Scientists search for a universal coronavirus vaccine
The Covid-19 pandemic had barely begun when VBI Vaccines, a biopharmaceutical company based in Cambridge, Massachusetts, initiated their search for a universal coronavirus vaccine.
It was March 2020, and while most pharmaceutical companies were scrambling to initiate vaccine programs which specifically targeted the SARS-CoV-2 virus, VBI’s executives were already keen to look at the broader picture.
Having observed the SARS and MERS coronavirus outbreaks over the last two decades, Jeff Baxter, CEO of VBI Vaccines, was aware that SARS-CoV-2 is unlikely to be the last coronavirus to move from an animal host into humans. “It's absolutely apparent that the future is to create a vaccine which gives more broad protection against not only pre-existing coronaviruses, but those that will potentially make the leap into humans in future,” says Baxter.
It was a prescient decision. Over the last two years, more biotechs and pharma companies have joined the search to find a vaccine which might be able to protect against all coronaviruses, along with dozens of academic research groups. Last September, the US National Institutes of Health dedicated $36 million specifically to pan-coronavirus vaccine research, while the global Coalition for Epidemic Preparedness Innovations (CEPI) has earmarked $200 million towards the effort.
Until October 2021, the very concept of whether it might be
theoretically possible to vaccinate against multiple coronaviruses remained an open question. But then a groundbreaking study renewed optimism.
The emergence of new variants of Covid-19 over the past year, particularly the highly mutated Omicron variant, has added greater impetus to find broader spectrum vaccines. But until October 2021, the very concept of whether it might be theoretically possible to vaccinate against multiple coronaviruses remained an open question. After all, scientists have spent decades trying to develop a similar vaccine for influenza with little success.
But then a groundbreaking study from renowned virologist Linfa Wang, who runs the emerging infectious diseases program at Duke-National University of Singapore Medical School, provided renewed optimism.
Wang found that eight SARS survivors who had been injected with the Pfizer/BioNTech Covid-19 vaccine had neutralising antibodies in their blood against SARS, the Alpha, Beta and Delta variants of SARS-CoV-2, and five other coronaviruses which reside in bats and pangolins. He concluded that the combination of past coronavirus infection, and immunization with a messenger RNA vaccine, had resulted in a wider spectrum of protection than might have been expected.
“This is a significant study because it showed that pre-existing immunity to one coronavirus could help with the elicitation of cross-reactive antibodies when immunizing with a second coronavirus,” says Kevin Saunders, Director of Research at the Duke Human Vaccine Institute in North Carolina, which is developing a universal coronavirus vaccine. “It provides a strategy to perhaps broaden the immune response against coronaviruses.”
In the next few months, some of the first data is set to emerge looking at whether this kind of antibody response could be elicited by a single universal coronavirus vaccine. In April 2021, scientists at the Walter Reed Army Institute of Research in Silver Spring, Maryland, launched a Phase I clinical trial of their vaccine, with a spokesman saying that it was successful, and the full results will be announced soon.
The Walter Reed researchers have already released preclinical data, testing the vaccine in non-human primates where it was found to have immunising capabilities against a range of Covid-19 variants as well as the original SARS virus. If the Phase I trial displays similar efficacy, a larger Phase II trial will begin later this year.
Two different approaches
Broadly speaking, scientists are taking two contrasting approaches to the task of finding a universal coronavirus vaccine. The Walter Reed Army Institute of Research, VBI Vaccines – who plan to launch their own clinical trial in the summer – and the Duke Human Vaccine Institute – who are launching a Phase I trial in early 2023 – are using a soccer-ball shaped ferritin nanoparticle studded with different coronavirus protein fragments.
VBI Vaccines is looking to elicit broader immune responses by combining SARS, SARS-CoV-2 and MERS spike proteins on the same nanoparticle. Dave Anderson, chief scientific officer at VBI Vaccines, explains that the idea is that by showing the immune system these three spike proteins at the same time, it can help train it to identify and respond to subtle differences between coronavirus strains.
The Duke Human Vaccine Institute is utilising the same method, but rather than including the entire spike proteins from different coronaviruses, they are only including the receptor binding domain (RBD) fragment from each spike protein. “We designed our vaccine to focus the immune system on a site of vulnerability for the virus, which is the receptor binding domain,” says Saunders. “Since the RBD is small, arraying multiple RBDs on a nanoparticle is a straight-forward approach. The goal is to generate immunity to many different subgenuses of viruses so that there will be cross-reactivity with new or unknown coronaviruses.”
