Health breakthroughs of 2022 that should have made bigger news
As the world has attempted to move on from COVID-19 in 2022, attention has returned to other areas of health and biotech with major regulatory approvals such as the Alzheimer's drug lecanemab – which can slow the destruction of brain cells in the early stages of the disease – being hailed by some as momentous breakthroughs.
This has been a year where psychedelic medicines have gained the attention of mainstream researchers with a groundbreaking clinical trial showing that psilocybin treatment can help relieve some of the symptoms of major depressive disorder. And with messenger RNA (mRNA) technology still very much capturing the imagination, the readouts of cancer vaccine trials have made headlines around the world.
But at the same time there have been vital advances which will likely go on to change medicine, and yet have slipped beneath the radar. I asked nine forward-thinking experts on health and biotech about the most important, but underappreciated, breakthrough of 2022.
Their descriptions, below, were lightly edited by Leaps.org for style and format.
New drug targets for Alzheimer’s disease
Professor Julie Williams, Director, Dementia Research Institute, Cardiff University
Genetics has changed our view of Alzheimer’s disease in the last five to six years. The beta amyloid hypothesis has dominated Alzheimer’s research for a long time, but there are multiple components to this complex disease, of which getting rid of amyloid plaques is one, but it is not the whole story. In April 2022, Nature published a paper which is the culmination of a decade’s worth of work - groups all over the world working together to identify 75 genes associated with risk of developing Alzheimer’s. This provides us with a roadmap for understanding the disease mechanisms.
For example, it is showing that there is something different about the immune systems of people who develop Alzheimer’s disease. There is something different about the way they process lipids in the brain, and very specific processes of how things travel through cells called endocytosis. When it comes to immunity, it indicates that the complement system is affecting whether synapses, which are the connections between neurons, get eliminated or not. In Alzheimer’s this process is more severe, so patients are losing more synapses, and this is correlated with cognition.
The genetics also implicates very specific tissues like microglia, which are the housekeepers in the brain. One of their functions is to clear away beta amyloid, but they also prune and nibble away at parts of the brain that are indicated to be diseased. If you have these risk genes, it seems that you are likely to prune more tissue, which may be part of the cell death and neurodegeneration that we observe in Alzheimer’s patients.
Genetics is telling us that we need to be looking at multiple causes of this complex disease, and we are doing that now. It is showing us that there are a number of different processes which combine to push patients into a disease state which results in the death of connections between nerve cells. These findings around the complement system and other immune-related mechanisms are very interesting as there are already drugs which are available for other diseases which could be repurposed in clinical trials. So it is really a turning point for us in the Alzheimer’s disease field.
Preventing Pandemics with Organ-Tissue Equivalents
Anthony Atala, Director of the Wake Forest Institute for Regenerative Medicine
COVID-19 has shown us that we need to be better prepared ahead of future pandemics and have systems in place where we can quickly catalogue a new virus and have an idea of which treatment agents would work best against it.
At Wake Forest Institute, our scientists have developed what we call organ-tissue equivalents. These are miniature tissues and organs, created using the same regenerative medicine technologies which we have been using to create tissues for patients. For example, if we are making a miniature liver, we will recreate this structure using the six different cell types you find in the liver, in the right proportions, and then the right extracellular matrix which holds the structure together. You're trying to replicate all the characteristics of the liver, but just in a miniature format.
We can now put these organ-tissue equivalents in a chip-like device, where we can expose them to different types of viral infections, and start to get a realistic idea of how the human body reacts to these viruses. We can use artificial intelligence and machine learning to map the pathways of the body’s response. This will allow us to catalogue known viruses far more effectively, and begin storing information on them.
Powering Deep Brain Stimulators with Breath
Islam Mosa, Co-Founder and CTO of VoltXon
Deep brain stimulation (DBS) devices are becoming increasingly common with 150,000 new devices being implanted every year for people with Parkinson’s disease, but also psychiatric conditions such as treatment-resistant depression and obsessive-compulsive disorders. But one of the biggest limitations is the power source – I call DBS devices energy monsters. While cardiac pacemakers use similar technology, their batteries last seven to ten years, but DBS batteries need changing every two to three years. This is because they are generating between 60-180 pulses per second.
Replacing the batteries requires surgery which costs a lot of money, and with every repeat operation comes a risk of infection, plus there is a lot of anxiety on behalf of the patient that the battery is running out.
