A company uses AI to fight muscle loss and unhealthy aging
There’s a growing need to slow down the aging process. The world’s population is getting older and, according to one estimate, 80 million Americans will be 65 or older by 2040. As we age, the risk of many chronic diseases goes up, from cancer to heart disease to Alzheimer’s.
BioAge Labs, a company based in California, is using genetic data to help people stay healthy for longer. CEO Kristen Fortney was inspired by the genetics of people who live long lives and resist many age-related diseases. In 2015, she started BioAge to study them and develop drug therapies based on the company’s learnings.
The team works with special biobanks that have been collecting blood samples and health data from individuals for up to 45 years. Using artificial intelligence, BioAge is able to find the distinctive molecular features that distinguish those who have healthy longevity from those who don’t.
In December 2022, BioAge published findings on a drug that worked to prevent muscular atrophy, or the loss of muscle strength and mass, in older people. Much of the research on aging has been in worms and mice, but BioAge is focused on human data, Fortney says. “This boosts our chances of developing drugs that will be safe and effective in human patients.”
How it works
With assistance from AI, BioAge measures more than 100,000 molecules in each blood sample, looking at proteins, RNA and metabolites, or small molecules that are produced through chemical processes. The company uses many techniques to identify these molecules, some of which convert the molecules into charged atoms and then separating them according to their weight and charge. The resulting data is very complex, with many thousands of data points from patients being followed over the decades.
BioAge validates its targets by examining whether a pathway going awry is actually linked to the development of diseases, based on the company’s analysis of biobank health records and blood samples. The team uses AI and machine learning to identify these pathways, and the key proteins in the unhealthy pathways become their main drug targets. “The approach taken by BioAge is an excellent example of how we can harness the power of big data and advances in AI technology to identify new drugs and therapeutic targets,” says Lorna Harries, a professor of molecular genetics at the University of Exeter Medical School.
Martin Borch Jensen is the founder of Gordian Biotechnology, a company focused on using gene therapy to treat aging. He says BioAge’s use of AI allows them to speed up the process of finding promising drug candidates. However, it remains a challenge to separate pathologies from aspects of the natural aging process that aren’t necessarily bad. “Some of the changes are likely protective responses to things going wrong,” Jensen says. “Their data doesn’t…distinguish that so they’ll need to validate and be clever.”
Developing a drug for muscle loss
BioAge decided to focus on muscular atrophy because it affects many elderly people, making it difficult to perform everyday activities and increasing the risk of falls. Using the biobank samples, the team modeled different pathways that looked like they could improve muscle health. They found that people who had faster walking speeds, better grip strength and lived longer had higher levels of a protein called apelin.
Apelin is a peptide, or a small protein, that circulates in the blood. It is involved in the process by which exercise increases and preserves muscle mass. BioAge wondered if they could prevent muscular atrophy by increasing the amount of signaling in the apelin pathway. Instead of the long process of designing a drug, they decided to repurpose an existing drug made by another biotech company. This company, called Amgen, had explored the drug as a way to treat heart failure. It didn’t end up working for that purpose, but BioAge took note that the drug did seem to activate the apelin pathway.
BioAge tested its new, repurposed drug, BGE-105, and, in a phase 1 clinical trial, it protected subjects from getting muscular atrophy compared to a placebo group that didn’t receive the drug. Healthy volunteers over age 65 received infusions of the drug during 10 days spent in bed, as if they were on bed rest while recovering from an illness or injury; the elderly are especially vulnerable to muscle loss in this situation. The 11 people taking BGE-105 showed a 100 percent improvement in thigh circumference compared to 10 people taking the placebo. Ultrasound observations also revealed that the group taking the durg had enhanced muscle quality and a 73 percent increase in muscle thickness. One volunteer taking BGE-105 did have muscle loss compared to the the placebo group.
Heather Whitson, the director of the Duke University Centre for the study of aging and human development, says that, overall, the results are encouraging. “The clinical findings so far support the premise that AI can help us sort through enormous amounts of data and identify the most promising points for beneficial interventions.”
More studies are needed to find out which patients benefit the most and whether there are side effects. “I think further studies will answer more questions,” Whitson says, noting that BGE-105 was designed to enhance only one aspect of physiology associated with exercise, muscle strength. But exercise itself has many other benefits on mood, sleep, bones and glucose metabolism. “We don’t know whether BGE-105 will impact these other outcomes,” she says.
The future
BioAge is planning phase 2 trials for muscular atrophy in patients with obesity and those who have been hospitalized in an intensive care unit. Using the data from biobanks, they’ve also developed another drug, BGE-100, to treat chronic inflammation in the brain, a condition that can worsen with age and contributes to neurodegenerative diseases. The team is currently testing the drug in animals to assess its effects and find the right dose.
BioAge envisions that its drugs will have broader implications for health than treating any one specific disease. “Ultimately, we hope to pioneer a paradigm shift in healthcare, from treatment to prevention, by targeting the root causes of aging itself,” Fortney says. “We foresee a future where healthy longevity is within reach for all.”
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.