The holy grail for pharma?

Real-world evidence – is it the sacred answer or a load of hype?

Real-world evidence (RWE) is a red-hot topic in the life sciences community. And for good reason – it’s changing the landscape of drug development, speed of delivery, improving efficacy and safety measures for patients and contributing to many regulatory decisions.

In fact, by the start of last year, the US Food and Drug Administration (FDA) had approved more than 20 drugs that included RWE as part of their regulatory submissions.

The first drug to be approved by the FDA with RWE included was Merck’s cancer drug Keytruda in 2017.

Pfizer also used RWE in a regulatory submission the following year when it evaluated the safety and effectiveness of Vizimpro in patients with non-small cell lung cancer, finding the drug to have a safety profile consistent with the clinical trial.

While there is most certainly burgeoning interest and adoption among biopharma organisations, we’re not yet seeing RWE used abundantly for an extensive set of use cases – things like trial feasibility, external control arms or even as a substitute for randomised controlled clinical trials. So, how do we get there?

There is incredible potential for RWE to play a much wider role in the advancement of drug development, delivering insights that ensure efficacy and safety, while increasing patient-centricity and trial feasibility.

One of the biggest challenges with RWE, however, is the cost of obtaining and deriving value from it. Collecting the data is meaningless if researchers are then not able to reliably and cost-effectively analyse, synthesise and explain the data.

To get there, artificial intelligence (AI) and cloud analytics are vital. These technologies can break down many of the blockers that make it difficult to turn promise into reality through advanced machine learning and data management techniques.

For real-world data to be used as comparator of randomised clinical trial data, it must be possible to compare these two sources of data with respect to the important endpoints and variables in scope for the treatment. And this requires a lot of statistical know-how and robust technology.

Privacy is another challenge with concerns potentially limiting the amount of RWE that can be collected, analysed, and used to make decisions. Analytics and AI platforms can help ensure patient privacy and data security by providing secure data sharing and analysis mechanisms.

Deloitte’s real world evidence benchmarking studies have highlighted the growing determination of industry executives to increase the scale and scope of RWE adoption.

And recent experience proves its potential. During the COVID-19 pandemic RWE helped biopharma companies bring new vaccines and therapies to market in a short time. Thanks to RWE, the scientific community was able to figure out which COVID-19 diagnostic tests were most effective.

It’s clear though that the use of RWE, while crucial for improving healthcare outcomes, informing healthcare policy and decision-making and advancing scientific knowledge, is neither the holy grail nor simply hype – it is an instrument to advance clinical research on the condition that you appreciate cloud analytics and solid research.

Fortunately, many now understand that investing in reliable cloud analytics and the ability to test additional statistical methods and remove inherent bias will be key to success. If these efforts are sustained, the potential for drug development is endless.

Mark Lambrecht is Director, Life Sciences Practice at SAS. Go to sas.com