As gene therapies and small molecule drugs are being studied in clinical trials, companies increasingly see the value in hiring patients to help explain the potential benefits.
Biomedical corporations and government research agencies are now incorporating patient advocacy more than ever, says Alice Lara, president and CEO of the Sudden Arrhythmia Death Syndromes Foundation in Salt Lake City, Utah. These organizations have seen the effectiveness of including patient voices to communicate and exemplify the benefits that key academic research institutions have shown in their medical studies.
“From our side of the aisle,” Lara says, “what we know as patient advocacy organizations is that educated patients do a lot better. They have a better course in their therapy and their condition, and understanding the genetics is important because all of our conditions are genetic.”
Founded in 2016, Tenaya is advancing gene therapies and small molecule drugs in clinical trials for both prevalent and rare forms of heart disease, says Ali, the CEO.
The firm's first small molecule, now in a Phase 1 clinical trial, is intended to treat heart failure with preserved ejection fraction, where the amount of blood pumped by the heart is reduced due to the heart chambers becoming weak or stiff. The condition accounts for half or more of all heart failure in the U.S., according to Ali, and is growing quickly because it's closely associated with diabetes. It’s also linked with metabolic syndrome, or a cluster of conditions including high blood pressure, high blood sugar, excess body fat around the waist, and abnormal cholesterol levels.
“We have a novel molecule that is first in class and, to our knowledge, best in class to tackle that, so we’re very excited about the clinical trial,” Ali says.
The first phase of the trial is being performed with healthy participants, rather than people with the disease, to establish safety and tolerability. The researchers can also look for the drug in blood samples, which could tell them whether it's reaching its target. Ali estimates that, if the company can establish safety and that it engages the right parts of the body, it will likely begin dosing patients with the disease in 2024.
Tenaya’s therapy delivers a healthy copy of the gene so that it makes a copy of the protein missing from the patients' hearts because of their mutation. The study will start with adult patients, then pivot potentially to children and even newborns, Ali says, “where there is an even greater unmet need because the disease progresses so fast that they have no options.”
Although this work still has a long way to go, Ali is excited about the potential because the gene therapy achieved positive results in the preclinical mouse trial. This animal trial demonstrated that the treatment reduced enlarged hearts, reversed electrophysiological abnormalities, and improved the functioning of the heart by increasing the ejection fraction after the single-dose of gene therapy. That measurement remained stable to the end of the animals’ lives, roughly 18 months, Ali says.
He’s also energized by the fact that heart disease has “taken a page out of the oncology playbook” by leveraging genetic research to develop more precise and targeted drugs and gene therapies.
“Now we are talking about a potential cure of a disease for which there was no cure and using a very novel concept,” says Melind Desai of the Cleveland Clinic.
Tenaya’s second program focuses on developing a gene therapy to mitigate the leading cause of hypertrophic cardiomyopathy through a specific gene called MYPBC3. The disease affects approximately 600,000 patients in the U.S. This particular genetic form, Ali explains, affects about 115,000 in the U.S. alone, so it is considered a rare disease.
“There are infants who are dying within the first weeks to months of life as a result of this mutation,” he says. “There are also adults who start having symptoms in their 20s, 30s and 40s with early morbidity and mortality.” Tenaya plans to apply before the end of this year to get the FDA’s approval to administer an investigational drug for this disease humans. If approved, the company will begin to dose patients in 2023.
“We now understand the genetics of the heart much better,” he says. “We now understand the leading genetic causes of hypertrophic myopathy, dilated cardiomyopathy and others, so that gives us the ability to take these large populations and stratify them rationally into subpopulations.”
Melind Desai, MD, who directs Cleveland Clinic’s Hypertrophic Cardiomyopathy Center, says that the goal of Tenaya’s second clinical study is to help improve the basic cardiac structure in patients with hypertrophic cardiomyopathy related to the MYPBC3 mutation.
“Now we are talking about a potential cure of a disease for which there was no cure and using a very novel concept,” he says. “So this is an exciting new frontier of therapeutic investigation for MYPBC3 gene-positive patients with a chance for a cure.
Neither of Tenaya’s two therapies address the gene mutation that has affected Borsari and her family. But Ali sees opportunity down the road to develop a gene therapy for her particular gene mutation, since it is the second leading cause of cardiomyopathy. Treating the MYH7 gene is especially challenging because it requires gene editing or silencing, instead of just replacing the gene.
Wendy Borsari was diagnosed at age 24 with a commonly inherited heart disease. She joined Tenaya as a patient advocate in 2021.
Wendy Borsari
“If you add a healthy gene it will produce healthy copies,” Ali explains, “but it won’t stop the bad effects of the mutant protein the gene produces. You can only do that by silencing the gene or editing it out, which is a different, more complicated approach.”
Euan Ashley, professor of medicine and genetics at Stanford University and founding director of its Center for Inherited Cardiovascular Disease, is confident that we will see genetic therapies for heart disease within the next decade.
“We are at this really exciting moment in time where we have diseases that have been under-recognized and undervalued now being attacked by multiple companies with really modern tools,” says Ashley, author of The Genome Odyssey. “Gene therapies are unusual in the sense that they can reverse the cause of the disease, so we have the enticing possibility of actually reversing or maybe even curing these diseases.”
Although no one is doing extensive research into a gene therapy for her particular mutation yet, Borsari remains hopeful, knowing that companies such as Tenaya are moving in that direction.
“I know that’s now on the horizon,” she says. “It’s not just some pipe dream, but will happen hopefully in my lifetime or my kids’ lifetime to help them.”
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.
