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Leveraging Real World Evidence for drug approval

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In 2016 the FDA introduced the 21st Century Cures Act, which allowed for the expansion of real world evidence in gaining approvals for new drugs. Following this Pfizer's Ibrance, a CDK 4/6 inhibitor, was recently approved for the treatment of breast cancer in men by leveraging real world evidence.

Whilst there are clearly exciting opportunities around the use of RWE, there is still skepticism from some around the data versus randomized clinical trials and a number of challenges still need to be overcome. Dan Danielson is Senior Director of the Access Experience Team at Precision for Value and was a leader in real world evidence and health economics research during his time at Premera Blue Cross Blue Shield. We spoke to him about the benefits and opportunities as well as the challenges that RWE presents drug manufacturers.

How was real-world evidence leveraged to gain approval for Ibrance® (palbociclib)?

The FDA Drug Approval Process was modified by the 21st Century Cures Act, which allows expansion of indications for marketed drugs through the use of real-world evidence (RWE). The evidence on the use of Ibrance was uncovered by examining the data residing in clinical systems, claims systems, and drug safety databases.

Specifically, the approval of Ibrance for hormone receptor–positive (HR+)/ human epidermal growth factor receptor 2–negative (HER2-) advanced or metastatic breast cancer was based on the analysis of data contained in and cross-referenced across electronic medical records, insurance databases (IQVIA), the Flatiron Health Breast Cancer database, and Pfizer’s global safety database.

Although the exact protocols used in the study submitted to the FDA have not yet been presented, I suspect that the data and outcomes recorded in the databases mentioned earlier regarding the off-label use of Ibrance for advanced or metastatic HR+/HER2- breast cancer in men was compared with either clinical trial data generated by trials conducted in women, or a similar database analysis conducted in women with the same type and stage of breast cancer. Based on the information released, we know from the Ibrance RWE study that it is at least as safe to use in men as it is to use in women.

What did RWE offer that couldn’t have been achieved through traditional trials?

RWE studies are somewhat less expensive to conduct than traditional trials. They have the advantage of being somewhat faster to conduct and draw conclusions from.

To be fair, the evidence generated from controlled clinical trials and RWE is somewhat different. Controlled trials are aimed at demonstrating cause and effect (I give drug A and it causes a 30% reduction in symptoms of condition B). Observational trials, such as RWE, demonstrate correlations between drug A and what happens to condition B’s symptoms (I give drug A and we see reduction in symptoms of condition B).

What are the benefits of RWE? Where are the biggest opportunities?

The benefits of using RWE are that it supplements the information generated by traditional trials. RWE allow us to examine data generated by prescribers and their patients using medications to treat conditions in real life (IRL), as opposed to a controlled trial. RWE allows us to:

  • Validate a drug’s safety in patient populations that may not have been studied.
  • Confirm that similar clinical results may be expected from the a drug in those unstudied populations (men with breast cancer, for example), or in conditions that may be biologically similar.

I see the real benefits of RWE coming in the near- to mid-term future. As machine learning (AI) becomes more sophisticated, analysis of ‘real language’ data in electronic medical records improves, and laboratory data (such as next-generation sequencing of tumors) gets better, we may be able to uncover new uses for drugs sooner than we might normally expect. Indeed, at a conference I attended a couple of years ago, a clinical oncologist asked a rather provocative question: “will RWE at some time in the future be the primary route for drugs to gain new approved uses?”.

In your experience how does RWE impact trial outcomes–particularly around patient engagement, timelines, and costs?

For patient engagement, there are upsides and downsides. There are good RWE studies and less good ones. I think as patients learn about their conditions, they need to continue to be careful about what they read.

In terms of timelines and costs, I think it’s too soon to tell. Partly because those who create medical coverage policies may have a learning curve in how to apply RWE to their populations.

What are the biggest challenges that still need to be overcome in implementing RWE?

The lack of standardization in electronic medical records is probably the biggest challenge. AI’s ability to analyze real language used in these records will certainly help speed the process.

How can manufacturers, and regulators, be sure of the quality of RWE data?

Make sure that the data fields necessary for the post-marketing studies are planned out in advance, as much as possible. Importantly, make sure you use the right people and expertise to assemble the disparate data sets into a cohesive whole. As with other data analytics efforts, with RWE, “garbage in, garbage out” (GIGO) is the rule.

RELATED ARTICLE: How Virtual Trials and Real World Evidence are changing the CRO landscape

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