How Leqembi became the biggest news in Alzheimer’s disease in 40 years, and what comes next
A few months ago, Betsy Groves traveled less than a mile from her home in Cambridge, Mass. to give a talk to a bunch of scientists. The scientists, who worked for the pharmaceutical companies Biogen and Eisai, wanted to know how she lived her life, how she thought about her future, and what it was like when a doctor’s appointment in 2021 gave her the worst possible news. Groves, 73, has Alzheimer’s disease. She caught it early, through a lumbar puncture that showed evidence of amyloid, an Alzheimer’s hallmark, in her cerebrospinal fluid. As a way of dealing with her diagnosis, she joined the Alzheimer’s Association’s National Early-Stage Advisory Board, which helped her shift into seeing her diagnosis as something she could use to help others.
After her talk, Groves stayed for lunch with the scientists, who were eager to put a face to their work. Biogen and Eisai were about to release the first drug to successfully combat Alzheimer’s in 40 years of experimental disaster. Their drug, which is known by the scientific name lecanemab and the marketing name Leqembi, was granted accelerated approval by the U.S. Food and Drug Administration last Friday, Jan. 6, after a study in 1,800 people showed that it reduced cognitive decline by 27 percent over 18 months.
It is no exaggeration to say that this result is a huge deal. The field of Alzheimer’s drug development has been absolutely littered with failures. Almost everything researchers have tried has tanked in clinical trials. “Most of the things that we've done have proven not to be effective, and it's not because we haven’t been taking a ton of shots at goal,” says Anton Porsteinsson, director of the University of Rochester Alzheimer's Disease Care, Research, and Education Program, who worked on the lecanemab trial. “I think it's fair to say you don't survive in this field unless you're an eternal optimist.”
As far back as 1984, a cure looked like it was within reach: Scientists discovered that the sticky plaques that develop in the brains of those who have Alzheimer’s are made up of a protein fragment called beta-amyloid. Buildup of beta-amyloid seemed to be sufficient to disrupt communication between, and eventually kill, memory cells. If that was true, then the cure should be straightforward: Stop the buildup of beta-amyloid; stop the Alzheimer’s disease.
It wasn’t so simple. Over the next 38 years, hundreds of drugs designed either to interfere with the production of abnormal amyloid or to clear it from the brain flamed out in trials. It got so bad that neuroscience drug divisions at major pharmaceutical companies (AstraZeneca, Pfizer, Bristol-Myers, GSK, Amgen) closed one by one, leaving the field to smaller, scrappier companies, like Cambridge-based Biogen and Tokyo-based Eisai. Some scientists began to dismiss the amyloid hypothesis altogether: If this protein fragment was so important to the disease, why didn’t ridding the brain of it do anything for patients? There was another abnormal protein that showed up in the brains of Alzheimer’s patients, called tau. Some researchers defected to the tau camp, or came to believe the proteins caused damage in combination.
The situation came to a head in 2021, when the FDA granted provisional approval to a drug called aducanumab, marketed as Aduhelm, against the advice of its own advisory council. The approval was based on proof that Aduhelm reduced beta-amyloid in the brain, even though one research trial showed it had no effect on people’s symptoms or daily life. Aduhelm could also cause serious side effects, like brain swelling and amyloid related imaging abnormalities (known as ARIA, these are basically micro-bleeds that appear on MRI scans). Without a clear benefit to memory loss that would make these risks worth it, Medicare refused to pay for Aduhelm among the general population. Two congressional committees launched an investigation into the drug’s approval, citing corporate greed, lapses in protocol, and an unjustifiably high price. (Aduhelm was also produced by the pharmaceutical company Biogen.)
To be clear, Leqembi is not the cure Alzheimer’s researchers hope for. While the drug is the first to show clear signs of a clinical benefit, the scientific establishment is split on how much of a difference Leqembi will make in the real world.
So far, Leqembi is like Aduhelm in that it has been given accelerated approval only for its ability to remove amyloid from the brain. Both are monoclonal antibodies that direct the immune system to attack and clear dysfunctional beta-amyloid. The difference is that, while that’s all Aduhelm was ever shown to do, Leqembi’s makers have already asked the FDA to give it full approval – a decision that would increase the likelihood that Medicare will cover it – based on data that show it also improves Alzheimer’s sufferer’s lives. Leqembi targets a different type of amyloid, a soluble version called “protofibrils,” and that appears to change the effect. “It can give individuals and their families three, six months longer to be participating in daily life and living independently,” says Claire Sexton, PhD, senior director of scientific programs & outreach for the Alzheimer's Association. “These types of changes matter for individuals and for their families.”
