Precision Medicine concept
Is the value of "personalized medicine" over-promised? Why is the quality of health care declining for many people despite the pace of innovation? Do patients and doctors have conflicting priorities? What is the best path forward?
"How do we generate evidence for value, which is what everyone is asking for?"
Some of the country's leading medical experts recently debated these questions at the prestigious annual Personalized Medicine Conference, held at Harvard Medical School in Boston, and LeapsMag was there to bring you the inside scoop.
Personalized Medicine: Is It Living Up to the Hype?
The buzzworthy phrase "personalized medicine" has been touted for years as the way of the future—customizing care to patients based on their predicted responses to treatments given their individual genetic profiles or other analyses. Since the initial sequencing of the human genome around fifteen years ago, the field of genomics has exploded as the costs have dramatically come down – from $2.7 billion to $1000 or less today. Given cheap access to such crucial information, the medical field has been eager to embrace an ultramodern world in which preventing illnesses is status quo, and treatments can be tailored for maximum effectiveness. But whether that world has finally arrived remains debatable.
"I've been portrayed as an advocate for genomics, because I'm excited about it," said Robert C. Green, Director of the Genomes2People Research Program at Harvard Medical School, the Broad Institute, and Brigham and Women's Hospital. He qualified his advocacy by saying that he tries to remain 'equipoised' or balanced in his opinions about the future of personalized medicine, and expressed skepticism about some aspects of its rapid commercialization.
"I have strong feelings about some of the [precision medicine] products that are rushing out to market in both the physician-mediated space and the consumer space," Green said, and challenged the value and sustainability of these products, such as their clinical utility and ability to help produce favorable health outcomes. He asked what most patients and providers want to know, which is, "What are the medical, behavioral, and economic outcomes? How do we generate evidence for value, which is what everyone is asking for?" He later questioned whether the use of 'sexy' and expensive diagnostic technologies is necessarily better than doing things the old-fashioned way. For instance, it is much easier and cheaper to ask a patient directly about their family history of disease, instead of spending thousands of dollars to obtain the same information with pricey diagnostic tests.
"Our mantra is to try to do data-driven health...to catch disease when it occurs early."
Michael Snyder, Professor & Chair of the Department of Genetics and Director of the Center for Genomics and Personalized Medicine at Stanford University, called himself more of an 'enthusiast' about precision medicine products like wearable devices that can digitally track vital signs, including heart rate and blood oxygen levels. "I'm certainly not equipoised," he said, adding, "Our mantra is to try to do data-driven health. We are using this to try to understand health and catch disease when it occurs early."
Snyder then shared his personal account about how his own wearable device alerted him to seek treatment while he was traveling in Norway. "My blood oxygen was low and my heart rate was high, so that told me something was up," he shared. After seeing a doctor, he discovered he was suffering from Lyme disease. He then shared other similar success stories about some of the patients in his department. Using wearable health sensors, he said, could significantly reduce health care costs: "$245 billion is spent every year on diabetes, and if we reduce that by ten percent we just saved $24 billion."
From left, Robert Green, Michael Snyder, Sandro Galea, and Thomas Miller.
(Courtesy Rachele Hendricks-Sturrup)
A Core Reality: Unresolved Societal Issues
Sandro Galea, Dean and Professor at Boston University's School of Public Health, coined himself as a 'skeptic' but also an 'enormous fan' of new technologies. He said, "I want to make sure that you all [the audience] have the best possible treatment for me when I get sick," but added, "In our rush and enthusiasm to embrace personalized and precision medicine approaches, we have done that at the peril of forgetting a lot of core realities."
"There's no one to pay for health care but all of us."
Galea stressed the need to first address certain difficult societal issues because failing to do so will deter precision medicine cures in the future. "Unless we pay attention to domestic violence, housing, racism, poor access to care, and poverty… we are all going to lose," he said. Then he quoted recent statistics about the country's growing gap in both health and wealth, which could potentially erode patient and provider interest in personalized medicine.
Thomas Miller, the founder and partner of a venture capital firm dedicated to advancing precision medicine, agreed with Galea and said that "there's no one to pay for health care but all of us." He recalled witnessing 'abuse' of diagnostic technologies that he had previously invested in. "They were often used as mechanisms to provide unnecessary care rather than appropriate care," he said. "The trend over my 30-year professional career has been that of sensitivity over specificity."
In other words: doctors rely too heavily on diagnostic tools that are sensitive enough to detect signs of a disease, but not accurate enough to confirm the presence of a specific disease. "You will always find that you're sick from something," Miller said. He lamented the counter-productivity and waste brought on by such 'abuse' and added, "That's money that could be used to address some of the problems that you [Galea] just talked about."
Do Patients and Providers Have Conflicting Priorities?
Distrust in the modern health care system is not new in the United States. That fact that medical errors were the third leading cause of death in 2016 may have fueled this mistrust even more. And the level of mistrust appears correlated with race; a recent survey of 118 adults between 18 to 75 years old showed that black respondents were less likely to trust their doctors than the non-Hispanic white respondents. The black respondents were also more concerned about personal privacy and potentially harmful hospital experimentation.
"The vast majority of physicians in this country are incentivized to keep you sick."
As if this context weren't troubling enough, some of the panelists suggested that health care providers and patients have misaligned goals, which may be financially driven.
For instance, Galea stated that health care is currently 'curative' even though that money is better spent on prevention versus cures. "The vast majority of physicians in this country are incentivized to keep you sick," he declared. "They are paid by sick patient visits. Hospital CEOs are paid by the number of sick people they have in their beds." He highlighted this issue as a national priority and mentioned some case studies showing that the behaviors of hospital CEOs quickly change when payment is based on the number of patients in beds versus the number of patients being kept out of the beds. Green lauded Galea's comment as "good sense."
Green also cautioned the audience about potential financial conflicts of interest held by proponents of precision medicine technologies. "Many of the people who are promoting genomics and personalized medicine are people who have financial interests in that arena," he warned. He emphasized that those who are perhaps curbing the over-enthusiasm do not have financial interests at stake.
What is the Best Path Forward for Personalized Medicine?
As useful as personalized medicine may be for selecting the best course of treatment, there is also the flip side: It can allow doctors to predict who will not respond well—and this painful reality must be acknowledged.
Miller argued, "We have a duty to call out therapies that won't work, that will not heal, that need to be avoided, and that will ultimately lead to you saying to a patient, 'There is nothing for you that will work.'"
Although that may sound harsh, it captures the essence of this emerging paradigm, which is to maximize health by using tailored methods that are based on comparative effectiveness, evidence of outcomes, and patient preferences. After all, as Miller pointed out, it wouldn't do much good to prescribe someone a regimen with little reason to think it might help.
For the hype around personalized medicine to be fully realized, Green concluded, "We have to prove to people that [the value of it] is true."
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
