Medical Breakthroughs Set to be Fast-Tracked by Innovative New Health Agency
In 2007, Matthew Might's son, Bertrand, was born with a life-threatening disease that was so rare, doctors couldn't diagnose it. Might, a computer scientist and biologist, eventually realized, "Oh my gosh, he's the only patient in the world with this disease right now." To find effective treatments, new methodologies would need to be developed. But there was no process or playbook for doing that.
Might took it upon himself, along with a team of specialists, to try to find a cure. "What Bertrand really taught me was the visceral sense of urgency when there's suffering, and how to act on that," he said.
He calls it "the agency of urgency"—and patients with more common diseases, such as cancer and Alzheimer's, often feel that same need to take matters into their own hands, as they find their hopes for new treatments running up against bureaucratic systems designed to advance in small, steady steps, not leaps and bounds. "We all hope for a cure," said Florence "Pippy" Rogers, a 65-year-old volunteer with Georgia's chapter of the Alzheimer's Association. She lost her mother to the disease and, these days, worries about herself and her four siblings. "We need to keep accelerating research."
We have a fresh example of what can be achieved by fast-tracking discoveries in healthcare: Covid-19 vaccines.
President Biden has pushed for cancer moonshots since the disease took the life of his son, Beau, in 2015. His administration has now requested $6.5 billion to start a new agency in 2022, called the Advanced Research Projects Agency for Health, or ARPA-H, within the National Institutes of Health. It's based on DARPA, the Department of Defense agency known for hatching world-changing technologies such as drones, GPS and ARPANET, which became the internet.
We have a fresh example of what can be achieved by fast-tracking discoveries in healthcare: Covid-19 vaccines. "Operation Warp Speed was using ARPA-like principles," said Might. "It showed that in a moment of crisis, institutions like NIH can think in an ARPA-like way. So now the question is, why don't we do that all the time?"
But applying the DARPA model to health involves several challenging decisions. I asked experts what could be the hardest question facing advocates of ARPA-H: which health problems it should seek to address. "All the wonderful choices lead to the problem of which ones to choose and prioritize," said Sudip Parikh, CEO of the American Association for the Advancement of Science and executive publisher of the Science family of journals. "There is no objectively right answer."
The Agency of Urgency
ARPA-H will borrow at least three critical ingredients from DARPA: goal-oriented project managers, many from industry; aggressive public-private partnerships; and collaboration among fields that don't always interact. The DARPA concept has been applied to other purposes, including energy and homeland security, with promising results. "We're learning that 'ARPA-ism' is a franchisable model," said Might, a former principal investigator on DARPA projects.
The federal government already pours billions of dollars into advancing research on life-threatening diseases, with much of it channeled through the National Institutes of Health. But the purpose of ARPA-H "isn't just the usual suspects that NIH would fund," said David Walt, a Harvard biochemist, an innovator in gene sequencing and former chair of DARPA's Defense Science Research Council. Whereas some NIH-funded studies aim to gradually improve our understanding of diseases, ARPA-H projects will give full focus to real-world applications; they'll use essential findings from NIH research as starting points, drawing from them to rapidly engineer new technologies that could save lives.
And, ultimately, billions in healthcare costs, if ARPA-H lives up to its predecessor's track record; DARPA's breakthroughs have been economic game-changers, while its fail-fast approach—quickly pulling the plug on projects that aren't panning out—helps to avoid sunken costs. ARPA-H could fuel activities similar to the human genome project, which used existing research to map the base pairs that make up DNA, opening new doors for the biotech industry, sparking economic growth and creating hundreds of thousands of new jobs.
Despite a nearly $4 trillion health economy, "we aren't innovating when it comes to technological capabilities for health," said Liz Feld, president of the Suzanne Wright Foundation for pancreatic cancer.
Individual Diseases Ripe for Innovation
Although the need for innovation is clear, which diseases ARPA-H should tackle is less apparent. One important consideration when choosing health priorities could be "how many people suffer from a disease," said Nancy Kass, a professor of bioethics and public health at Johns Hopkins.
That perspective could justify cancer as a top objective. Cancer and heart disease have long been the two major killers in the U.S. Leonidas Platanias, professor of oncology at Northwestern and director of its cancer center, noted that we've already made significant progress on heart disease. "Anti-cholesterol drugs really have a wide impact," he said. "I don't want to compare one disease to another, but I think cancer may be the most challenging. We need even bigger breakthroughs." He wondered whether ARPA-H should be linked to the part of NIH dedicated to cancer, the National Cancer Institute, "to take maximum advantage of what happens" there.
Previous cancer moonshots have laid a foundation for success. And this sort of disease-by-disease approach makes sense in a way. "We know that concentrating on some diseases has led to treatments," said Parikh. "Think of spinal muscular atrophy or cystic fibrosis. Now, imagine if immune therapies were discovered ten years earlier."
