How pandemic pressure is re-engineering clinical trials | Science

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The pandemic’s shadow has Tami Minnier questioning everything about medical practice—including how clinical trials scrutinize it. “We have all these traditions that we haven’t really challenged,” says Minnier, a nurse at the University of Pittsburgh Medical Center (UPMC) and its chief quality officer.

Like many others who operate the global machinery of trials, which test everything from cancer drugs to the benefits of exercise, Minnier has spent years lamenting their shortcomings, including their sluggish pace, their shortage and lack of diversity of volunteers, and that they’re often too small to produce conclusive results. But change has been slow. COVID-19 upended that, compelling trials to adapt to new constraints, such as limits on in-person participation. It also led to a proliferation of sometimes creatively designed trials testing ways to fight the virus.

Clinical trialists hope many reforms will stick. “I’m really interested in the extent to which the pandemic has served as a forcing mechanism to change the conduct of trials for the better,” says Joseph Unger, a health services researcher and biostatistician at the Fred Hutchinson Cancer Research Center. “It’s like a natural experiment.”

UPMC, with hundreds of active clinical trials, is one of many places around the world where the experiment is playing out. Prepandemic, clinical research “could be methodical and careful and take years,” Minnier says, but when patients seriously ill with a new disease flooded emergency rooms, health care providers were awash in questions. Should they give steroids? Would flipping someone onto their stomach improve breathing? When should doctors resort to a ventilator? Queries quickly became research studies. This “questioning of, ‘Why are we doing what we’re doing’” in patient care was, Minnier says, “the driving force for me personally” to start to rethink so much across medicine.

A 10-minute walk away, at UPMC’s 600-bed main hospital, physician Derek Angus treats patients in the intensive care unit (ICU) and runs clinical trials for sepsis, a bloodstream infection. Although sepsis is the leading cause of death in hospitals, sepsis trials have, historically, accomplished little. Patients and families often decline to participate because the trials test one drug at a time, often with a 50% chance of getting a placebo. “Meanwhile, you’re really sick, in septic shock,” says Angus, who has tried for years to design what he calls “smarter, more efficient trials.”

After swiveling to COVID-19, Minnier and Angus bonded over their desire for trials that offer quick, clear answers. Part of the solution, they believe, is to fuse clinical research with patient care, which can both speed recruitment and lighten the burden on participants. For example, if a clinical trial requires the same blood test a patient’s doctor is seeking, Angus says, it could tap into those results rather than requiring a separate blood draw. Once patients are enrolled, he wants trials to feel seamless, a model he compares to the iPhone his elderly mother operates with ease. The phone is “designed to look effortless,” he says, “even though the engineering is more complicated now than it’s ever been.”

Behind the scenes, re-engineered data infrastructure at UPMC is smoothing the process. In the years preceding COVID-19, Oscar Marroquin, a cardiologist who leads the institution’s health care data and analytics activities, took steps to meld millions of electronic medical records with ongoing or potential trials, making it easier to flag patients who might be eligible or track them if enrolled. That system became more quickly integrated into research when COVID-19 struck, and Marroquin is now applying it beyond the coronavirus, for example to studies of cancer and back pain.

Minnier and Angus, who was recently named UPMC’s chief innovation officer, are also helping other UPMC researchers design statistically powerful studies that yield rapid results. One way to accomplish this is with a pragmatic trial, which assesses the power of interventions in patients already getting them. Another uses Bayesian statistics, which allow patients to be randomized in different ways as the trial progresses, depending on how a treatment performs.

UPMC physicians recently launched a COVID-19 trial that combines those innovations to study monoclonal antibodies, a treatment for recently diagnosed people. Anyone in the UPMC system and the nearby region who tests positive for COVID-19 and is eligible for the antibodies can be referred to get the therapy, and a UPMC physician explains that they will receive one of several options. Those who agree are automatically enrolled in the trial and their care—a single antibody infusion—doesn’t change. But behind the scenes, volunteers are randomly assigned to one of three antibody therapies, unless the doctor or patient requests a specific one. “No one knows if they all work equally well,” Angus says—an even more pressing question now, given the rise of the Omicron variant.

About 60% of those approached, nearly 10,000 people so far, have consented to get monoclonal antibodies and ended up in the study; typically, 10% to 20% of those eligible for a trial agree to participate. “The sample size is staggering,” Angus says, and both he and Minnier plan to stretch these trial designs beyond COVID-19. Angus recently met with pharmacists to discuss comparing different antibiotics in certain hospitalized patients. Minnier wants to do the same for blood pressure medications given in the ICU, and diabetes drugs for outpatients. Pragmatic and Bayesian trials aren’t new, but Angus and Minnier say COVID-19 underscored their appeal and feasibility.

“We may have underestimated our ability to pivot and change rapidly,” says Peter Merkel, a rheumatologist at the University of Pennsylvania who leads an international network called the Vasculitis Clinical Research Consortium. Several years earlier, Merkel received a grant to experiment with remote informed consent and patient visits for trials, but the project struggled because patients and providers weren’t always accepting of the technology, and the computer infrastructure was difficult to navigate.

Pandemic pressure made all the difference. This time, “We were able to turn what were pilot ideas to the norm,” Merkel says. COVID-19 forced his network to collect far fewer blood samples from volunteers, and the team is reconsidering the frequency of future sample collection and physical exams. “Are there things you can omit without doing major harm” to the trial, he wonders, such as imaging studies, tissue samples, and frequent in-person checkups?

“There’s really no good reason to go back” to the pre–COVID-19 trial model, agrees Yana Najjar, an oncologist at UPMC. Like Merkel, she reoriented her trial testing a drug combination for melanoma, allowing participants to stretch out the time between intravenous doses from 3 weeks to 6 and shipping medications to sites near her patients. These changes, Najjar and Merkel say, not only make life easier for existing participants, but may help diversify trials, by attracting people who live in rural areas, lack transportation, or struggle to find time and resources to visit a trial site regularly. “We want our trials to represent that population with the disease, not just the patients who live” near study sites, Merkel says. “This democratizes trials.”

The shift comes with hurdles. “There’s much more burden on the data science team,” says Manisha Desai, a biostatistician at Stanford University. A trial she’s involved in, on pulmonary arterial hypertension, assesses how far participants can walk in 6 minutes at home, while researchers gauge fatigue and shortness of breath via a video link—a COVID-19–driven change. Desai notes the data are “so much noisier than the gold standard,” which is measured on an in-person test at a clinic. There’s less uniformity among walking paths, for example. Unger is studying to what degree, if any, shunting parts of studies outside cancer centers has compromised quality.

At UPMC, the new normal is a work in progress. Marroquin doesn’t know yet how many researchers will be willing to link up their trials with his vast, new infrastructure. And he acknowledges that the system can’t help power trials beyond UPMC. Still, like others, he sees change enduring. A key goal, Angus says, is to spur enthusiasm among more patients by offering easier to navigate trials that minimize risk and demand less sacrifice. “The design should basically ask for as little altruism” as possible, he says—while tackling as many questions as it can to speed science along.


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