Scientists Jason Schrum and Kerry Benenato solved crucial challenges in mRNA vaccine development.
Derrick Rossi, a scientist at MIT, and Noubar Afeyan, a Cambridge-based investor, launched Moderna in September 2010. Their idea was to create mRNA molecules capable of delivering instructions to the body's cells, directing them to make proteins to heal ailments and cure disease. Need a statin, immunosuppressive, or other drug or vaccine? Just use mRNA to send a message to the body's cells to produce it. Rossi and Afeyan were convinced injecting mRNA into the body could turn it into its own laboratory, generating specific medications or vaccines as needed.
At the time, the notion that one might be able to teach the body to make proteins bordered on heresy. Everyone knew mRNA was unstable and set off the body's immune system on its way into cells. But in the late 2000's, two scientists at the University of Pennsylvania, Katalin Karikó and Drew Weissman, had figured out how to modify mRNA's chemical building blocks so the molecule could escape the notice of the immune system and enter the cell. Rossi and Afeyan couldn't convince the University of Pennsylvania to license Karikó and Weissman's patent, however, stymying Moderna's early ambitions. At the same time, the Penn scientists' technique seemed more applicable to an academic lab than a biotech company that needed to produce drugs or shots consistently and in bulk. Rossi and Afeyan's new company needed their own solution to help mRNA evade the body's defenses.
Some of Moderna's founders doubted Schrum could find success and they worried if their venture was doomed from the start.
The Scientist Who Modified mRNA: Jason Schrum
In 2010, Afeyan's firm subleased laboratory space in the basement of another Cambridge biotech company to begin scientific work. Afeyan chose a young scientist on his staff, Jason Schrum, to be Moderna's first employee, charging him with getting mRNA into cells without relying on Karikó and Weissman's solutions.
Schrum seemed well suited for the task. Months earlier, he had received a PhD in biological chemistry at Harvard University, where he had focused on nucleotide chemistry. Schrum even had the look of someone who might do big things. The baby-faced twenty-eight-year-old favored a relaxed, start-up look: khakis, button-downs, and Converse All-Stars.
Schrum felt immediate strain, however. He hadn't told anyone, but he was dealing with intense pain in his hands and joints, a condition that later would be diagnosed as degenerative arthritis. Soon Schrum couldn't bend two fingers on his left hand, making lab work difficult. He joined a drug trial, but the medicine proved useless. Schrum tried corticosteroid injections and anti-inflammatory drugs, but his left hand ached, restricting his experiments.
"It just wasn't useful," Schrum says, referring to his tender hand.*
He persisted, nonetheless. Each day in the fall of 2010, Schrum walked through double air-locked doors into a sterile "clean room" before entering a basement laboratory, in the bowels of an office in Cambridge's Kendall Square neighborhood, where he worked deep into the night. Schrum searched for potential modifications of mRNA nucleosides, hoping they might enable the molecule to produce proteins. Like all such rooms, there were no windows, so Schrum had to check a clock to know if it was day or night. A colleague came to visit once in a while, but most of the time, Schrum was alone.
Some of Moderna's founders doubted Schrum could find success and they worried if their venture was doomed from the start. An established MIT scientist turned down a job with the start-up to join pharmaceutical giant Novartis, dubious of Moderna's approach. Colleagues wondered if mRNA could produce proteins, at least on a consistent basis.
As Schrum began testing the modifications in January 2011, he made an unexpected discovery. Karikó and Weissman saw that by turned one of the building blocks for mRNA, a ribonucleoside called uridine, into a slightly different form called pseudouridine, the cell's immune system ignored the mRNA and the molecule avoided an immune response. After a series of experiments in the basement lab, Schrum discovered that a variant of pseudouridine called N1- methyl-pseudouridine did an even better job reducing the cell's innate immune response. Schrum's nucleoside switch enabled even higher protein production than Karikó and Weissman had generated, and Schrum's mRNAs lasted longer than either unmodified molecules or the modified mRNA the Penn academics had used, startling the young researcher. Working alone in a dreary basement and through intense pain, he had actually improved on the Penn professors' work.
Years later, Karikó and Weissman who would win acclaim. In September 2021, the scientists were awarded the Lasker-DeBakey Clinical Medical Research Award. Some predict they eventually will win a Nobel prize. But it would be Schrum's innovation that would form the backbone of both Moderna and Pfizer-BioNTech's Covid-19 vaccine, not the chemical modifications that Karikó and Weissman developed. For Schrum, necessity had truly been the mother of invention.
