heart attacks per year due to blood cholesterol damage in the United States, and heart disease is the number one cause of death in the world. According to the CDC, a 10 percent decrease in total blood cholesterol levels can reduce the incidence of heart disease by as much as 30 percent.
Verve’s Founder and CEO, Sekar Kathiresan, spent two decades studying the genetic basis for heart attacks while serving as a professor of medicine at Harvard Medical School. His research led to two critical insights.
“One is that there are some people that are naturally resistant to heart attack and have lifelong, low levels of LDL,” the cardiologist says. “Second, there are some genes that can be switched off that lead to very low LDL cholesterol, and individuals with those genes switched off are resistant to heart attacks.”
Kathiresan and his team formed a hypothesis in 2016 that if they could develop a medicine that mimics the natural protection that some people enjoy, then they might identify a powerful new way to treat and ultimately prevent heart attacks. They launched Verve in 2018 with the goal of creating a one-time therapy that would permanently lower LDL and eliminate heart attacks caused by high LDL.
"Imagine a future where somebody gets a one-time treatment at the time of their heart attack or before as a preventive measure," says Kathiresan.
The medication is targeted specifically for patients who have a genetic form of high cholesterol known as heterozygous familial hypercholesterolemia, or FH, caused by expression of a gene called PCSK9. Verve also plans to develop a program to silence a gene called ANGPTL3 for patients with FH and possibly those with or at risk of atherosclerotic cardiovascular disease.
FH causes cholesterol to be high from birth, reaching levels of 200 to 300 milligrams per deciliter. Suggested normal levels are around 100 to 129 mg/dl, and anything above 130 mg/dl is considered high. Patients with cardiovascular disease usually are asked to aim for under 70 mg/dl, but many still have unacceptably high LDL despite taking oral medications such as statins. They are more likely to have heart attacks in their 30s, 40s and 50s, and require lifelong LDL control.
The goal for drug treatments for high LDL, Kathiresan says, is to reduce LDL as low as possible for as long as possible. Physicians and researchers also know that a sizeable portion of these patients eventually start to lose their commitment to taking their statins and other LDL-controlling medications regularly.
“If you ask 100 patients one year after their heart attack what fraction are still taking their cholesterol-lowering medications, it’s less than half,” says Kathiresan. “So imagine a future where somebody gets a one-time treatment at the time of their heart attack or before as a preventive measure. It’s right in front of us, and it’s something that Verve is looking to do.”
In late 2020, Verve completed primate testing with monkeys that had genetically high cholesterol, using a one-time intravenous injection of VERVE-101. It reduced the monkeys’ LDL by 60 percent and, 18 months later, remains at that level. Kathiresan expects the LDL to stay low for the rest of their lives.
Verve’s gene editing medication is packaged in a lipid nanoparticle to serve as the delivery mechanism into the liver when infused intravenously. The drug is absorbed and makes its way into the nucleus of the liver cells.
Verve’s program targeting PCSK9 uses precise, single base, pair base editing, Kathiresan says, meaning it doesn't cut DNA like CRISPR gene editing systems do. Instead, it changes one base, or letter, in the genome to a different one without affecting the letters around it. Comparing it to a pencil and eraser, he explains that the medication erases out a letter A and makes it a letter G in the A, C, G and T code in DNA.
“We need to continue to advance our approach and tools to make sure that we have the absolute maximum ability to detect off-target effects,” says Euan Ashley, professor of medicine and genetics at Stanford University.
By making that simple change from A to G, the medication switches off the PCSK9 gene, automatically lowering LDL cholesterol.
“Once the DNA change is made, all the cells in the liver will have that single A to G change made,” Kathiresan says. “Then the liver cells divide and give rise to future liver cells, but every time the cell divides that change, the new G is carried forward.”
Additionally, Verve is pursuing its second gene editing program to eliminate ANGPTL3, a gene that raises both LDL and blood triglycerides. In 2010, Kathiresan's research team learned that people who had that gene completely switched off had LDL and triglyceride levels of about 20 and were very healthy with no heart attacks. The goal of Verve’s medication will be to switch off that gene, too, as an option for additional LDL or triglyceride lowering.
“Success with our first drug, VERVE-101, will give us more confidence to move forward with our second drug,” Kathiresan says. “And it opens up this general idea of making [genomic] spelling changes in the liver to treat other diseases.”
