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.
Basecamp Research is using portable labs like this one to gather samples from ecosystems around the world.
Clark works for Basecamp Research, a biotech company headquartered in London, and his job is to collect samples from ecosystems around the world. By extracting DNA from soil, water, plants, microbes and other organisms, Basecamp is building an extensive database of the Earth’s proteins. While DNA itself isn’t a protein, the information stored in DNA is used to create proteins, so extracting, sequencing, and annotating DNA allows for the discovery of unique protein sequences.
Using what they’re finding in the middle of the Atlantic and beyond, Basecamp’s detailed database is constantly growing. The outputs could be essential for cleaning up the damage done by toxic chemicals and finding alternatives to these chemicals.
Catalysts for change
Proteins provide structure and function in all living organisms. Some of these functional proteins are enzymes, which quite literally make things happen.
“Industrial chemistry is heavily polluting, especially the chemistry done in pharmaceutical drug development. Biocatalysis is providing advantages, both to make more complex drugs and to be more sustainable, reducing the pollution and toxicity of conventional chemistry," says Ahir Pushpanath, who heads partnerships for Basecamp.
“Enzymes are perfectly evolved catalysts,” says Ahir Pushpanath, a partnerships lead at Basecamp. ”Enzymes are essentially just a polymer, and polymers are made up of amino acids, which are nature’s building blocks.” He suggests thinking about it like Legos — if you have a bunch of Lego pieces and use them to build a structure that performs a function, “that’s basically how an enzyme works. In nature, these monuments have evolved to do life’s chemistry. If we didn’t have enzymes, we wouldn’t be alive.”
In our own bodies, enzymes catalyze everything from vision to digesting food to regrowing muscles, and these same types of enzymes are necessary in the pharmaceutical, agrochemical and fine chemical industries. But industrial conditions differ from those inside our bodies. So, when scientists need certain chemical reactions to create a particular product or substance, they make their own catalysts in their labs — generally through the use of petroleum and heavy metals.
These petrochemicals are effective and cost-efficient, but they’re wasteful and often hazardous. With growing concerns around sustainability and long-term public health, it's essential to find alternative solutions to toxic chemicals. “Industrial chemistry is heavily polluting, especially the chemistry done in pharmaceutical drug development,” Pushpanath says.
Basecamp is trying to replace lab-created catalysts with enzymes found in the wild. This concept is called biocatalysis, and in theory, all scientists have to do is find the right enzymes for their specific need. Yet, historically, researchers have struggled to find enzymes to replace petrochemicals. When they can’t identify a suitable match, they turn to what Pushpanath describes as “long, iterative, resource-intensive, directed evolution” in the laboratory to coax a protein into industrial adaptation. But the latest scientific advances have enabled these discoveries in nature instead.
Marlon Clark, a research scientist at Basecamp Research, looks for novel biochemistries in the Azores.
Glen Gowers
Enzyme hunters
Whether it’s Clark and a colleague setting off on an expedition, or a local, on-the-ground partner gathering and processing samples, there’s a lot to be learned from each collection. “Microbial genomes contain complete sets of information that define an organism — much like how letters are a code allowing us to form words, sentences, pages, and books that contain complex but digestible knowledge,” Clark says. He thinks of the environmental samples as biological libraries, filled with thousands of species, strains, and sequence variants. “It’s our job to glean genetic information from these samples.”
“We can actually dream up new proteins using generative AI," Pushpanath says.
Basecamp researchers manage this feat by sequencing the DNA and then assembling the information into a comprehensible structure. “We’re building the ‘stories’ of the biota,” Clark says. The more varied the samples, the more valuable insights his team gains into the characteristics of different organisms and their interactions with the environment. Sequencing allows scientists to examine the order of nucleotides — the organic molecules that form DNA — to identify genetic makeups and find changes within genomes. The process used to be too expensive, but the cost of sequencing has dropped from $10,000 a decade ago to as low as $100. Notably, biocatalysis isn’t a new concept — there have been waves of interest in using natural enzymes in catalysis for over a century, Pushpanath says. “But the technology just wasn’t there to make it cost effective,” he explains. “Sequencing has been the biggest boon.”
AI is probably the second biggest boon.
“We can actually dream up new proteins using generative AI,” Pushpanath says, which means that biocataylsis now has real potential to scale.
Glen Gowers, the co-founder of Basecamp, compares the company’s AI approach to that of social networks and streaming services. Consider how these platforms suggest connecting with the friends of your friends, or how watching one comedy film from the 1990s leads to a suggestion of three more.
“They’re thinking about data as networks of relationships as opposed to lists of items,” says Gowers. “By doing the same, we’re able to link the metadata of the proteins — by their relationships to each other, the environments in which they’re found, the way those proteins might look similar in sequence and structure, their surrounding genome context — really, this just comes down to creating a searchable network of proteins.”