The Scientist Who Solved Delivery: Kerry Benenato
For several years, Moderna would make slow progress developing drugs to treat various diseases. Eventually, the company decided that mRNA was likely better suited for vaccines. By 2017, Moderna and the National Institutes of Health were discussing working together to develop mRNA–based vaccines, a partnership that buoyed Moderna's executives. There remained a huge obstacle in Moderna's way, however. It was up to Kerry Benenato to find a solution.
Benenato received an early hint of the hurdle in front of her three years earlier, when the organic chemist was first hired. When a colleague gave her a company tour, she was introduced to Moderna's chief scientific officer, Joseph Bolen, who seemed unusually excited to meet her.
"Oh, great!" Bolen said with a smile. "She's the one who's gonna solve delivery."
Bolen gave a hearty laugh and walked away, but Benenato detected seriousness in his quip.
Solve delivery?
It was a lot to expect from a 37-year-old scientist already dealing with insecurities and self-doubt. Benenato was an accomplished researcher who most recently had worked at AstraZeneca after completing post-doctoral studies at Harvard University. Despite her impressive credentials, Benenato battled a lack of confidence that sometimes got in her way. Performance reviews from past employers had been positive, but they usually produced similar critiques: Be more vocal. Do a better job advocating for your ideas. Give us more, Kerry.
Benenato was petite and soft-spoken. She sometimes stuttered or relied on "ums" and "ahs" when she became nervous, especially in front of groups, part of why she sometimes didn't feel comfortable speaking up.
"I'm an introvert," she says. "Self-confidence is something that's always been an issue."
To Benenato, Moderna's vaccine approach seemed promising—the team was packaging mRNAs in microscopic fatty-acid compounds called lipid nanoparticles, or LNPs, that protected the molecules on their way into cells. Moderna's shots should have been producing ample and long-lasting proteins. But the company's scientists were alarmed—they were injecting shots deep into the muscle of mice, but their immune systems were mounting spirited responses to the foreign components of the LNPs, which had been developed by a Canadian company.
This toxicity was a huge issue: A vaccine or drug that caused sharp pain and awful fevers wasn't going to prove very popular. The Moderna team was in a bind: Its mRNA had to be wrapped in the fatty nanoparticles to have a chance at producing plentiful proteins, but the body wasn't tolerating the microscopic encasements, especially upon repeated dosing.
The company's scientists had done everything they could to try to make the molecule's swathing material disappear soon after entering the cells, in order to avoid the unfortunate side effects, such as chills and headaches, but they weren't making headway. Frustration mounted. Somehow, the researchers had to find a way to get the encasements—made of little balls of fat, cholesterol, and other substances—to deliver their payload mRNA and then quickly vanish, like a parent dropping a teenager off at a party, to avoid setting off the immune system in unpleasant ways, even as the RNA and the proteins the molecule created stuck around.
Benenato wasn't entirely shocked by the challenges Moderna was facing. One of the reasons she had joined the upstart company was to help develop its delivery technology. She just didn't realize how pressing the issue was, or how stymied the researchers had become. Benenato also didn't know that Moderna board members were among those most discouraged by the delivery issue. In meetings, some of them pointed out that pharmaceutical giants like Roche Holding and Novartis had worked on similar issues and hadn't managed to develop lipid nanoparticles that were both effective and well tolerated by the body. Why would Moderna have any more luck?
Stephen Hoge insisted the company could yet find a solution.
"There's no way the only innovations in LNP are going to come from some academics and a small Canadian company," insisted Hoge, who had convinced the executives that hiring Benenato might help deliver an answer.
Benenato realized that while Moderna might have been a hot Boston-area start- up, it wasn't set up to do the chemistry necessary to solve their LNP problem. Much of its equipment was old or secondhand, and it was the kind used to tinker with mRNAs, not lipids.
"It was scary," she says.
When Benenato saw the company had a nuclear magnetic resonance spectrometer, which allows chemists to see the molecular structure of material, she let out a sigh of relief. Then Benenato inspected the machine and realized it was a jalopy. The hulking, aging instrument had been decommissioned and left behind by a previous tenant, too old and banged up to bring with them.
Benenato began experimenting with different chemical changes for Moderna's LNPs, but without a working spectrometer she and her colleagues had to have samples ready by noon each day, so they could be picked up by an outside company that would perform the necessary analysis. After a few weeks, her superiors received an enormous bill for the outsourced work and decided to pay to get the old spectrometer running again.
