A string of the code that comprises a DNA molecule, pictured at Miraikan, the National Museum of Emerging Science and Innovation in Japan.
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.
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.”