My colleagues at the University of Connecticut and I, have developed a new way of charging these devices using the person’s own breathing movements, which would mean that the batteries never need to be changed. As the patient breathes in and out, their chest wall presses on a thin electric generator, which converts that movement into static electricity, charging a supercapacitor. This discharges the electricity required to power the DBS device and send the necessary pulses to the brain.
So far it has only been tested in a simulated pig, using a pig lung connected to a pump, but there are plans now to test it in a real animal, and then progress to clinical trials.
Smartwatches for Disease Detection
Jessilyn Dunn, Assistant Professor in Duke Biomedical Engineering
A group of researchers recently showed that digital biomarkers of infection can reveal when someone is sick, often before they feel sick. The team, which included Duke biomedical engineers, used information from smartwatches to detect Covid-19 cases five to 10 days earlier than diagnostic tests. Smartwatch data included aspects of heart rate, sleep quality and physical activity. Based on this data, we developed an algorithm to decide which people have the most need to take the diagnostic tests. With this approach, the percent of tests that come back positive are about four- to six-times higher, depending on which factors we monitor through the watches.
Our study was one of several showing the value of digital biomarkers, rather than a single blockbuster paper. With so many new ideas and technologies coming out around Covid, it’s hard to be that signal through the noise. More studies are needed, but this line of research is important because, rather than treat everyone as equally likely to have an infectious disease, we can use prior knowledge from smartwatches. With monkeypox, for example, you've got many more people who need to be tested than you have tests available. Information from the smartwatches enables you to improve how you allocate those tests.
Smartwatch data could also be applied to chronic diseases. For viruses, we’re looking for information about anomalies – a big change point in people’s health. For chronic diseases, it’s more like a slow, steady change. Our research lays the groundwork for the signals coming from smartwatches to be useful in a health setting, and now it’s up to us to detect more of these chronic cases. We want to go from the idea that we have this single change point, like a heart attack or stroke, and focus on the part before that, to see if we can detect it.
A Vaccine For RSV
Norbert Pardi, Vaccines Group Lead, Penn Institute for RNA Innovation, University of Pennsylvania
Scientists have long been trying to develop a vaccine for respiratory syncytial virus (RSV), and it looks like Pfizer are closing in on this goal, based on the latest clinical trial data in newborns which they released in November. Pfizer have developed a protein-based vaccine against the F protein of RSV, which they are giving to pregnant women. It turns out that it induces a robust immune response after the administration of a single shot and it seems to be highly protective in newborns. The efficacy was over 80% after 90 days, so it protected very well against severe disease, and even though this dropped a little after six month, it was still pretty high.
I think this has been a very important breakthrough, and very timely at the moment with both COVID-19, influenza and RSV circulating, which just shows the importance of having a vaccine which works well in both the very young and the very old.
The road to an RSV vaccine has also illustrated the importance of teamwork in 21st century vaccine development. You need people with different backgrounds to solve these challenges – microbiologists, immunologists and structural biologists working together to understand how viruses work, and how our immune system induces protective responses against certain viruses. It has been this kind of teamwork which has yielded the findings that targeting the prefusion stabilized form of the F protein in RSV induces much stronger and highly protective immune responses.
Gene therapy shows its potential
Nicole Paulk, Assistant Professor of Gene Therapy at the University of California, San Francisco
The recent US Food and Drug Administration (FDA) approval of Hemgenix, a gene therapy for hemophilia B, is big for a lot of reasons. While hemophilia is absolutely a rare disease, it is astronomically more common than the first two approvals – Luxturna for RPE65-meidated inherited retinal dystrophy and Zolgensma for spinal muscular atrophy - so many more patients will be treated with this. In terms of numbers of patients, we are now starting to creep up into things that are much more common, which is a huge step in terms of our ability to scale the production of an adeno-associated virus (AAV) vector for gene therapy.
Hemophilia is also a really special patient population because this has been the darling indication for AAV gene therapy for the last 20 to 30 years. AAV trafficks to the liver so well, it’s really easy for us to target the tissues that we want. If you look at the numbers, there have been more gene therapy scientists working on hemophilia than any other condition. There have just been thousands and thousands of us working on gene therapy indications for the last 20 or 30 years, so to see the first of these approvals make it, feels really special.
I am sure it is even more special for the patients because now they have a choice – do I want to stay on my recombinant factor drug that I need to take every day for the rest of my life, or right now I could get a one-time infusion of this virus and possibly experience curative levels of expression for the rest of my life. And this is just the first one for hemophilia, there’s going to end up being a dozen gene therapies within the next five years, targeted towards different hemophilias.
Every single approval is momentous for the entire field because it gets investors excited, it gets companies and physicians excited, and that helps speed things up. Right now, it's still a challenge to produce enough for double digit patients. But with more interest comes the experiments and trials that allow us to pick up the knowledge to scale things up, so that we can go after bigger diseases like diabetes, congestive heart failure, cancer, all of these much bigger afflictions.
Treating Thickened Hearts
John Spertus, Professor in Metabolic and Vascular Disease Research, UMKC School of Medicine
Hypertrophic cardiomyopathy (HCM) is a disease that causes your heart muscle to enlarge, and the walls of your heart chambers thicken and reduce in size. Because of this, they cannot hold as much blood and may stiffen, causing some sufferers to experience progressive shortness of breath, fatigue and ultimately heart failure.
So far we have only had very crude ways of treating it, using beta blockers, calcium channel blockers or other medications which cause the heart to beat less strongly. This works for some patients but a lot of time it does not, which means you have to consider removing part of the wall of the heart with surgery.
Earlier this year, a trial of a drug called mavacamten, became the first study to show positive results in treating HCM. What is remarkable about mavacamten is that it is directed at trying to block the overly vigorous contractile proteins in the heart, so it is a highly targeted, focused way of addressing the key problem in these patients. The study demonstrated a really large improvement in patient quality of life where they were on the drug, and when they went off the drug, the quality of life went away.
Some specialists are now hypothesizing that it may work for other cardiovascular diseases where the heart either beats too strongly or it does not relax well enough, but just having a treatment for HCM is a really big deal. For years we have not been very aggressive in identifying and treating these patients because there have not been great treatments available, so this could lead to a new era.
Regenerating Organs
David Andrijevic, Associate Research Scientist in neuroscience at Yale School of Medicine
As soon as the heartbeat stops, a whole chain of biochemical processes resulting from ischemia – the lack of blood flow, oxygen and nutrients – begins to destroy the body’s cells and organs. My colleagues and I at Yale School of Medicine have been investigating whether we can recover organs after prolonged ischemia, with the main goal of expanding the organ donor pool.
Earlier this year we published a paper in which we showed that we could use technology to restore blood circulation, other cellular functions and even heart activity in pigs, one hour after their deaths. This was done using a perfusion technology to substitute heart, lung and kidney function, and deliver an experimental cell protective fluid to these organs which aimed to stop cell death and aid in the recovery.
One of the aims of this technology is that it can be used in future to lengthen the time window for recovering organs for donation after a person has been declared dead, a logistical hurdle which would allow us to substantially increase the donor pool. We might also be able to use this cell protective fluid in studies to see if it can help people who have suffered from strokes and myocardial infarction. In future, if we managed to achieve an adequate brain recovery – and the brain, out of all the organs, is the most susceptible to ischemia – this might also change some paradigms in resuscitation medicine.
Antibody-Drug Conjugates for Cancer
Yosi Shamay, Cancer Nanomedicine and Nanoinformatics researcher at the Technion Israel Institute of Technology
For the past four or five years, antibody-drug conjugates (ADCs) - a cancer drug where you have an antibody conjugated to a toxin - have been used only in patients with specific cancers that display high expression of a target protein, for example HER2-positive breast cancer. But in 2022, there have been clinical trials where ADCs have shown remarkable results in patients with low expression of HER2, which is something we never expected to see.
In July 2022, AstraZeneca published the results of a clinical trial, which showed that an ADC called trastuzumab deruxtecan can offer a very big survival benefit to breast cancer patients with very little expression of HER2, levels so low that they would be borderline undetectable for a pathologist. They got a strong survival signal for patients with very aggressive, metastatic disease.
I think this is very interesting and important because it means that it might pave the way to include more patients in clinical trials looking at ADCs for other cancers, for example lymphoma, colon cancer, lung cancers, even if they have low expression of the protein target. It also holds implications for CAR-T cells - where you genetically engineer a T cell to attack the cancer - because the concept is very similar. If we now know that an ADC can have a survival benefit, even in patients with very low target expression, the same might be true for T cells.
Look back further: Breakthroughs of 2021
https://leaps.org/6-biotech-breakthroughs-of-2021-that-missed-the-attention-they-deserved/
Podcast: The Friday Five weekly roundup in health research
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Covered in this week's Friday Five:
- Sex differences in cancer
- Promising research on a vaccine for Lyme disease
- Using a super material for brain-like devices
- Measuring your immunity to Covid
- Reducing dementia risk with leisure activities
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
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.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.