But the other strategy is to create a vaccine which contains regions of the viral protein structure which are conserved between all coronavirus strains. This is something which scientists have tried to do for a universal influenza vaccine, but it is thought to be more feasible for coronaviruses because they mutate at a slower rate and are more constrained in the ways that they can evolve.
DIOSynVax, a biotech based in Cambridge, United Kingdom, announced in a press release earlier this month that they are partnering with CEPI to use their computational predictive modelling techniques to identify common structures between all of the SARS coronaviruses which do not mutate, and thus present good vaccine targets.
Stephen Zeichner, an infectious disease specialist at the University of Virginia Medical Center, has created an early stage vaccine using the fusion peptide region – another part of the coronavirus spike protein that aids the virus’s entry into host cells – which so far appears to be highly conserved between all coronaviruses.
So far Zeichner has trialled this version of the vaccine in pigs, where it provided protection against a different coronavirus called porcine epidemic diarrhea virus, which he described as very promising as this virus is from a different family called alphacoronaviruses, while SARS-CoV-2 is a betacoronavirus.
“If a betacoronavirus fusion peptide vaccine designed from SARS-CoV-2 can protect pigs against clinical disease from an alphacoronavirus, then that suggests that an analogous vaccine would enable broad protection against many, many different coronaviruses,” he says.
The road ahead
But while some of the early stage results are promising, researchers are fully aware of the scale of the challenge ahead of them. Although CEPI have declared an aim of having a licensed universal coronavirus vaccine available by 2024-2025, Zeichner says that such timelines are ambitious in the extreme.
“I was incredibly impressed at the speed at which the mRNA coronavirus vaccines were developed for SARS-CoV-2,” he says. “That was faster than just about anybody anticipated. On the other hand, I think a universal coronavirus vaccine is more equivalent to the challenge of developing an HIV vaccine and we're 35 years into that effort without success. We know a lot more now than before, and maybe it will be easier than we think. But I think the route to a universal vaccine is harder than an individual vaccine, so I wouldn’t want to put money on a timeline prediction.”
The major challenge for scientists is essentially designing a vaccine for a future threat which is not even here yet. As such, there are no guidelines on what safety data would be required to license such a vaccine, and how researchers can demonstrate that it truly provides efficacy against all coronaviruses, even those which have not yet jumped to humans.
The teams working on this problem have already devised some ingenious ways of approaching the challenge. VBI Vaccines have taken the genetic sequences of different coronaviruses found in bats and pangolins, from publicly available databases, and inserted them into what virologists call a pseudotype virus – one which has been engineered so it does not have enough genetic material to replicate.
This has allowed them to test the neutralising antibodies that their vaccine produces against these coronaviruses in test tubes, under safe lab conditions. “We have literally just been ordering the sequences, and making synthetic viruses that we can use to test the antibody responses,” says Anderson.
However, some scientists feel that going straight to a universal coronavirus vaccine is likely to be too complex. Instead they say that we should aim for vaccines which are a little more specific. Pamela Bjorkman, a structural biologist at the California Institute of Technology, suggests that pan-coronavirus vaccines which protect against SARS-like betacoronaviruses such as SARS or SARS-CoV-2, or MERS-like betacoronaviruses, may be more realistic.
“I think a vaccine to protect against all coronaviruses is likely impossible since there are so many varieties,” she says. “Perhaps trying to narrow down the scope is advisable.”
But if the mission to develop a universal coronavirus vaccine does succeed, it will be one of the most remarkable feats in the annals of medical science. In January, US chief medical advisor Anthony Fauci urged for greater efforts to be devoted towards this goal, one which scientists feel would be the biological equivalent of the race to develop the first atomic bomb
“The development of an effective universal coronavirus vaccine would be equally groundbreaking, as it would have global applicability and utility,” says Saunders. “Coronaviruses have caused multiple deadly outbreaks, and it is likely that another outbreak will occur. Having a vaccine that prevents death from a future outbreak would be a tremendous achievement in global health.”
He agrees that it will require creativity on a remarkable scale: “The universal coronavirus vaccine will also require ingenuity and perseverance comparable to that needed for the Manhattan project.”
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.