Real World Evidence: From Market Access to Drug Approval?

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Ulrich Neumann of Certara outlines recent developments in the use of real-world evidence (RWE) and the sizeable impact it stands to make on the global biopharma industry.

 

While the collection of real-world data is not new, real-world evidence is poised to have a profound impact on the biopharma industry

The end of 2018 ushered in a flurry of new regulatory guidance and sponsor enthusiasm on real-world evidence (RWE) and its adoption in the drug development process. While the collection of real-world data (RWD) is not new, RWE is poised to have a profound impact on the biopharma industry. Today, it is already common practice for regulators to use RWE to monitor post-market safety and to make regulatory decisions. Increasingly, sponsors have been leveraging RWE to support clinical trial design while healthcare systems are mandating the collection of RWE to substantiate coverage decisions.

 

FDA reveals its RWE Framework

While randomized clinical trial data remains the gold standard evaluating treatment efficacy, there is increasing interest and potential for leveraging RWD to inform healthcare decision-making. Both the 21st Century Cures Act and the PDUFA VI required the FDA to create a framework for addressing how RWE can be used to better support regulatory decisions. That framework, published at the end of 2018, offers some key definitions:

  • Real-World Data (RWD) is data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.
  • Real-World Evidence (RWE) is the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.
  • Examples of RWD include data derived from electronic health records (EHRs); medical claims and billing data; data from product and disease registries; patient-generated data, including from in-home-use settings; and data gathered from other sources that can inform on health status, such as mobile devices. RWD sources (e.g., registries, collections of EHRs, administrative and medical claims databases) can be used for data collection and, in certain cases, to develop analysis infrastructure to support many types of study designs to develop RWE, including, but not limited to, randomized trials (e.g., large simple trials, pragmatic clinical trials) and observational studies (prospective or retrospective).

According to Janet Woodcock, MD and Director of FDA CDER, “FDA will work with its stakeholders to understand how RWE can best be used to increase the efficiency of clinical research and answer questions that may not have been answered in the trials that led to the drug approval, for example how a drug works in populations that weren’t studied prior to approval.”

Specifically, FDA’s RWE Program will evaluate the potential use of RWE to support changes to labeling about drug product effectiveness, including adding or modifying an indication, such as a change in dose, dose regimen, or route of administration; adding a new population; or adding comparative effectiveness or safety information. The framework will include these considerations:

  1. Whether the RWD are fit for use
  2. Whether the RWE study design can provide adequate scientific evidence to help answer the regulatory question
  3. Whether the study conduct meets FDA regulatory requirements (e.g., for study monitoring and data collection)

Pilot projects are already underway, and the agency is seeking additional sponsors for novel partnerships. This provides a new and exciting opportunity for Pharma and its partners to explore new and innovative ways to use RWE to fast-track products to market; cutting the cost of large phase III trials and massively reducing the waiting time for patients to receive life-changing new therapies.

 

EMA’s “Regulatory Science to 2025” Rallies behind RWE

The EMA just published its ‘Strategic Reflection: Regulatory Science to 2025’ document. Aligned with the FDA and other global regulators, the EMA views RWE alongside cell-based therapies, genomics-based diagnostics, drug-device combinations, novel clinical trial design, predictive toxicology, modelling & simulation, ‘big data,’ and artificial intelligence as transformative research endeavours.

To that end, EMA is seeking to:

  • create a sustainable, quality-assured, flexible framework delivering rapid access to and analysis of representative, longitudinal RWD throughout a product’s lifecycle;
  • develop a capacity that will enable EMA to rapidly and securely access and analyze large amounts of healthcare data;
  • accelerate the implementation of a learning regulatory system based on health economics and outcomes research (HEOR) and other clinical care data;

The agency recognizes the benefit of using RWD to generate complementary evidence across the product life cycle and is committed to promoting the use of high-quality RWD in decision-making. EMA is further offering consultations in parallel with the European network of health technology assessment bodies (EUnetHTA), while countries are opening for HTA/regulatory early dialogues via processes called early/joint scientific advice.

National health systems have long been interested in RWE partnerships. A recent engagement with the French National Authority for Health (Haute Autorité de Santé; HAS) presents a powerful example. The RWE study involved 600+ patients over 6 centres for a conditional reimbursement scheme in chronic obstructive pulmonary disease (COPD). Over an 18 month timeframe, the therapy was shown to significantly reduce the number of hospitalizations and, therefore, remained fully reimbursed.

The European Commission has addressed these opportunities within the Innovative Medicines Initiative (IMI), the largest public-private partnership to improve the drug development process. It initiated the “GetReal” program, a consortium of pharmaceutical companies, academia, HTA agencies and regulators (e.g., NICE, HAS, EMA and ZIN), tasked with furthering the adoption of tools, methodologies, and best practices for increasing the quality of RWE generation in medicines development and regulatory/HTA processes across the continent.

 

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Patient-Reported Outcomes (PROs) as a Benchmark of RWD Excellence

Patient data systematically recorded from routine clinical settings (such as PROs) are known to be one of the key enablers of regulatory acceptance of real-world evidence. The strong benefits of PROs to the product development strategy rest on high-quality scales that can address the target audience’s constructs of interest. Researchers need to find the most appropriate tools for the context: health-related quality of life (HRQoL), satisfaction with treatment, adherence, or symptom measures. In many cases, cultural adaptations of PROs across different countries are necessary, paired with validation studies to assess their psychometric properties (reliability, validity, and sensitivity to change).

However, even the strongest proponents of drawing on RWD will have to acknowledge that the ‘real world’ tends to be messy and the data are often as fragmented, unstructured, and multifaceted as the settings they emerge from. It may well still take years to address changes around interoperability and quality for critical regulatory decision making to fully rely on these datasets. But as the FDA is integrating digital, RWD-based technologies into classic RCTs and drawing on machine learning and AI use in RWE to review its labelling changes, manufacturers are well-advised to pay attention. Preemptively addressing the so-called efficacy-to-evidence gap has practically become a requirement for market access decisions in line with developing a strong value proposition of novel technologies. It looks like regulators around the world are also paying close attention to what happens ‘in the real world.’

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