On an Azores island, this volcanic opening may harbor organisms that can help scientists identify enzymes for biocatalysis to replace toxic chemicals.
Emma Bolton
Uwe Bornscheuer, professor at the Institute of Biochemistry at the University of Greifswald, and co-founder of Enzymicals, another biocatalysis company, says that the development of machine learning is a critical component of this work. “It’s a very hot topic, because the challenge in protein engineering is to predict which mutation at which position in the protein will make an enzyme suitable for certain applications,” Bornscheuer explains. These predictions are difficult for humans to make at all, let alone quickly. “It is clear that machine learning is a key technology.”
Benefiting from nature’s bounty
Biodiversity commonly refers to plants and animals, but the term extends to all life, including microbial life, and some regions of the world are more biodiverse than others. Building relationships with global partners is another key element to Basecamp’s success. Doing so in accordance with the access and benefit sharing principles set forth by the Nagoya Protocol — an international agreement that seeks to ensure the benefits of using genetic resources are distributed in a fair and equitable way — is part of the company's ethos. “There's a lot of potential for us, and there’s a lot of potential for our partners to have exactly the same impact in building and discovering commercially relevant proteins and biochemistries from nature,” Clark says.
Bornscheuer points out that Basecamp is not the first company of its kind. A former San Diego company called Diversa went public in 2000 with similar work. “At that time, the Nagoya Protocol was not around, but Diversa also wanted to ensure that if a certain enzyme or microorganism from Costa Rica, for example, were used in an industrial process, then people in Costa Rica would somehow profit from this.”
An eventual merger turned Diversa into Verenium Corporation, which is now a part of the chemical producer BASF, but it laid important groundwork for modern companies like Basecamp to continue to scale with today’s technologies.
“To collect natural diversity is the key to identifying new catalysts for use in new applications,” Bornscheuer says. “Natural diversity is immense, and over the past 20 years we have gained the advantages that sequencing is no longer a cost or time factor.”
This has allowed Basecamp to rapidly grow its database, outperforming Universal Protein Resource or UniProt, which is the public repository of protein sequences most commonly used by researchers. Basecamp’s database is three times larger, totaling about 900 million sequences. (UniProt isn’t compliant with the Nagoya Protocol, because, as a public database, it doesn’t provide traceability of protein sequences. Some scientists, however, argue that Nagoya compliance hinders progress.)
“Eventually, this work will reduce chemical processes. We’ll have cleaner processes, more sustainable processes," says Uwe Bornscheuer, a professor at the University of Greifswald.
With so much information available, Basecamp’s AI has been trained on “the true dictionary of protein sequence life,” Pushpanath says, which makes it possible to design sequences for particular applications. “Through deep learning approaches, we’re able to find protein sequences directly from our database, without the need for further laboratory-directed evolution.”
Recently, a major chemical company was searching for a specific transaminase — an enzyme that catalyzes a transfer of amino groups. “They had already spent a year-and-a-half and nearly two million dollars to evolve a public-database enzyme, and still had not reached their goal,” Pushpanath says. “We used our AI approaches on our novel database to yield 10 candidates within a week, which, when validated by the client, achieved the desired target even better than their best-evolved candidate.”
Basecamp’s other huge potential is in bioremediation, where natural enzymes can help to undo the damage caused by toxic chemicals. “Biocatalysis impacts both sides,” says Gowers. “It reduces the usage of chemicals to make products, and at the same time, where contamination sites do exist from chemical spills, enzymes are also there to kind of mop those up.”
So far, Basecamp's round-the-world sampling has covered 50 percent of the 14 major biomes, or regions of the planet that can be distinguished by their flora, fauna, and climate, as defined by the World Wildlife Fund. The other half remains to be catalogued — a key milestone for understanding our planet’s protein diversity, Pushpanath notes.
There’s still a long road ahead to fully replace petrochemicals with natural enzymes, but biocatalysis is on an upward trajectory. "Eventually, this work will reduce chemical processes,” Bornscheuer says. “We’ll have cleaner processes, more sustainable processes.”
In an initial study, Canary analyzed speech recordings with AI and identified early stage Alzheimer’s with 96 percent accuracy.
Arnold was diagnosed sooner than many others. It's important to find out early, when interventions can make the most difference. One promising avenue is looking at how people talk. Research has shown that Alzheimer’s affects a part of the brain that controls speech, resulting in small changes before people show other signs of the disease.
Now, Canary Speech, a company based in Utah, is using AI to examine elements like the pitch of a person’s voice and their pauses. In an initial study, Canary analyzed speech recordings with AI and identified early stage Alzheimer’s with 96 percent accuracy.
Developing the AI model
Canary Speech’s CEO, Henry O’Connell, met cofounder Jeff Adams about 40 years before they started the company. Back when they first crossed paths, they were both living in Bethesda, Maryland; O’Connell was a research fellow at the National Institutes of Health studying rare neurological diseases, while Adams was working to decode spy messages. Later on, Adams would specialize in building mathematical models to analyze speech and sound as a team leader in developing Amazon's Alexa.
