Why Haven’t Researchers Developed an HIV Vaccine or Cure Yet?
Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
Last week, top experts on HIV/AIDS convened in Amsterdam for the 22nd International AIDS conference, and the mood was not great. Even though remarkable advances in treating HIV have led to effective management for many people living with the disease, and its overall incidence has declined, there are signs that the virus could make a troubling comeback.
"In a perfect world, we'd get a vaccine like the HPV vaccine that was 100% effective and I think that's ultimately what we're going to strive for."
Growing resistance to current HIV drugs, a population boom in Sub-Saharan Africa, and insufficient public health resources are all poised to contribute to a second AIDS pandemic, according to published reports.
Already, the virus is nowhere near under control. Though the infection rate has declined 47 percent since its peak in 1996, last year 1.8 million people became newly infected with HIV around the world, and 37 million people are currently living with it. About 1 million people die of AIDS every year, making it the fourth biggest killer in low-income countries.
Leapsmag Editor-in-Chief Kira Peikoff reached out to Dr. Carl Dieffenbach, Director of the Division of AIDS at the National Institute of Allergy and Infectious Diseases, to find out what the U.S. government is doing to develop an HIV vaccine and cure. This interview has been edited and condensed for clarity.
What is the general trajectory of research in HIV/AIDS today?
We can break it down to two specific domains: focus on treatment and cure, and prevention.
Let's start with people living with HIV. This is the area where we've had the most success over the past 30 plus years, because we've taken a disease that was essentially a death sentence and converted it through the development of medications to a treatable chronic disease.
The second half of this equation is, can we cure or create a functional cure for people living with HIV? And the definition of functional cure would be the absence of circulating virus in the body in the absence of therapy. Essentially the human body would control the HIV infection within the individual. That is a much more, very early research stage of discovery. There are some interesting signals but it's still in need of innovation.
I'd like to make a contrast between what we are able to do with a virus called Hepatitis C and what we can do with the virus HIV. Hep C, with 12 weeks of highly active antiviral therapy, we can cure 95 to 100% of infections. With HIV, we cannot do that. The difference is the behavior of the virus. HIV integrates into the host's genome. Hep C is an RNA virus that stays in the cytoplasm of the cell and never gets into the DNA.
On the prevention side, we have two strategies: The first is pre-exposure prophylaxis. Then of course, we have the need for a safe, effective and durable HIV vaccine, which is a very active area of discovery. We've had some spectacular success with RV144, and we're following up on that success, and other vaccines are in the pipeline. Whether they are sufficient to provide the level of durability and activity is not yet clear, but progress has been made and there's still the need for innovation.
The most important breakthrough in the past 5 to 10 years has been the discovery of broad neutralizing monoclonal antibodies. They are proteins that the body makes, and not everybody who's HIV infected makes these antibodies, but we've been able to clone out these antibodies from certain individuals that are highly potent, and when used either singly or in combination, can truly neutralize the vast majority of HIV strains. Can those be used by themselves as treatment or as prevention? That is the question.
Can you explain more about RV144 and why you consider it a success?
Prior to RV144, we had run a number of vaccine studies and nothing had ever statistically shown to be protective. RV144 showed a level of efficacy of about 31 percent, which was statistically significant. Not enough to take forward into other studies, but it allowed us to generate some ideas about why this worked, go back to the drawing board, and redesign the immunogens to optimize and test the next generation for this vaccine. We just recently opened that new study, the follow-up to RV144, called HVTN702. That's up and enrolling and moving along quite nicely.
Carl Dieffenbach, Director of the Division of AIDS at the National Institute of Allergy and Infectious Diseases
(Courtesy)
Where is that enrolling?
Primarily in Sub-Saharan Africa and South Africa.
When will you expect to see signals from that?
Between 2020 and 2021. It's complicated because the signal also takes into account the durability. After a certain time of vaccination, we're going to count up endpoints.
How would you explain the main scientific obstacle in the way of creating a very efficacious HIV vaccine?
Simply put, it's the black box of the human immune system. HIV employs a shield technology, and the virus is constantly changing its shield to protect itself, but there are some key parts of the virus that it cannot shield, so that's the trick – to be able to target that.
So, you're trying to find the Achilles' Heel of the virus?
Exactly. To make a flu vaccine or a Zika vaccine or even an Ebola vaccine, the virus is a little bit more forthcoming with the target. In HIV, the virus does everything in its power to hide the target, so we're dealing with a well-adapted [adversary] that actively avoids neutralization. That's the scientific challenge we face.
What's next?
On the vaccine side, we are currently performing, in collaboration with partners, two vaccine trials – HVTN702, which we talked about, and another one called 705. If either of those are highly successful, they would both require an additional phase 3 clinical trial before they could be licensed. This is an important but not final step. Then we would move into scale up to global vaccination. Those conversations have begun but they are not very far along and need additional attention.
What percent of people in the current trials would need to be protected to move on to phase 3?
Between 50 and 60 percent. That comes with this question of durability: how long does the vaccine last?
It also includes, can we simplify the vaccine regimen? The vaccines we're testing right now are multiple shots over a period of time. Can we get more like the polio or smallpox vaccine, a shot with a booster down the road?
We're dealing with sovereign nations. We're doing this in partnership, not as helicopter-type researchers.
If these current trials pan out, do you think kids in the developed world will end up getting an HIV vaccine one day? Or just people in-at risk areas?
That's a good question. I don't have an answer to that. In a perfect world, we'd get a vaccine like the HPV vaccine that was 100% effective and I think that's ultimately what we're going to strive for. That's where that second or third generation of vaccines that trigger broad neutralizing antibodies come in.
With any luck at all, globally, the combination of antiretroviral treatment, pre-exposure prophylaxis and other prevention and treatment strategies will lower the incidence rate where the HIV pandemic continues to wane, and we will then be able to either target the vaccine or roll it out in a way that is both cost effective and destigmatizing.
And also, what does the country want? We're dealing with sovereign nations. We're doing this in partnership, not as helicopter-type researchers.
How close do you think we are globally to eradicating HIV infections?
Eradication's a big word. It means no new infections. We are nowhere close to eradicating HIV. Whether or not we can continue to bend the curve on the epidemic and have less infections so that the total number of people continues to decline over time, I think we can achieve that if we had the political will. And that's not just the U.S. political will. That's the will of the world. We have the tools, albeit they're not perfect. But that's where a vaccine that is efficacious and simple to deliver could be the gamechanger.
Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
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.