After months of futility, Benenato became impatient. An overachiever who could be hard on herself, she was eager to impress her new bosses. Benenato felt pressure outside the office, as well. She was married with a preschool-age daughter and an eighteen-month-old son. In her last job, Benenato's commute had been a twenty-minute trip to Astra-Zeneca's office in Waltham, outside Boston; now she was traveling an hour to Moderna's Cambridge offices. She became anxious—how was she going to devote the long hours she realized were necessary to solve their LNP quandary while providing her children proper care? Joining Moderna was beginning to feel like a possible mistake.
She turned to her husband and father for help. They reminded her of the hard work she had devoted to establishing her career and said it would be a shame if she couldn't take on the new challenge. Benenato's husband said he was happy to stay home with the kids, alleviating some of her concerns.
Back in the office, she got to work. She wanted to make lipids that were easier for the body to chop into smaller pieces, so they could be eliminated by the body's enzymes. Until then, Moderna, like most others, relied on all kinds of complicated chemicals to hold its LNP packaging together. They weren't natural, though, so the body was having a hard time breaking them down, causing the toxicity.
Benenato began experimenting with simpler chemicals. She inserted "ester bonds"—compounds referred to in chemical circles as "handles" because the body easily grabs them and breaks them apart. Ester bonds had two things going for them: They were strong enough to help ensure the LNP remained stable, acting much like a drop of oil in water, but they also gave the body's enzymes something to target and break down as soon as the LNP entered the cell, a way to quickly rid the body of the potentially toxic LNP components. Benenato thought the inclusion of these chemicals might speed the elimination of the LNP delivery material.
This idea, Benenato realized, was nothing more than traditional, medicinal chemistry. Most people didn't use ester bonds because they were pretty unsophisticated. But, hey, the tricky stuff wasn't working, so Benenato thought she'd see if the simple stuff worked.
Benenato also wanted to try to replace a group of unnatural chemicals in the LNP that was contributing to the spirited and unwelcome response from the immune system. Benenato set out to build a new and improved chemical combination. She began with ethanolamine, a colorless, natural chemical, an obvious start for any chemist hoping to build a more complex chemical combination. No one relied on ethanolamine on its own.
Benenato was curious, though. What would happen if she used just these two simple modifications to the LNP: ethanolamine with the ester bonds? Right away, Benenato noticed her new, super-simple compound helped mRNA create some protein in animals. It wasn't much, but it was a surprising and positive sign. Benenato spent over a year refining her solution, testing more than one hundred variations, all using ethanolamine and ester bonds, showing improvements with each new version of LNP. After finishing her 102nd version of the lipid molecule, which she named SM102, Benenato was confident enough in her work to show it to Hoge and others.
They immediately got excited. The team kept tweaking the composition of the lipid encasement. In 2017, they wrapped it around mRNA molecules and injected the new combination in mice and then monkeys. They saw plentiful, potent proteins were being produced and the lipids were quickly being eliminated, just as Benenato and her colleagues had hoped. Moderna had its special sauce.
That year, Benenato was asked to deliver a presentation to Stephane Bancel, Moderna's chief executive, Afeyan, and Moderna's executive committee to explain why it made sense to use the new, simpler LNP formulation for all its mRNA vaccines. She still needed approval from the executives to make the change. Ahead of the meeting, she was apprehensive, as some of her earlier anxieties returned. But an unusual calm came over her as she began speaking to the group. Benenato explained how experimenting with basic, overlooked chemicals had led to her discovery.
She said she had merely stumbled onto the company's solution, though her bosses understood the efforts that had been necessary for the breakthrough. The board complimented her work and agreed with the idea of switching to the new LNP. Benenato beamed with pride.
"As a scientist, serendipity has been my best friend," she told the executives.
Over the next few years, Benenato and her colleagues would improve on their methods and develop even more tolerable and potent LNP encasement for mRNA molecules. Their work enabled Moderna to include higher doses of vaccine in its shots. In early 2020, Moderna developed Covid-19 shots that included 100 micrograms of vaccine, compared with 30 micrograms in the Pfizer-BioNTech vaccine. That difference appears to help the Moderna vaccine generate higher titers and provide more protection.
"You set out in a career in drug discovery to want to make a difference," Benenato says. "Seeing it come to reality has been surreal and emotional."
Editor's Note: This essay is excerpted from A SHOT TO SAVE THE WORLD: The Inside Story of the Life-or-Death Race for a COVID-19 Vaccine by Gregory Zuckerman, now on sale from Portfolio/Penguin.
*Jason Schrum's arthritis is now in complete remission, thanks to Humira (adalimumab), a TNF-alpha blocker.
Avalanche rescue dogs train to find and dig out people buried in snow slides
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”