It wasn't until 2015 that they decided to make use of the fit between their backgrounds. ““We established Canary Speech in 2017 to build a product that could be used in multiple languages in clinical environments,” O'Connell says.
The need is growing. About 55 million people worldwide currently live with Alzheimer’s, a number that is expected to double by 2050. Some scientists think the disease results from a buildup of plaque in the brain. It causes mild memory loss at first and, over time, this issue get worse while other symptoms, such as disorientation and hallucinations, can develop. Treatment to manage the disease is more effective in the earlier stages, but detection is difficult since mild symptoms are often attributed to the normal aging process.
O’Connell and Adams specialize in the complex ways that Alzheimer’s effects how people speak. Using AI, their mathematical model analyzes 15 million data points every minute, focusing on certain features of speech such as pitch, pauses and elongation of words. It also pays attention to how the vibrations of vocal cords change in different stages of the disease.
To create their model, the team used a type of machine learning called deep neural nets, which looks at multiple layers of data - in this case, the multiple features of a person’s speech patterns.
“Deep neural nets allow us to look at much, much larger data sets built out of millions of elements,” O’Connell explained. “Through machine learning and AI, we’ve identified features that are very sensitive to an Alzheimer’s patient versus [people without the disease] and also very sensitive to mild cognitive impairment, early stage and moderate Alzheimer's.” Based on their learnings, Canary is able to classify the disease stage very quickly, O’Connell said.
“When we’re listening to sublanguage elements, we’re really analyzing the direct result of changes in the brain in the physical body,” O’Connell said. “The brain controls your vocal cords: how fast they vibrate, the expansion of them, the contraction.” These factors, along with where people put their tongues when talking, function subconsciously and result in subtle changes in the sounds of speech.
Further testing is needed
In an initial trial, Canary analyzed speech recordings from phone calls to a large U.S. health insurer. They looked at the audio recordings of 651 policyholders who had early stage Alzheimer’s and 1018 who did not have the condition, aiming for a representative sample of age, gender and race. They used this data to create their first diagnostic model and found that it was 96 percent accurate in identifying Alzheimer’s.
Christian Herff, an assistant professor of neuroscience at Maastricht University in the Netherlands, praised this approach while adding that further testing is needed to assess its effectiveness.
“I think the general idea of identifying increased risk for cognitive impairment based on speech characteristics is very feasible, particularly when change in a user’s voice is monitored, for example, by recording speech every year,” Herff said. He noted that this can only be a first indication, not a full diagnosis. The accuracy still needs to be validated in studies that follows individuals over a period of time, he said.
Toby Walsh, a professor of artificial intelligence at the University of New South Wales, also thinks Canary’s tool has potential but highlights that Canary could diagnose some people who don’t really have the disease. “This is an interesting and promising application of AI,” he said, “but these tools need to be used carefully. Imagine the anxiety of being misdiagnosed with Alzheimer’s.”
As with many other AI tools, privacy and bias are additional issues to monitor closely, Walsh said.
Other languages
A related issue is that not everyone is fluent in English. Mahnaz Arvaneh, a senior lecturer in automatic control and systems engineering at the University of Sheffield, said this could be a blind spot.
“The system may not be very accurate for those who have English as their second language as their speaking patterns would be different, and any issue might be because of language deficiency rather than cognitive issues,” Arvaneh said.
The team is expanding to multiple languages starting with Japanese and Spanish. The elements of the model that make up the algorithm are very similar, but they need to be validated and retrained in a different language, which will require access to more data.
Recently, Canary analyzed the phone calls of 233 Japanese patients who had mild cognitive impairment and 704 healthy people. Using an English model they were able to identify the Japanese patients who had mild cognitive impairment with 78 percent accuracy. They also developed a model in Japanese that was 45 percent accurate, and they’re continuing to train it with more data.
The future
Canary is using their model to look at other diseases like Huntington’s and Parkinson’s. They’re also collaborating with pharmaceuticals to validate potential therapies for Alzheimer’s. By looking at speech patterns over time, Canary can get an indication of how well these drugs are working.
Dave Arnold and his wife dance at his nephew’s wedding in Rochester, New York, ten years ago, before his Alzheimer's diagnosis.
Dave Arnold
Ultimately, they want to integrate their tool into everyday life. “We want it to be used in a smartphone, or a teleconference call so that individuals could be examined in their home,” O’Connell said. “We could follow them over time and work with clinical teams and hospitals to improve the evaluation of patients and contribute towards an accurate diagnosis.”
Arnold, the patient with early stage Alzheimer’s, sees great promise. “The process of getting a diagnosis is already filled with so much anxiety,” he said. “Anything that can be done to make it easier and less stressful would be a good thing, as long as it’s proven accurate.”