COTA: COVID-19 increasing use of real-world data
Outsourcing-Pharma (OSP) spoke with Viraj Narayanan, COTA’s vice president of life sciences, about how attitudes about RWD are changing and the effect the pandemic is having on its acceptance.
OSP: Could you explain what we mean by “real-world data”?
VN: The US Food and Drug Administration (FDA) defines RWD as data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. This includes EHRs, claims and billing information, disease registries, and personal health information captured by wearables or other mobile devices.
OSP: What are differences and similarities between RWD and real-world evidence, and how they might be used by clinical trials?
VN: In the simplest terms, real-world evidence (RWE) is generated by the analysis of RWD. We’ve seen the most RWE adoption and interest from sponsors around virtual or external control arms, which are most typically under consideration for Phase II clinical trials with high unmet need in a rare population, providing the program with the potential for an accelerated approval path; in such circumstances, the sponsor typically engages with the FDA to build a scientific and statistical case that a RWE-powered control arm is an appropriate fit-for-purpose use.
We've also seen significant interest in looking at off-label use in RWD to explore label expansion, as well as, in the post-launch setting, using RWD to inform label updates with a more representative and generalizable population.
OSP: How have industry attitudes toward use of RWD in drug development evolved in recent years?
VN: When it comes to cultural appetite, there’s certainly an adoption curve, and some organizations are more innovative than others. Today, we see a lot of disparity in organizations that are ready to adopt real-world evidence in the clinical development setting.
The main challenge they face is around positioning RWE as a different way of meeting a goal to help augment and accelerate insights, rather than as a threat or risk to the status quo. On top of that, there’s the associated challenge of developing the set of capabilities needed for RWE—a data science capability, an epidemiology capability, and a medical capability, for example.
Most recently, because of COVID-19, we’ve experienced increased interest in RWD from sponsors. As we slow down trials, we could speed things up with RWD—it’s digital and doesn’t require someone going to an office or a trial site.
How do you use that digital patient experience to ensure that you've got the right patient population, study design, and external comparator? It could be a pivotal moment for sponsors to leverage RWE to speed up development timelines when the system returns to normal.
OSP: Similarly, how have regulations and understanding by regulators changed about use of RWD in recent years?
VN: The staggering cost and timeline of drug development prompted the FDA to act, and in December 2016, the FDA signed into law the 21st Century Cures Act. As part of this Act, the FDA worked with stakeholders across the healthcare spectrum to create a framework to incorporate RWE to modernize clinical trial designs, with the intention to help accelerate medical product development and bring new therapies to patients faster, more efficiently, and—arguably most important to the biopharma industry—at a far lower price tag.
Without question, the FDA recognizes the value of RWD, particularly where there are significant challenges to executing a randomized clinical trial (RCT). They also understand the need for standardization related to data collection, curation, and analysis which they are addressing through various cross-industry collaborations like the pilot projects with Friends of Cancer Research.
OSP: Could you share some of the ways in which RWD can be successfully harnessed in drug development? If you can provide real-world examples, that would be great.
VN:
- Increase patient diversity in clinical trials. Clinical trial participants tend to be disproportionately more wealthy, white, and male than patients in the real world. Additionally, clinical trials often exclude patients who are older or have multiple illnesses. Consider that 59% of cancer patients in America are 65 years and older but from 2007 to 2010 only 33% of drug approvals had patients over this age. RWD can be used to supplement clinical trials to provide a more accurate view of patient populations that includes greater racial, socioeconomic, age, and gender diversity.
- Expand indications of already approved drugs. In 2019, in a landmark approval, the FDA approved the breast cancer drug Ibrance for use in male breast cancer patients based entirely on RWE. RWD from multiple sources was pooled to cite the use of Ibrance in clinical settings for this specific patient population, ultimately confirming that the risks and benefits of treating men were similar to those of treating women. In this example, RWE was able to replace the RCT to offer a new drug to an underserved population of patients.
- Replace the traditional standard-of-care or placebo group with an external comparator arm. External comparator arms offer an appealing alternative that relies on RWD collected from various sources. Using this data, external comparator arms can, in some cases, replace the placebo or standard-of-care group, thus eliminating any ethical concerns while also accelerating the time it takes to get a drug to market and reducing the overall cost of drug development.
- Help pharmaceutical companies make informed go/no-go decisions. There are a number of regulatory examples in which a drug that appeared promising after a phase 2 study ultimately failed upon phase 3 assessment of efficacy. The use of external controls derived from RWD has the potential to help pharmaceutical companies make better-informed go/no-go decisions as they consider whether to take a drug into a phase 3 trial. We recently heard from a pharma executive that every day in a phase 1 program represents $100,000 in cost.
OSP: Are there any caveats trial teams or pharma development pros should keep in mind when using RWD in their work?
VN: The key issues sponsors should keep in mind when introducing RWD to their work include addressing challenges with the data itself and the process of establishing an overarching strategy for using RWD and RWE.
Within the data component, we see challenges with sample size (“we don’t have enough data to match the control arm”), data harmonization (“how do we know that we’re measuring endpoints in the same way across data sets?”), and assessing data fitness for purpose (“is this the right fit-for-purpose data we need to answer our question?”).
In clinical development, companies like COTA are uniquely equipped to provide fit-for-purpose data because of their deep clinical data—claims data is less useful in this case.
From a strategy perspective, there are important questions to answer: When do you use RWE, and at what phase of development? Do you have advocacy at the right levels of the organization? There has to be an overarching strategy and someone responsible for it.
Our most successful partnerships with life sciences companies are open and collaborative. We typically have senior sponsorship at a very high level in the organization, and we learn together and transparently. These challenges are difficult to solve, and neither of us will be able to solve them alone, so we must share back and forth. That's the model we try to build towards in our biopharma partnerships.
Narayanan will be moderating the session Faster, Better, Cheaper: The Changing Role of Real World Data in Drug Development, Thursday June 18, 5 pm. Panelists include representatives from Janssen Research and Development, Bristol-Myers Squibb and Abbvie.
According to Narayanan, participants should:
- Learn how drug development has evolved over the past 20 years
- Understand the role that RWD plays today in drug development
- Hear How RWD will change clinical trials of the future
- Learn about methodology and competences to build an RWE-powered external comparator arm