But many advocates think ARPA-H should choose projects that don't revolve around any one disease. "It absolutely has to be disease agnostic," said Feld, president of the pancreatic cancer foundation. "We cannot reach ARPA-H's potential if it's subject to the advocacy of individual patient groups who think their disease is worse than the guy's disease next to them. That's not the way the DARPA model works." Platanias agreed that ARPA-H should "pick the highest concepts and developments that have the best chance" of success.
Finding Connections Between Diseases
Kass, the Hopkins bioethicist, believes that ARPA-H should walk a balance, with some projects focusing on specific diseases and others aspiring to solutions with broader applications, spanning multiple diseases. Being impartial, some have noted, might involve looking at the total "life years" saved by a health innovation; the more diseases addressed by a given breakthrough, the more years of healthy living it may confer. The social and economic value should increase as well.
For multiple payoffs, ARPA-H could concentrate on rare diseases, which can yield important insights for many other diseases, said Might. Every case of cancer and Alzheimer's is, in a way, its own rare disease. Cancer is a genetic disease, like his son Bertrand's rare disorder, and mutations vary widely across cancer patients. "It's safe to say that no two people have ever actually had the same cancer," said Might. In theory, solutions for rare diseases could help us understand how to individualize treatments for more common diseases.
Many experts I talked with support another priority for ARPA-H with implications for multiple diseases: therapies that slow down the aging process. "Aging is the greatest risk factor for every major disease that NIH is studying," said Matt Kaeberlein, a bio-gerontologist at the University of Washington. Yet, "half of one percent of the NIH budget goes to researching the biology of aging. An ARPA-H sized budget would push the field forward at a pace that's hard to imagine."
Might agreed. "It could take ARPA-H to get past the weird stigmas around aging-related research. It could have a tremendous impact on the field."
For example, ARPA-H could try to use mRNA technology to express proteins that affect biological aging, said Kaeberlein. It's an engineering project well-suited to the DARPA model. So is harnessing machine learning to identify biomarkers that assess how fast people are aging. Biological aging clocks, if validated, could quickly reveal whether proposed therapies for aging are working or not. "I think there's huge value in that," said Kaeberlein.
By delivering breakthroughs in computation, ARPA-H could improve diagnostics for many different diseases. That could include improving biowearables for continuously monitoring blood pressure—a hypothetical mentioned in the White House's concept paper on ARPA-H—and advanced imaging technologies. "The high cost of medical imaging is a leading reason why our healthcare costs are the highest in the world," said Feld. "There's no detection test for ALS. No brain detection for Alzheimer's. Innovations in detection technology would save on cost and human suffering."
Some biotech companies may be skeptical about the financial rewards of accelerating such technologies. But ARPA-H could fund public-private partnerships to "de-risk" biotech's involvement—an incentive that harkens back to the advance purchase contracts that companies got during Covid. (Some groups have suggested that ARPA-H could provide advance purchase agreements.)
Parikh is less bullish on creating diagnostics through ARPA-H. Like DARPA, Biden's health agency will enjoy some independence from federal oversight; it may even be located hundreds of miles from DC. That freedom affords some breathing room for innovation, but it could also make it tougher to ensure that algorithms fully consider diverse populations. "That part I really would like the government more involved in," Parikh said.
Might thinks ARPA-H should also explore innovations in clinical trials, which many patients and medical communities view as grindingly slow and requiring too many participants. "We can approve drugs for very tiny patient populations, even at the level of the individual," he said, while emphasizing the need for safety. But Platanias thinks the FDA has become much more flexible in recent years. In the cancer field, at least, "You now see faster approvals for more drugs. Having [more] shortcuts on clinical trial approvals is not necessarily a good idea."
With so many options on the table, ARPA-H needs to show the public a clear framework for measuring the value of potential projects. Kass warned that well-resourced advocates could skew the agency's priorities. They've affected health outcomes before, she noted; fundraising may partly explain larger increases in life expectancy for cystic fibrosis than sickle cell anemia. Engaging diverse communities is a must for ARPA-H. So are partnerships to get the agency's outputs to people who need them. "Research is half the equation," said Kass. "If we don't ensure implementation and access, who cares." The White House concept paper on ARPA-H made a similar point.
As Congress works on authorizing ARPA-H this year, Might is doing what he can to ensure better access to innovation on a patient-by-patient basis. Last year, his son, Bertrand, passed away suddenly from his disorder. He was 12. But Might's sense of urgency has persisted, as he directs the Precision Medicine Institute at the University of Alabama-Birmingham. That urgency "can be carried into an agency like ARPA-H," he said. "It guides what I do as I apply for funding, because I'm trying to build the infrastructure that other parents need. So they don't have to build it from scratch like I did."
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