A string of the code that comprises a DNA molecule, pictured at Miraikan, the National Museum of Emerging Science and Innovation in Japan.
Miki Yoshihito/Flickr (Creative Commons)
News about COVID-19 continues to relentlessly dominate as Omicron surges around the globe. Yet somehow, during the pandemic’s exhausting twists and turns, progress in other areas of health and biotech has marched on.
In some cases, these innovations have occurred despite a broad reallocation of resources to address the COVID crisis. For other breakthroughs, COVID served as the forcing function, pushing scientists and medical providers to rethink key aspects of healthcare, including how cancer, Alzheimer’s and other diseases are studied, diagnosed and treated. Regardless of why they happened, many of these advances didn’t make the headlines of major media outlets, even when they represented turning points in overcoming our toughest health challenges.
If it bleeds, it leads—and many disturbing stories, such as COVID surges, deserve top billing. Too often, though, mainstream media’s parallel strategy seems to be: if it innovates, it fades to the background. But our breakthroughs are just as critical to understanding the state of the world as our setbacks. I asked six pragmatic yet forward-thinking experts on health and biotech for their perspectives on the most important, but under-appreciated, breakthrough of 2021.
Their descriptions, below, were lightly edited by Leaps.org for style and format.
New Alzheimer's Therapies
Mary Carrillo, Chief Science Officer at the Alzheimer’s Association
Alzheimer's Association
One of the biggest health stories of 2021 was the FDA’s accelerated approval of aducanumab, the first drug that treats the underlying biology of Alzheimer’s, not just the symptoms. But, Alzheimer’s is a complex disease and will likely need multiple treatment strategies that target various aspects of the disease. It’s been exciting to see many of these types of therapies advance in 2021.
Following the FDA action in June, we saw renewed excitement in this class of disease-modifying drugs that target beta-amyloid, a protein that accumulates in the brain and leads to brain cell death. This class includes drugs from Eli Lilly (donanemab), Eisai (lecanemab) and Roche (gantenerumab), all of which received Breakthrough Designation by the FDA in 2021, advancing the drugs more quickly through the approval process.
We’ve also seen treatments advance that target other hallmarks of Alzheimer’s this year. We heard topline results from a phase 2 trial of semorinemab, a drug that targets tau tangles, a toxic protein that destroys neurons in the Alzheimer’s brain. Plus, strategies targeting neuroinflammation, protecting brain cells, and reducing vascular contributions to dementia – all funded through the Alzheimer's Association Part the Cloud program – advanced into clinical trials.
The future of Alzheimer’s treatment will likely be combination therapy, including drug therapies and healthy lifestyle changes, similar to how we treat heart disease. Washington University announced they will be testing a combination of both anti-amyloid and anti-tau drugs in a first-of-its-kind clinical trial, with funding from the Alzheimer’s Association.
AlphaFold
Olivier Elemento, Director of the Caryl and Israel Englander Institute for Precision Medicine at Cornell University
Cornell University
AlphaFold is an artificial intelligence system designed by Google’s DeepMind that opens the door to understanding the three-dimensional structures and functions of proteins, the building blocks that make up almost half of our bodies' dry weight. In 2021, Google made AlphaFold available for free and since then, researchers have used it to drive greater understanding of how proteins interact. This is a foundational event in the field of biotech.
It’s going to take time for the benefits from AlphaFold to transpire, but once we know the 3-D structures of proteins that cause various diseases, it will be much easier to design new drugs that can bind to these proteins and change their activity. Prior to AlphaFold, scientists had identified the 3-D structure of just 17 percent of about 20,000 proteins in the body, partly because mapping the structures was extremely difficult and expensive. Thanks to AlphaFold, we’ve now jumped to knowing – with at least some degree of certainty – the protein structures of 98.5 percent of the proteome.
For example, kinases are a class of proteins that modify other proteins and are often aberrantly active in cancer due to DNA mutations. Some of the earliest targeted therapies for cancer were ones that block kinases but, before AlphaFold, we had only a premature understanding of a few hundred kinases. We can now determine the structures of all 1,500 kinases. This opens up a universe of drug targets we didn’t have before.
Additional progress has been made this year toward potentially using AlphaFold to develop blockers of certain protein receptors that contribute to psychiatric illnesses and other neurological diseases. And in July, scientists used AlphaFold to map the dimensions of a bacterial protein that may be key to countering antibiotic resistance. Another discovery in May could be essential to finding treatments for COVID-19. Ongoing research is using AlphaFold principles to create entirely new proteins from scratch that could have therapeutic uses. The AlphaFold revolution is just beginning.
Virtual First Care
Jennifer Goldsack, CEO of Digital Medicine Society
Digital Medicine Society
Imagine a new paradigm of healthcare defined by how good we are at keeping people healthy and out of the clinic, not how good we are at offering services to a sick person at the clinic. That is the promise of virtual-first care, or V1C, what I consider to be the greatest, and most underappreciated, advance that occurred in medicine this year.
V1C is defined as medical care accessed through digital interactions where possible, guided by a clinician, and integrated into a person’s everyday life. This type of care includes spit kits mailed for laboratory tests and replacing in-person exams with biometric sensors. It’s built around the patient, not the clinic, and provides us with the opportunity to fundamentally reimagine what good healthcare looks like.
V1C flew under the radar in 2021, eclipsed by the ongoing debate about the value of telehealth more broadly as we emerge from the pandemic. However, the growth in the number of specialty and primary care virtual-first providers has been matched only by the number of national health plans offering virtual-first plans. Our own virtual-first community, IMPACT, has tripled in size, mirroring the rapid growth of the field driven by patient demand for care on their terms.
