Conner Curran, now 10 years old, can walk more than two miles after gene therapy treatment for his Duchenne's muscular dystrophy.
That the medical world is on the cusp of a successful treatment for a crippling and deadly disease is the culmination of more than 35 years of work by Dr. Jude Samulski, a professor of pharmacology at the University of North Carolina School of Medicine in Chapel Hill. More recently, he's become a leading gene therapy entrepreneur.
But Samulski likens this breakthrough to the frustrations of solving a Rubik's cube. "Just because one side is now all the color yellow does not mean that it is completely aligned," he says.
Although Conner's life and future have dramatically improved, he's not cured. The gene therapy tamed but did not extinguish his disorder: Conner is now suffering from the equivalent of Becker's muscular dystrophy, a milder form of the disease with symptoms that appear later in life and progress more slowly. Moreover, the loss of muscle cells Conner suffered prior to the treatment is permanent.
"It will take more time and more innovations," Samulski says of finding an even more effective gene therapy for muscular dystrophy.
Conner's family is still overjoyed with the results. "Jude's grit and determination gave Conner a chance at a new life, one that was not in his cards before gene therapy," says his mother Jessica Curran. She adds that "Conner is more confident than before and enjoys life, even though he has limitations, if compared to his brothers or peers."
Conner Curran holding a football post gene therapy treatment.
Courtesy of the Curran family
For now, the use of gene therapy as a treatment for diseases and disorders remains relatively isolated. On paper at least, progress appears glacially slow. In 2018, there were four FDA-approved gene therapies (excluding those reliant on bone marrow/stem cell transplants or implants). Today, there are 10. One therapy is solely for the cosmetic purpose of reducing facial lines and folds.
Nevertheless, experts in the space believe gene therapy is poised to expand dramatically.
"Certainly in the next three to five years you will see dozens of gene therapies and cell therapies be approved," says Dr. Pavan Cheruvu, who is CEO of Sio Gene Therapies in New York. The company is developing treatments for Parkinson's disease and Tay-Sachs, among other diseases.
Cheruvu's conclusion is supported by NEWDIGS, a think tank at the Massachusetts Institute of Technology that keeps tabs on gene therapy developments. NEWDIGS predicts there will be at least 60 gene therapies approved for use in the U.S. by the end of the decade. That number could be closer to 100 if Chinese researchers and biotech ventures decide the American market is a good fit for the therapies they develop.
"We are watching something of a conditional evolution, like a dot-com, or cellphones that were sizes of shoeboxes that have now matured to the size of wafers. Our space will follow along very similarly."
Dr. Carsten Brunn, a chemist by training and CEO of Selecta Biosciences outside of Boston, is developing ways to reduce the immune responses in patients who receive gene therapy. He observes that there are more than 300 therapies in development and thousands of clinical trials underway. "It's definitely an exciting time in the field," he says.
That's a far cry from the environment of little more than a decade ago. Research and investment in gene therapy had been brought low for years after the death of teenager Jesse Gelsinger in 1999 while he had been enrolled in a clinical trial to treat a liver disease. Gene therapy was a completely novel concept back then, and his death created existential questions about whether it was a proper pathway to pursue. Cheruvu, a cardiologist, calls the years after Gelsinger's death an "ice age" for gene therapy.
However, those dark years eventually yielded to a thaw. And while there have been some recent stumbles, they are considered part of the trial-and-error that has often accompanied medical research as opposed to an ominous "stop" sign.
The deaths of three patients last year receiving gene therapy for myotubular myopathy – a degenerative disease that causes severe muscle weakness – promptly ended the clinical trial in which they were enrolled. However, the incident caused few ripples beyond that. Researchers linked the deaths to dosage sizes that caused liver toxicity, as opposed to the gene therapy itself being an automatic death sentence; younger patients who received lower doses due to a less advanced disease state experienced improvements.
The gene sequencing and editing that helped create vaccines for COVID-19 in record time also bolstered the argument for more investment in research and development. Cheruvu notes that the field has usually been the domain of investors with significant expertise in the field; these days, more money is flowing in from generalists.
The Challenges Ahead
What will be the next step in gene therapy's evolution? Many of Samulski's earliest innovations came in the laboratory, for example. Then that led to him forming a company called AskBio in collaboration with the Muscular Dystrophy Association. AskBio sold its gene therapy to Pfizer five years ago to assure that enough could be manufactured for stage 3 clinical trials and eventually reach the market.
Cheruvu suggests that many future gene therapy innovations will be the result of what he calls "congruent innovation." That means publicly funded laboratories and privately funded companies might develop treatments separately or in collaboration. Or, university scientists may depend on private ventures to solve one of gene therapy's most vexing issues: producing enough finished material to test and treat on a large scale. "Manufacturing is a real bottleneck right now," Brunn says.
The alternative is referred to in the sector as the "valley of death": a lab has found a promising treatment, but is not far enough along in development to submit an investigational new drug application with the FDA. The promise withers away as a result. But the new abundance of venture capital for gene therapy has made this scenario less of an issue for private firms, some of which have received hundreds of millions of dollars in funding.
There are also numerous clinical challenges. Many gene therapies use what are known as adeno-associated virus vectors (AAVs) to deliver treatments. They are hollowed-out husks of viruses that can cause a variety of mostly mild maladies ranging from colds to pink eye. They are modified to deliver the genetic material used in the therapy. Most of these vectors trigger an antibody reaction that limits treatments to a single does or a handful of smaller ones. That can limit the potential progress for patients – an issue referred to as treatment "durability."
Although vectors from animals such as horses trigger far less of an antibody reaction in patients -- and there has been significant work done on using artificial vectors -- both are likely years away from being used on a large scale. "For the foreseeable future, AAV is the delivery system of choice," Brunn says.
Also, there will likely be demand for concurrent gene therapies that can lead to a complete cure – not only halting the progress of Duchenne's in kids like Conner Curran, but regenerating their lost muscle cells, perhaps through some form of stem cell therapy or another treatment that has yet to be devised.
Nevertheless, Samulski believes demand for imperfect treatments will be high – particularly with a disease such as muscular dystrophy, where many patients are mere months from spending the remainder of their lives in wheelchairs. But Samulski believes those therapies will also inevitably evolve into something far more effective.
"We are watching something of a conditional evolution, like a dot-com, or cellphones that were sizes of shoeboxes that have now matured to the size of wafers," he says. "Our space will follow along very similarly."
Jessica Curran will remain forever grateful for what her son has received: "Jude gave us new hope. He gave us something that is priceless – a chance to watch Conner grow up and live out his own dreams."
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.