The approach is less ethically concerning than other gene editing technologies because it applies somatic editing that affects only the individual patient, whereas germline editing in the patient’s sperm or egg, or in an embryo, gets passed on to children. Additionally, gene editing therapies receive the same comprehensive amount of testing for side effects as any other medicine.
“We need to continue to advance our approach and tools to make sure that we have the absolute maximum ability to detect off-target effects,” says Euan Ashley, professor of medicine and genetics at Stanford University and founding director of its Center for Inherited Cardiovascular Disease. Ashley and his colleagues at Stanford’s Clinical Genomics Program and beyond are increasingly excited about the promise of gene editing.
“We can offer precision diagnostics, so increasingly we’re able to define the disease at a much deeper level using molecular tools and sequencing,” he continues. “We also have this immense power of reading the genome, but we’re really on the verge of taking advantage of the power that we now have to potentially correct some of the variants that we find on a genome that contribute to disease.”
He adds that while the gene editing medicines in development to correct genomes are ahead of the delivery mechanisms needed to get them into the body, particularly the heart and brain, he’s optimistic that those aren’t too far behind.
“It will probably take a few more years before those next generation tools start to get into clinical trials,” says Ashley, whose book, The Genome Odyssey, was published last year. “The medications might be the sexier part of the research, but if you can’t get it into the right place at the right time in the right dose and not get it to the places you don’t want it to go, then that tool is not of much use.”
Medical experts consider knocking out the PCSK9 gene in patients with the fairly common genetic disorder of familial hypercholesterolemia – roughly one in 250 people – a potentially safe approach to gene editing and an effective means of significantly lowering their LDL cholesterol.
Nurse Erin McGlennon has an Implantable Cardioverter Defibrillator and takes medications, but she is also hopeful that a gene editing medication will be developed in the near future.
Erin McGlennon
Mary McGowan, MD, chief medical officer for The Family Heart Foundation in Pasadena, CA, sees the tremendous potential for VERVE-101 and believes patients should be encouraged by the fact that this kind of research is occurring and how much Verve has accomplished in a relatively short time. However, she offers one caveat, since even a 60 percent reduction in LDL won’t completely eliminate the need to reduce the remaining amount of LDL.
“This technology is very exciting,” she said, “but we want to stress to our patients with familial hypercholesterolemia that we know from our published research that most people require several therapies to get their LDL down., whether that be in primary prevention less than 100 mg/dl or secondary prevention less than 70 mg/dl, So Verve’s medication would be an add-on therapy for most patients.”
Dr. Kathiresan concurs: “We expect our medicine to lower LDL cholesterol by about 60 percent and that our patients will be on background oral medications, including statins that lower LDL cholesterol.”
Several leading research centers are investigating gene editing treatments for other types of cardiovascular diseases. Elizabeth McNally, Elizabeth Ward Professor and Director at the Center for Genetic Medicine at Northwestern University’s Feinberg School of Medicine, pursues advanced genetic correction in neuromuscular diseases such as Duchenne muscular dystrophy and spinal muscular atrophy. A cardiologist, she and her colleagues know these diseases frequently have cardiac complications.
“Even though the field is driven by neuromuscular specialists, it’s the first therapies in patients with neuromuscular diseases that are also expected to make genetic corrections in the heart,” she says. “It’s almost like an afterthought that we’re potentially fixing the heart, too.”
Another limitation McGowan sees is that too many healthcare providers are not yet familiar with how to test patients to determine whether or not they carry genetic mutations that need to be corrected. “We need to get more genetic testing done,” she says. “For example, that’s the case with hypertrophic cardiomyopathy, where a lot of the people who probably carry that diagnosis and have never been genetically identified at a time when genetic testing has never been easier.”
One patient who has been diagnosed with hypertrophic cardiomyopathy also happens to be a nurse working in research at Genentech Pharmaceutical, now a member of the Roche Group, in South San Francisco. To treat the disease, Erin McGlennon, RN, has an Implantable Cardioverter Defibrillator and takes medications, but she is also hopeful that a gene editing medication will be developed in the near future.
“With my condition, the septum muscles are just growing thicker, so I’m on medicine to keep my heart from having dangerous rhythms,” says McGlennon of the disease that carries a low risk of sudden cardiac death. “So, the possibility of having a treatment option that can significantly improve my day-to-day functioning would be a major breakthrough.”
McGlennon has some control over cardiovascular destiny through at least one currently available technology: in vitro fertilization. She’s going through it to ensure that her children won't express the gene for hypertrophic cardiomyopathy.
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.