Virtual Clinical Trials Are Letting More People of Color Participate in Research
Herman Taylor, director of the cardiovascular research institute at Morehouse college, got in touch with UnitedHealth Group early in the pandemic.
The very people who most require solutions to COVID are those who are least likely to be involved in the search to find them.
A colleague he worked with at Grady Hospital in Atlanta was the guy when it came to studying sickle cell disease, a recessive genetic disorder that causes red blood cells to harden into half-moon shapes, causing cardiovascular problems. Sickle cell disease is more common in African Americans than it is in Caucasians, in part because having just one gene for the disease, called sickle cell trait, is protective against malaria, which is endemic to much of Africa. Roughly one in 12 African Americans carry sickle cell trait, and Taylor's colleague wondered if this could be one factor affecting differential outcomes for COVID-19.
UnitedHealth Group granted Taylor and his colleague the money to study sickle cell trait in COVID, and then, as they continued working together, they began to ask Taylor his opinion on other topics. As an insurance company, United had realized early in the pandemic that it was sitting on a goldmine of patient data—some 120 million patients' worth—that it could sift through to look for potential COVID treatments.
Their researchers thought they had found one: In a small subset of 14,000 people who'd contracted COVID, there was a group whose bills were paid by Medicare (which the researchers took as a proxy for older age). The people in this group who were taking ACE inhibitors, blood vessel dilators often used to treat high blood pressure, were 40 percent less likely to be hospitalized than those who were not taking the drug.
The connection between ACE inhibitors and COVID hospitalizations was a correlation, a statistical association. To determine whether the drugs had any real effect on COVID outcomes, United would have to perform another, more rigorous study. They would have to assign some people to receive small doses of ACE inhibitors, and others to receive placebos, and measure the outcomes under each condition. They planned to do this virtually, allowing study participants to sign up and be screened online, and sending drugs, thermometers, and tests through the mail. There were two reasons to do it this way: First, the U.S. Food and Drug Administration had been advising medical researchers to embrace new strategies in clinical trials as a way to protect participants during the pandemic.
The second reason was why they asked Herman Taylor to co-supervise it: Clinical trials have long had a diversity problem. And going virtual is a potential solution.
Since the beginning of the pandemic, COVID-19 has infected people of color at a rate of three times that of Caucasians (killing Black people at a rate 2.5 times as high, and Hispanic and American Indian or Alaska Native people at a rate 1.3 times as high). A number of explanations have been put forth to explain this disproportionate toll. Among them: higher levels of poverty, essential jobs that increase exposure, and lower quality or inadequate access to medical care.
Unfortunately, these same factors also affect who participates in research. People of color may be less likely to have doctors recommend studies to them. They may not have the time or the resources to hang out in a waiting room for hours. They may not live near large research institutions that conduct trials. The result is that new treatments, even for diseases that affect Latin, Native American, or African American populations in greater proportions, are studied mostly in white volunteers. The very people who most require solutions to COVID are those who are least likely to be involved in the search to find them.
Virtual trials can alleviate a number of these problems. Not only can people find and request to participate in these types of trials through their phones or computers, virtual trials also cover more costs, include a larger geographical range, and have inherently flexible hours.
"[In a traditional study] you have to go to a doctor's office to enroll and drive a couple of hours and pay $20 for parking and pay $15 for a sandwich in the hospital cafeteria and arrange for daycare for your kids and take time off of work," says Dr. Jonathan Cotliar, chief medical officer of Science37, a platform that investigators can hire to host and organize their trials virtually. "That's a lot just for one visit, much less over the course of a six to 12-month study."
Cotliar's data suggests that virtual trials' enhanced access seriously affects the racial makeup of a given study's participant pool. Sixty percent of patients enrolled in Science37 trials are non-Caucasian, which is, Cotliar says, "staggering compared to what you find in traditional site-based research."
But access is not the only barrier to including more people of color in clinical trials. There is also trust. When agreeing to sign up for research, undocumented immigrants may worry about finding themselves in legal trouble or without any medical support should something go wrong. In a country with a history of experimenting on African Americans without their consent, black people may not trust institutions not to use them as guinea pigs.
"A lot of people report being somewhat disregarded or disrespected once entering the healthcare system," Taylor says. "You take it all together, then people wonder, well, okay, this is how the system tends to regard me, yet now here come these people doing research, and they're all about getting me into their studies." Not so surprising that a lot of people may respond with a resounding "No thanks."
