President John F. Kennedy gave Dr. Frances Oldham Kelsey the nation's highest federal civilian service award in 1962, saying she had "prevented a major tragedy of birth deformities."
By 1960, Thalidomide was being sold in countries throughout the world, and the United States was expected to soon follow suit. Dr. Frances Oldham Kelsey, a pharmacologist and physician, was hired by the Food and Drug Administration (FDA) in September of that year to review and approve drugs for the administration. Immediately, Kelsey was tasked with approving thalidomide for commercial use in the United States under the name Kevadon. Kelsey's approval was supposed to be a formality, since the drug was so widely used in other countries.
But Kelsey did something that few people expected – she paused. Rather than approving the drug offhand as she was expected to do, Kelsey asked the manufacturer – William S. Merrell Co., who was manufacturing thalidomide under license from Chemie Grünenthal – to supply her with more safety data, noting that Merrell's application for approval relied mostly on anecdotal testimony. Kelsey – along with her husband who worked as a pharmacologist at the National Institutes of Health (NIH) — was highly suspicious of a drug that had no lethal dose and no side effects. "It was just too positive," Kelsey said later. "This couldn't be the perfect drug with no risk."
At the same time, rumors were starting to swirl across Europe that thalidomide was not as safe as everyone had initially thought: Physicians were starting to notice an "unusual increase" in the birth of severely deformed babies, and they were beginning to suspect thalidomide as the cause. The babies, whose mothers had all taken thalidomide during pregnancy, were born with conditions like deafness, blindness, congenital heart problems, and even phocomelia, a malformation of the arms and legs. Doctors and midwives were also starting to notice a sharp rise in miscarriages and stillbirths among their patients as well.
Kelsey's skepticism was rewarded in November 1961 when thalidomide was yanked abruptly off the market, following a growing outcry that it was responsible for hundreds of stillbirths and deformities.
Kelsey had heard none of these rumors, but she did know from her post-doctoral research that adults could metabolize drugs differently than fetuses – in other words, a drug that was perfectly safe for adults could be detrimental to a patient's unborn child. Noting that thalidomide could cross the placental barrier, she asked for safety data, such as clinical trials, that showed specifically the drug was non-toxic for fetuses. Merrell supplied Kelsey with anecdotal data – in other words, accounts from patients who attested to the fact that they took thalidomide with no adverse effects – but she rejected it, needing stronger data: clinical studies with pregnant women included.
The drug company was annoyed at what they considered Kelsey's needless bureaucracy. After all, Germans were consuming around 1 million doses of thalidomide every day in 1960, with lots of anecdotal evidence that it was safe, even among pregnant women. As the holidays approached – the most lucrative time of year for sedative sales – Merrell executives started hounding Kelsey to approve thalidomide, even phoning her superior and paying her visits at work. But Kelsey was unmovable. Kelsey's skepticism was solidified in December 1960, when she read a letter published in the British Medical Journal from a physician. In the letter, the author warned that his long-term thalidomide patients were starting to report pain in their arms and legs.
"The burden of proof that the drug is safe … lies with the applicant," Kelsey wrote in a letter to Merrell executive Joseph F. Murray in May of 1961. Despite increasing pressure, Kelsey held fast to her insistence that more safety data – particularly for fetuses – was needed.
Kelsey's skepticism was rewarded in November 1961 when Chemie Grünenthal yanked thalidomide off the market overseas, following a growing outcry that it was responsible for hundreds of stillbirths and deformities. In early 1962, Merrell conceded that the drug's safety was unproven in fetuses and formally withdrew its application at the FDA.
Thanks to Kelsey, the United States was spared the effects of thalidomide – although countries like Europe and Canada were not so lucky. Thalidomide remained in people's homes under different names long after it was pulled from the market, and so women unfortunately continued to take thalidomide during their pregnancies, unaware of its effects. All told, thalidomide is thought to have caused around 10,000 birth defects and anywhere from 5,000 to 7,000 miscarriages. Many so-called "thalidomide babies" are now adults living with disabilities.
Niko von Glasow, born in 1960, is a German film director and producer who was born disabled due to the side effects of thalidomide.
Wikimedia Commons
Just two years after joining the FDA, Kelsey was presented with the President's Award for Distinguished Federal Civilian Service and was appointed as the head of the Investigational Drug Branch at the FDA. Not only did Kelsey save the U.S. public from the horrific effects of thalidomide, but she forever changed the way drugs were developed and approved for use in the United States: Drugs now need to not only be proven safe and effective, but adverse drug reactions need to be reported to the FDA and informed consent must be obtained by all participants before they volunteer for clinical trials. Today, the United States is safer because of Frances Kelsey's bravery.
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.
