Biohackers Made a Cheap and Effective Home Covid Test -- But No One Is Allowed to Use It
Christi Guerrini, JD, MPH studies biomedical citizen science and is an Associate Professor at Baylor College of Medicine. Alex Pearlman, MA, is a science journalist and bioethicist who writes about emerging issues in biotechnology. They have recently launched outlawbio.org, a place for discussion about nontraditional research.
Last summer, when fast and cheap Covid tests were in high demand and governments were struggling to manufacture and distribute them, a group of independent scientists working together had a bit of a breakthrough.
Working on the Just One Giant Lab platform, an online community that serves as a kind of clearing house for open science researchers to find each other and work together, they managed to create a simple, one-hour Covid test that anyone could take at home with just a cup of hot water. The group tested it across a network of home and professional laboratories before being listed as a semi-finalist team for the XPrize, a competition that rewards innovative solutions-based projects. Then, the group hit a wall: they couldn't commercialize the test.
They wanted to keep their project open source, making it accessible to people around the world, so they decided to forgo traditional means of intellectual property protection and didn't seek patents. (They couldn't afford lawyers anyway). And, as a loose-knit group that was not supported by a traditional scientific institution, working in community labs and homes around the world, they had no access to resources or financial support for manufacturing or distributing their test at scale.
But without ethical and regulatory approval for clinical testing, manufacture, and distribution, they were legally unable to create field tests for real people, leaving their inexpensive, $16-per-test, innovative product languishing behind, while other, more expensive over-the-counter tests made their way onto the market.
Who Are These Radical Scientists?
Independent, decentralized biomedical research has come of age. Also sometimes called DIYbio, biohacking, or community biology, depending on whom you ask, open research is today a global movement with thousands of members, from scientists with advanced degrees to middle-grade students. Their motivations and interests vary across a wide spectrum, but transparency and accessibility are key to the ethos of the movement. Teams are agile, focused on shoestring-budget R&D, and aim to disrupt business as usual in the ivory towers of the scientific establishment.
Ethics oversight is critical to ensuring that research is conducted responsibly, even by biohackers.
Initiatives developed within the community, such as Open Insulin, which hopes to engineer processes for affordable, small-batch insulin production, "Slybera," a provocative attempt to reverse engineer a $1 million dollar gene therapy, and the hundreds of projects posted on the collaboration platform Just One Giant Lab during the pandemic, all have one thing in common: to pursue testing in humans, they need an ethics oversight mechanism.
These groups, most of which operate collaboratively in community labs, homes, and online, recognize that some sort of oversight or guidance is useful—and that it's the right thing to do.
But also, and perhaps more immediately, they need it because federal rules require ethics oversight of any biomedical research that's headed in the direction of the consumer market. In addition, some individuals engaged in this work do want to publish their research in traditional scientific journals, which—you guessed it—also require that research has undergone an ethics evaluation. Ethics oversight is critical to ensuring that research is conducted responsibly, even by biohackers.
Bridging the Ethics Gap
The problem is that traditional oversight mechanisms, such as institutional review boards at government or academic research institutions, as well as the private boards utilized by pharmaceutical companies, are not accessible to most independent researchers. Traditional review boards are either closed to the public, or charge fees that are out of reach for many citizen science initiatives. This has created an "ethics gap" in nontraditional scientific research.
Biohackers are seen in some ways as the direct descendents of "white hat" computer hackers, or those focused on calling out security holes and contributing solutions to technical problems within self-regulating communities. In the case of health and biotechnology, those problems include both the absence of treatments and the availability of only expensive treatments for certain conditions. As the DIYbio community grows, there needs to be a way to provide assurance that, when the work is successful, the public is able to benefit from it eventually. The team that developed the one-hour Covid test found a potential commercial partner and so might well overcome the oversight hurdle, but it's been 14 months since they developed the test--and counting.
In short, without some kind of oversight mechanism for the work of independent biomedical researchers, the solutions they innovate will never have the opportunity to reach consumers.
In a new paper in the journal Citizen Science: Theory & Practice, we consider the issue of the ethics gap and ask whether ethics oversight is something nontraditional researchers want, and if so, what forms it might take. Given that individuals within these communities sometimes vehemently disagree with each other, is consensus on these questions even possible?
We learned that there is no "one size fits all" solution for ethics oversight of nontraditional research. Rather, the appropriateness of any oversight model will depend on each initiative's objectives, needs, risks, and constraints.
We also learned that nontraditional researchers are generally willing (and in some cases eager) to engage with traditional scientific, legal, and bioethics experts on ethics, safety, and related questions.
We suggest that these experts make themselves available to help nontraditional researchers build infrastructure for ethics self-governance and identify when it might be necessary to seek outside assistance.
Independent biomedical research has promise, but like any emerging science, it poses novel ethical questions and challenges. Existing research ethics and oversight frameworks may not be well-suited to answer them in every context, so we need to think outside the box about what we can create for the future. That process should begin by talking to independent biomedical researchers about their activities, priorities, and concerns with an eye to understanding how best to support them.
Christi Guerrini, JD, MPH studies biomedical citizen science and is an Associate Professor at Baylor College of Medicine. Alex Pearlman, MA, is a science journalist and bioethicist who writes about emerging issues in biotechnology. They have recently launched outlawbio.org, a place for discussion about nontraditional research.
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.