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.
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
