For years, pharma has rhapsodized about its love of real-world data and bragged about the preponderance of information soon to be within its grasp. Finally, it seems, the on-the-ground reality matches the industry’s lofty opinions of its collection and analysis efforts.
Take Takeda, which has been at the forefront of the RWE push with studies of the effectiveness of its ulcerative colitis/Crohn’s drug Entyvio and its multiple myeloma treatments. The latter ambitiously aimed to enroll 5,000 patients around the world.
“There’s immense demand for real-world data, and it’s only increasing,” says Hui Huang, Takeda’s executive director, head of global outcomes research, oncology. “It’s the right thing to do for patients — and whenever we do right for patients, we do right for the future of drug development.”
Then there’s the ongoing AIRWISE collaboration, billed by backers Anthem, Healthcore, and Boehringer Ingelheim as “the world’s largest pragmatic clinical trial in COPD.” AIRWISE is designed to test the real-world effectiveness of Boehringer Ingelheim’s Stiolto Respimat inhalation spray against any commercially available combination of a long-acting muscarinic antagonist, a long-acting beta-agonist, and an inhaled corticosteroid.
The three organizations say it’s too early to even think about discussing results — the program kicked off in early October — but they’re thrilled with what they’ve seen so far. In a statement, Anthem medical director Dr. Mark Napier enthused about the possibility of securing “real-world data on a broad population of COPD patients that cannot be obtained from traditional randomized clinical trials. This will be invaluable for clinicians and healthcare organizations alike.”
Also, patients have warmed to the prospect of sharing more of their data. As part of the annual Consumer Survey conducted by the PwC Health Research Institute, they were asked, “For what purposes would you be comfortable having your medical and health information shared among healthcare organizations?” Nearly half (48%) said they’d be OK with it “to measure the safety and effectiveness of a drug I’m taking,” while 31% would permit the sharing of data “to determine whether the performance of a drug I’m taking justifies the cost.” Neither figure tops the 50% mark, but in years past, the percentages barely reached double-digits. That constitutes progress.
So for all the talk about how efforts to secure real-world data remain a work in progress — and how technologists are hard at work implementing AI and machine learning to buttress them — they are no longer hypothetical. They’re happening as we speak and sophisticated beyond what anybody could have expected a year or two ago.
Tech-enabled real-world evidence boosters in and around pharma say they’re just getting started. “Like anything else, it’s a journey,” says Andrea Brückner, head of Accenture’s Life Sciences practice in Europe, whose group is working with Roche on a program to more efficiently steer data toward tumor boards. “There’s already sophistication in leveraging big data to make more educated decisions for patients. The difference is now you’re seeing a lot of pilots and the learning that comes with them.”
Such sentiments are music to the ears of pharmacy benefit managers, whose receptivity to real-world data might best be characterized as “the more the merrier.” Programs that employ AI or machine learning appeal to them, but so too does any analytic approach that helps them distinguish between one product and the next.
“I look at integrated claims and other [real-world] data every day. I answer business questions with the data daily,” says Patrick Gleason, senior director, health outcomes at Prime Therapeutics. “If we’re weighing one product over another, where one might have more cost, you take everything you have to make an informed decision.”
Gleason points to Prime’s attempts to differentiate the real-world effectiveness of two relatively new insulins, Sanofi’s Toujeo and Novo Nordisk’s Tresiba.
“One thing that could make one of them better than the other is lower rates of hypoglycemic events. People who have these events can wind up in an ER, which is costly,” he explains. “So how much of this is really happening out there? We’ve found it’s very rare: 22 ER visits per month per million people.”
Left unmentioned is the cost-benefit analysis. If you’re only able to knock down the incidence of ER visits for hypoglycemic events by, say, 15%, you’re probably not going to overpay for high-cost drugs.
“The real-world data helps us set good, reliable expectations,” Gleason adds.
DRAWBACKS AND CHALLENGES
The drawback is that there’s rarely a link this overt. “There are times when you feel good about drawing conclusions and times when you don’t, no matter how much data you have and what tools you’re working with,” he continues.
If there’s an effective way to access lab data or electronic medical records and merge them into the mix, pharma hasn’t yet stumbled upon it. Everybody believes genomic information will add a dimension to such efforts, but that likely remains several years away.
Worries about transparency haven’t been quelled. As much as pharma has bolstered its data security, the industry is conscious one high-profile breach could torpedo all its hard work.
“Sample sizes and methodology are a challenge. So is taking unstructured data and putting it into a database with structured data,” Huang says. “We’re getting better in all these areas, but the whole field is evolving at a time when there’s so much demand.”
Still, you’d be hard-pressed to find anyone who isn’t bullish about the real-world evidence surge and the tech that facilitates it. Brückner points to an increasing level of investment, both by pharma and nontraditional players outside the life sciences space. “That creates an even bigger opportunity going forward, especially in personalized healthcare,” she notes.
Huang says Takeda hopes to collaborate more effectively with patient communities, noting the company continues to invite proposals for avenues of data analysis that is already being collected. She is encouraged by the response, just as she is by the feedback from people under her own roof.
“You used to get blank stares,” she says. “Now, when you say, ‘Let’s measure missed days of work in patients who take orals versus patients who have IVs,’ people get it. When we add all the new tech and AI in this field, it’s going to expand even faster.”