The Nobel Prize-Winning Treatment Approach That Could Tackle COVID-19
In October 2006, Craig Mello received a strange phone call from Sweden at 4:30 a.m. The voice at the other end of the line told him to get dressed and that his life was about to change.
"We think this could be effective in [the early] phase, helping the body clear the virus and preventing progression to that severe hyperimmune response which occurs in some patients."
Shortly afterwards, he was informed that along with his colleague Andrew Fire, he had won the Nobel Prize in Physiology or Medicine.
Eight years earlier, biologists Fire and Mello had made a landmark discovery in the history of genetics. In a series of experiments conducted in worms, they had revealed an ancient evolutionary mechanism present in all animals that allows RNA – the structures within our cells that take genetic information from DNA and use it to make proteins – to selectively switch off genes.
At the time, scientists heralded the dawn of a new field of medical research utilizing this mechanism, known as RNA interference or RNAi, to tackle rare genetic diseases and deactivate viruses. Now, 14 years later, the pharmaceutical company Alnylam — which has pioneered the development of RNAi-based treatments over the past decade — is looking to use it to develop a groundbreaking drug for the virus that causes COVID-19.
"We can design small interfering RNAs to target regions of the viral genome and bind to them," said Akin Akinc, who manages several of Alnylam's drug development programs. "What we're learning about COVID-19 is that there's an early phase where there's lots of viral replication and a high viral load. We think this could be effective in that phase, helping the body clear the virus and preventing progression to that severe hyperimmune response which occurs in some patients."
Called ALN-COV, Alnylam's treatment hypothetically works by switching off a key gene in the virus, inhibiting its ability to replicate itself. In order to deliver it to the epithelial cells deep in the lung tissue, where the virus resides, patients will inhale a fine mist containing the RNAi molecules mixed in a saline solution, using a nebulizer.
But before human trials of the drug can begin, the company needs to convince regulators that it is both safe and effective in a series of preclinical trials. While early results appear promising - when mixed with the virus in a test tube, the drug displayed a 95 percent inhibition rate – experts are reserving judgment until it performs in clinical trials.
"If successful this could be a very important milestone in the development of RNAi therapies, but virus infections are very complicated and it can be hard to predict whether a given level of inhibition in cell culture will be sufficient to have a significant impact on the course of the infection," said Si-Ping Han, who researches RNAi therapeutics at California Institute of Technology and is not involved in the development of this drug.
So far, Alnylam has had success in using RNAi to treat rare genetic diseases. It currently has treatments licensed for Hereditary ATTR Amyloidosis and Acute Hepatic Porphyria. Another treatment, for Primary Hyperoxaluria Type 1, is currently under regulatory review. But its only previous attempt to use RNAi to tackle a respiratory infection was a failed effort to develop a drug for respiratory syncytial virus (RSV) almost a decade ago.
However, the technology has advanced considerably since then. "Back then, RNAi drugs had no chemical modifications whatsoever, so they were readily degraded by the body, and they could also result in unintended immune stimulation," said Akinc. "Since then, we've learned how to chemically modify our RNAi's to make them immunosilent and give them improved potency, stability, and duration of action."
"It would be a very important milestone in the development of RNAi therapies."
But one key challenge the company will face is the sheer speed at which viruses evolve, meaning they can become drug-resistant very quickly. Scientists predict that Alnylam will ultimately have to develop a series of RNAi drugs for the coronavirus that work together.
"There's been considerable interest in using RNAi to treat viral infections, as RNA therapies can be developed more rapidly than protein therapies like monoclonal antibodies, since one only needs to know the viral genome sequence to begin to design them," said David Schaffer, professor of bioengineering at University of California, Berkeley. "But viruses can evolve their sequences rapidly around single drugs so it is likely that a combinatorial RNAi therapy may be needed."
In the meantime, Alnylam is conducting further preclinical trials over the summer and fall, with the aim of launching testing in human volunteers by the end of this year -- an ambitious aim that would represent a breakneck pace for a drug development program.
If the approach does ultimately succeed, it would represent a major breakthrough for the field as a whole, potentially opening the door to a whole new wave of RNAi treatments for different lung infections and diseases.
"It would be a very important milestone in the development of RNAi therapies," said Han, the Caltech researcher. "It would be both the first time that an RNAi drug has been successfully used to treat a respiratory infection and as far as I know, the first time that one has been successful in treating any disease in the lungs. RNAi is a platform that can be reconfigured to hit different targets, and so once the first drug has been developed, we can expect a rapid flow of variants targeting other respiratory infections or other lung diseases."
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.