V1C differs from the ‘bolt on’ approach of video visits as an add-on to traditional visit-based, episodic care. V1C takes a much more holistic approach; it allows individuals to initiate care at any time in any place, recognizing that healthcare needs extend beyond 9-5. It matches the care setting with each individual’s clinical needs and personal preferences, advancing a thorough, evidence-based, safe practice while protecting privacy and recognizing that patients’ expectations have changed following the pandemic. V1C puts the promise of digital health into practice. This is the blueprint for what good healthcare looks like in the digital era.
Digital Clinical Trials
Craig Lipset, Founder of Clinical Innovation Partners and former Head of Clinical Innovation at Pfizer
Craig Lipset
In 2021, a number of digital- and data-enabled approaches have sustained decentralized clinical trials around the world for many different disease types. Pharma companies and clinical researchers are enthusiastic about this development for good reason. Throughout the pandemic, these decentralized trials have allowed patients to continue in studies with a reduced need for site visits, without compromising their safety or data quality.
Risk-based monitoring was deployed using data and thoughtful algorithms to identify quality and safety issues without relying entirely on human monitors visiting research sites. Some trials used digital measures to ensure high quality data on target health outcomes that could be captured in ways that made the participants’ physical location irrelevant. More than three-quarters of research organizations, such as pharma and biotech, have accelerated their decentralized clinical trial strategies. Before COVID-19, 72 percent of trial sites “rarely or never” used telemedicine for trial participants; during COVID, 64 percent “sometimes, often or always” do.
While the research community does appreciate the tremendous hope and promise brought by these innovations, perhaps what has been under-appreciated is the culture shift toward thoughtful risk-taking and a willingness to embrace and adopt clinical trial innovations. These solutions existed before COVID, but the pandemic shifted the perception of risks versus benefits involved in these trials. If there is one breakthrough that is perhaps under-appreciated in life sciences clinical research today, it’s the power of this new culture of willingness and receptivity to outlast the pandemic. Perhaps the greatest loss to the research ecosystem would be if we lose the momentum with recent trial innovations and must wait for another global pandemic in order to see it again.
Designing Biology
Sudip Parikh, CEO of the American Association for the Advancement of Science and Executive Publisher of the Science family of journals
American Association for the Advancement of Science
As our understanding of basic biology has grown, we are fast approaching an era where it will be possible to design and direct biological machinery to create treatments, medicine, and materials. 2021 saw many breakthroughs in this area, three of which are listed below.
The understanding of the human microbiome is growing as is our ability to modify it. One example is the movement toward the notion of the “bug as the drug.” In June, scientists at the Brigham and Women’s Hospital published a paper showing that they had genetically engineered yeast – using CRISPR/Cas9 – to sense and treat inflammation in the body to relieve symptoms of irritable bowel syndrome in mice. This approach could potentially be used to address issues with your microbiome to treat other chronic conditions.
Another way in which we saw the application of basic biology discoveries to real world problems in 2021 is through groundbreaking research on synthetic biology. Several institutions and companies are pursuing this path. Ginkgo Bioworks, valued at $15 billion, already claims to engineer cells with assembly-line efficiency. Imagine the possibilities of programming cells and tissue to perform chemistry for the manufacturing process, inspired by the way your body does chemistry. That could mean cleaner, more controllable, and affordable ways to manufacture food, therapeutics, and other materials in a factory-like setting.
A final example: consider the possibility of leveraging the mechanics of your own body to deliver proteins as treatments, vaccines, and more. In 2021, several scientists accelerated research to apply the mRNA technology underlying COVID-19 vaccines to make and replace proteins that, when they’re missing or don’t work, cause rare conditions such as cystic fibrosis and multiple sclerosis.
These applications of basic biology to solve real world problems are exciting on their own, but their convergence with incredible advances in computing, materials, and drug delivery hold the promise of game-changing progress in health care and beyond.
Brain Biomarkers
David R. Walt, Professor of Biologically Inspired Engineering, Harvard Medical School, Brigham and Women’s Hospital, Wyss Institute at Harvard University
David Walt
2021 brought the first real hope for identifying biomarkers that can predict neurodegenerative disease. Multiple biomarkers (which are measurable indicators of the presence or severity of disease) were identified that can diagnose disease and that correlate with disease progression. Some of these biomarkers were detected in cerebrospinal fluid (CSF) but others were measured directly in blood by examining precursors of protein fibers.
The blood-brain barrier prevents many biomolecules from both exiting and entering the brain, so it has been a longstanding challenge to detect and identify biomarkers that signal changes in brain chemistry due to neurodegenerative disease. With the advent of omics-based approaches (an emerging field that encompasses genomics, epigenomics, transcriptomics, proteomics, and metabolomics), coupled with new ultrasensitive analytical methods, researchers are beginning to identify informative brain biomarkers. Such biomarkers portend our ability to detect earlier stages of disease when therapeutic intervention could be effective at halting progression.
In addition, these biomarkers should enable drug developers to monitor the efficacy of candidate drugs in the blood of participants enrolled in clinical trials aimed at slowing neurodegeneration. These biomarkers begin to move us away from relying on cognitive performance indicators and imaging—methods that do not directly measure the underlying biology of neurodegenerative disease. The identity of these biomarkers may also provide researchers with clues about the causes of neurodegenerative disease, which can serve as new targets for drug intervention.
Avalanche rescue dogs train to find and dig out people buried in snow slides
Sarah McLear
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”
