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.
AI was integral to creating Moderna's mRNA vaccine against COVID.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