United's ACE inhibitor trial was notable for addressing both of these challenges. In addition to covering costs and allowing study subjects to participate from their own homes, it was being co-sponsored by a professor at Morehouse, one of the country's historic black colleges and universities (often abbreviated HBCUs). United was recruiting heavily in Atlanta, whose population is 52 percent African American. The study promised a thoughtful introduction to a more egalitarian future of medical research.
There's just one problem: It isn't going to happen.
This month, in preparation for the study, United reanalyzed their ACE inhibitor data with all the new patients who'd contracted COVID in the months since their first analysis. Their original data set had been concentrated in the Northeast, mostly New York City, where the earliest outbreak took place. In the 12 weeks it had taken them to set up the virtual followup study, epicenters had shifted. United's second, more geographically comprehensive sample had ten times the number of people in it. And in that sample, the signal simply disappeared.
"I was shocked, but that's the reality," says Deneen Vojta, executive vice president of enterprise research and development for UnitedHealth Group. "You make decisions based on the data, but when you get more data, more information, you might make a different decision. The answer is the answer."
There was no point in running a virtual ACE inhibitor study if a larger, more representative sample of people indicated the drug was unlikely to help anyone. Still, the model United had established to run the trial remains viable. Even as she scrapped the ACE inhibitor study, Vojta hoped not just to continue United's relationship with Dr. Taylor and Morehouse, but to formalize it. Virtual platforms are still an important part of their forthcoming trials.
If people don't believe a vaccine has been created with them in mind, then they won't take it, and it won't matter whether it exists or not.
United is not alone in this approach. As phase three trials for vaccines against SARS CoV-2 get underway, big pharma companies have been publicly articulating their own strategies for including more people of color in clinical trials, and many of these include virtual elements. Janelle Sabo, global head of clinical innovation, systems and clinical supply chain at Eli Lilly, told me that the company is employing home health and telemedicine, direct-to-patient shipping and delivery, and recruitment using social media and geolocation to expand access to more diverse populations.
Dr. Macaya Douoguih, Head of Clinical Development and Medical Affairs for Janssen Vaccines under Johnson & Johnson, spoke to Congress about this issue during a July hearing before the House Energy and Commerce Oversight and Investigations Subcommittee. She said that the company planned to institute a "focused digital and community outreach plan to provide resources and opportunities to encourage participation in our clinical trials," and had partnered with Johns Hopkins Bloomberg School of Public Health "to understand how the COVID-19 crisis is affecting different communities in the United States."
But while some of these plans are well thought-out, others are concerningly nebulous, featuring big pronouncements but fewer tangible strategies. In that same July hearing, Massachusetts representative Joe Kennedy III (D) sounded like a frustrated teacher when admonishing four of the five leads of the present pharma companies (AstraZeneca, Johnson & Johnson, Merck, Moderna, and Pfizer) for not explaining exactly how they'd ensure diversity both in the study of their vaccines, and in their eventual distribution.
This matters: The uptake of the flu vaccine is 10 percentage points lower in both the African American and Hispanic communities than it is in Caucasians. A Pew research study conducted early in the pandemic found that just 54 percent of Black adults said they "would definitely or probably get a coronavirus vaccine," compared to 74 percent of Whites and Hispanics.
"As a good friend of mine, Dr. [James] Hildreth, president at Meharry, another HBC medical school, likes to say: 'A vaccine is great, but it is the vaccination that saves people,'" Taylor says. If people don't believe a vaccine has been created with them in mind, then they won't take it, and it won't matter whether it exists or not.
In this respect, virtual platforms remain an important first step, at least in expanding admittance. In June, United Health opened up a trial to their entire workforce for a computer game that could treat ADHD. In less than two months, 1,743 people had signed up for it, from all different socioeconomic groups, from all over the country. It was inching closer to the kind of number you need for a phase three vaccine trial, which can require tens of thousands of people. Back when they'd been planning the ACE inhibitor study, United had wanted 9,600 people to agree to participate.
Now, with the help of virtual enrollment, they hope they can pull off similarly high numbers for the COVID vaccine trial they will be running for an as-yet-unnamed pharmaceutical partner. It stands to open in September.
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.