A blood test for analysis of HIV.
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.
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