To be clear, Leqembi is not the cure Alzheimer’s researchers hope for. It does not halt or reverse the disease, and people do not get better. While the drug is the first to show clear signs of a clinical benefit, the scientific establishment is split on how much of a difference Leqembi will make in the real world. It has “a rather small effect,” wrote NIH Alzheimer’s researcher Madhav Thambisetty, MD, PhD, in an email to Leaps.org. “It is unclear how meaningful this difference will be to patients, and it is unlikely that this level of difference will be obvious to a patient (or their caregivers).” Another issue is cost: Leqembi will become available to patients later this month, but Eisai is setting the price at $26,500 per year, meaning that very few patients will be able to afford it unless Medicare chooses to reimburse them for it.
The same side effects that plagued Aduhelm are common in Leqembi treatment as well. In many patients, amyloid doesn’t just accumulate around neurons, it also forms deposits in the walls of blood vessels. Blood vessels that are shot through with amyloid are more brittle. If you infuse a drug that targets amyloid, brittle blood vessels in the brain can develop leakage that results in swelling or bleeds. Most of these come with no symptoms, and are only seen during testing, which is why they are called “imaging abnormalities.” But in situations where patients have multiple diseases or are prescribed incompatible drugs, they can be serious enough to cause death. The three deaths reported from Leqembi treatment (so far) are enough to make Thambisetty wonder “how well the drug may be tolerated in real world clinical practice where patients are likely to be sicker and have multiple other medical conditions in contrast to carefully selected patients in clinical trials.”
Porsteinsson believes that earlier detection of Alzheimer’s disease will be the next great advance in treatment, a more important step forward than Leqembi’s approval.
Still, there are reasons to be excited. A successful Alzheimer’s drug can pave the way for combination studies, in which patients try a known effective drug alongside newer, more experimental ones; or preventative studies, which take place years before symptoms occur. It also represents enormous strides in researchers’ understanding of the disease. For example, drug dosages have increased massively—in some cases quadrupling—from the early days of Alzheimer’s research. And patient selection for studies has changed drastically as well. Doctors now know that you’ve got to catch the disease early, through PET-scans or CSF tests for amyloid, if you want any chance of changing its course.
Porsteinsson believes that earlier detection of Alzheimer’s disease will be the next great advance in treatment, a more important step forward than Leqembi’s approval. His lab already uses blood tests for different types of amyloid, for different types of tau, and for measures of neuroinflammation, neural damage, and synaptic health, but commercially available versions from companies like C2N, Quest, and Fuji Rebio are likely to hit the market in the next couple of years. “[They are] going to transform the diagnosis of Alzheimer's disease,” Porsteinsson says. “If someone is experiencing memory problems, their physicians will be able to order a blood test that will tell us if this is the result of changes in your brain due to Alzheimer's disease. It will ultimately make it much easier to identify people at a very early stage of the disease, where they are most likely to benefit from treatment.”
Learn more about new blood tests to detect Alzheimer's
Early detection can help patients for more philosophical reasons as well. Betsy Groves credits finding her Alzheimer’s early with giving her the space to understand and process the changes that were happening to her before they got so bad that she couldn’t. She has been able to update her legal documents and, through her role on the Advisory Group, help the Alzheimer’s Association with developing its programs and support services for people in the early stages of the disease. She still drives, and because she and her husband love to travel, they are hoping to get out of grey, rainy Cambridge and off to Texas or Arizona this spring.
Because her Alzheimer’s disease involves amyloid deposits (a “substantial portion” do not, says Claire Sexton, which is an additional complication for research), and has not yet reached an advanced stage, Groves may be a good candidate to try Leqembi. She says she’d welcome the opportunity to take it. If she can get access, Groves hopes the drug will give her more days to be fully functioning with her husband, daughters, and three grandchildren. Mostly, she avoids thinking about what the latter stages of Alzheimer’s might be like, but she knows the time will come when it will be her reality. “So whatever lecanemab can do to extend my more productive ways of engaging with relationships in the world,” she says. “I'll take that in a minute.”
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in 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.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers