The FDA process for approving drugs and medical devices requires controlled clinical studies, traditionally within brick-and-mortar settings. Such traditional research will always be crucial – but it’s not the lone source of information that regulators, insurers, and manufacturers should consider. Real-world use of approved products in the context of people’s everyday lives, if used correctly, can provide additional valuable insights. A new crop of startups is helping regulators and global healthcare companies make use of this data.  

B Capital Group portfolio companies Evidation and Aetion are together at the forefront of gathering insights or “real-world evidence” (RWE) from this real-world data. RWD can refer to new data people create day-to-day through products such as wearables, as well as other data  patients generate by interacting with the healthcare system, such as claims data and electronic medical records.

Biopharmaceuticals, tech companies, manufacturers, regulators, insurers and others all use Aetion and Evidation’s platforms for different parts of the research and approvals process. Aetion’s platform is a scientifically-validated software that analyzes real-world data (RWD), and rapidly produces transparent answers on the safety, effectiveness, and value of products across their lifecycle.  Evidation’s platform, meanwhile, expands the pool of potential data by connecting healthcare industry players with demographically-targeted participants who consent to sharing their personal health-related data for RWE studies across therapeutic areas. 

“Rapid advances in data analysis from big data, to machine learning and AI, are enabling this industry to improve the ability of companies to find novel medicine candidates, test them in software, and develop clinical data with new techniques for their commercialization,” said B Capital Group general partner Robert Mittendorff MD.

Mittendorff interviewed Evidation co-CEO Deb Kilpatrick and Aetion CEO Carolyn Magill during the B Capital Group’s 2021 Annual General Meeting in September, about their thoughts on the potential for real world data and evidence (RWD and RWE) to bring new efficiency to drug approval processes and therapeutics lifecycle management, while also enabling more patients to receive care that meets their unique needs. 

Where real world data comes from

Evidation divides real world data into two buckets. The first is “system-generated data,” said Kilpatrick.  That’s “the data that is generated largely (by) traditionally brick-and-mortar healthcare [and its stakeholders]. And then there’s the person generated data, which is largely generated outside of traditional brick and mortar healthcare” in the daily life context of individuals.

System-generated data includes things like billing and claims data, electronic health record data from doctors’ offices, lab data, and diagnostic data. Basically it includes formal records of a patient’s care within traditional settings. Person-generated data, on the other hand, can include any number of things. It can include data from someone’s smart watch or phone, as well as at-home diagnostic testing results. But it can also include tools with clinical-grade APIs, such as a continuous glucose monitor used by a type 1 diabetic.

What’s helpful about both categories of data is that one can ask the person who provides the data to grant permission for its use in research or other important use cases. Their information can then be used to gain broader insights for patient care, or to improve that person’s care directly by presenting a more complete understanding of their habits, situation, and health issues. 

“Over time, this new form of real world data is being used to generate new forms of evidence about what therapies are working, who they’re working in, and how they’re working in the context of the real world, largely for the pharmaceutical sector,” Kilpatrick said. 

Who uses real world data, and how 

Pharmaceutical companies, regulators, academic researchers, tech companies, government agencies, students, physicians, hospitals, health insurers, and others may all be able to use real world data for various purposes. 

“Ultimately, we want to use these types of data to inform decisions and health care and improve patient outcomes,” said Aetion CEO Magill. 

By analyzing the data with scientific rigor, we can help insurers learn when covering a certain drug that seems expensive will both improve patient outcomes and save the insurer money for the total cost of care. A doctor with access to technology that analyzes wearable data might learn a patient possibly has a heart condition that the patient didn’t know about or understand. Drug developers can reduce the time of a trial by working with better targeted and more relevant patient populations. The FDA might be able to analyze real-world data  as part of its process for approving label expansions, helping reduce the time to bring these treatments to more patients. 

Magill said the value of data from outside clinical trials is that it can be reflective of the messiness of people’s everyday lives. Sometimes people forget to take their medications, or choose not to take them because of side effects, for example. It also can help us understand the treatment risks and benefits for populations that are not always studied in clinical trial research. And this data from daily life is plentiful. A single wearable device, by way of example, can produce hundreds of thousands of measurements in a day. 

For regulators and researchers to trust insights from such data, they need to be replicable. Researchers at Harvard have used Aetion’s platform to replicate 31 published studies. The FDA has also tested the platform and found it to be highly accurate. 

 

How real world data can change health care

Analyzing real-world data can lead to faster, more cost-efficient approval of drugs and medical devices and better documentation of drug side-effects, both short and long-term. It can help with refining the design of clinical trials and identify more issues and trends worth deeper study. Platforms to collect, analyze, and interpret these important new data types are creating novel insights and  information for healthcare sector stakeholders that were unimaginable even a decade ago.

“When we think about the ways in which this has an impact on development, it really spans the drug development lifecycle,” said Magill. She added, “Most notably, today, I’d say clinical trial optimization — reducing trial costs by doing a better job targeting who participates in those trials, increasing the speed to market of drugs, (and) better identifying which populations are likely to respond positively to a given therapy.