“Real world evidence is about generating insights that matter to patients sooner; embracing the possibilities offered by real world evidence for patient centred product development is an opportunity beyond obligation.” 

Álmath Spooner, Director Global Regulatory Policy and Intelligence at AbbVie

‘’Realizing the potential of RWE will require collaboration across all stakeholders to deliver improved health information infrastructure and a regulatory regime that facilitates iterative scientific dialogue on evidence generation plans for faster, better decision making.’’ 

Karin Van Baelen, Head of Global Regulatory Affairs at Janssen, Pharmaceutical Companies of Johnson and Johnson

Introduction

Clinical evidence generation for healthcare products has advanced to include a range of approaches. Although clinical trials (CTs) remain the research modality most relied upon by regulatory authorities for establishing clinical safety and efficacy, the lifecycle of evidence generation for medicines and vaccines increasingly include real world evidence (RWE) as a valuable component for assessment both pre- and post- regulatory approval[1]^,[2]. The key driver for the selection of a research methodology in healthcare product development is the underlying research question. Both clinical trials and RWE generation require robust research practices and their value in a particular case depends on their fitness to address the question posed.  

The COVID-19 pandemic has highlighted the need to embrace the totality of evidence to accelerate epidemiological understanding of disease and the value of existing and new preventive and therapeutic interventions. The effectiveness of several COVID-19 mRNA vaccines was estimated at the population level in “real-world” settings by analyzing Electronic Health Records (EHR) and vaccination data. Importantly, this example illustrates a scenario where RWE can provide answers to scientific questions that might not otherwise be available from CTs. Real-world data (RWD) are data regarding the effects of disease (patient characteristics, clinical and economic outcomes; health related quality of life) and health interventions (e.g. safety, effectiveness, resource use) been collected through routine clinical practice. The evidence derived from the analysis (and/or synthesis) of RWD, known as RWE, provides insights that inform all aspects of drug development from discovery to outcomes research. Feedback loops from clinical care to research and development allow for better, faster decision making through product development to delivery of healthcare.  The main advantage over data and evidence from CTs is that RWD and the resulting RWE reflect real clinical practice. RWD can enable a better view of the actual effectiveness and characteristics of a medical product or technology, and create  new insights to improve  health outcomes for patients and efficiency of healthcare systems.   

RWE can enhance healthcare decision making, by demonstrating outcomes and value for patients, healthcare professionals, regulatory bodies, Health Technology Assessment bodies and payers. Consequently, RWE can play a key role in their decision-making process and, as such, has received increased attention over recent years by the healthcare community and policymakers. Changing expectations for the use of RWE in regulatory decision making has particularly catalyzed scientific dialogue  on how to increase alignment on the fitness for purpose of RWE in a particular research scenario. As users and generators of RWE, the research-based pharmaceutical industry has called for clear principles from regulatory authorities for data quality and interoperability, governance frameworks that facilitate access to data, and pathways for iterative scientific dialogue to align on the relevance of data sources and the fitness for purpose of methodologies and analytical approaches.

Use of RWD offers unique potential to create a learning healthcare environment where individual patient care can be personalised, where transitions between research and clinical practice are more seamless and where understanding of patient experience is more holistic. To build multistakeholder confidence in RWE, the value proposition for all partners in the healthcare ecosystem must drive collective efforts to collaborate as part of a patient centered model for clinical evidence generation.

Current barriers

While healthcare product developers and policy makers recognize the potential value of RWD/RWE, there is also acknowledgement that there are challenges that need to be addressed to realise this potential. The variety in research objectives, study designs, data and methods presents a challenge for standardization. Regulators and other decision-makers across the globe evaluating RWE hold different expectations regarding their use and fitness for purpose. This diversity within and across regions, including across and within stakeholder groups, persists, although efforts are underway to improve convergence where possible[3]. A reliable data ecosystem is essential to accomplish a basic level of quality, relevance and interoperability amongst health data with the ability to link different sources. In this context, a European Health Data Space (EHDS) is one of the main priorities of the European Commission[4].

Generating insights from RWD requires access to the data in manner that is sustainable, predictable and which robustly protects data privacy. The EHDS will have a robust system of data governance and exchanging procedures, to safeguard the quality of data, and to create strong infrastructures that guarantee complete interoperability. Besides supporting healthcare delivery, the EHDS will provide a more robust framework for using RWD for secondary research and to inform  health policy making. Furthermore, the scheduled launch of DARWIN EU® in 2024, which is EMA’s own combined network of RWD sets, will be added to the EHDS and utilized to advance regulatory decision making by performing RWD-related research. This will provide multiple stakeholders a unique opportunity to align on the composition of fit-for-purpose evidence in a specific context[5].

Alongside DARWIN EU, we already see the benefits of public-private partnerships to improve healthcare data infrastructure in Europe. The IMI European Health Data and Evidence Network (EHDEN) project was set up to build an open-science network for large-scale data sources, in order to allow the use of RWD to generate RWE that is applicable to a broad range of use cases[6].

Robust study design and data analysis are critical requirements for multistakeholder confidence in RWE.  A shared confidence in methodologies requires platforms for discussion[7]. Additionally, a framework for iterative scientific dialogue will enable greater alignment on evidence generation plans.

Conclusion

RWD/RWE has the ability to characterize patients’ unmet needs, expedite understanding of disease and inform decision making through the product lifecycle from discovery through to access. Realizing the potential of RWE will require collaboration across all stakeholders to  deliver improved health information infrastructure and a regulatory regime that facilitates iterative scientific dialogue on evidence generation plans for faster, better decision making.

[1] Flynn R. Et al. Marketing Authorization Applications Made to the European Medicines Agency in 2018– 2019: What was the Contribution of Real- World Evidence? Clin Pharmacol Ther. 2022;111(1):90-7. 

[2] https://ascpt.onlinelibrary.wiley.com/doi/full/10.1002/cpt.2462

[3] https://globalforum.diaglobal.org/issue/november-2022/right-time-for-roadmap-to-harmonizing-rwe/

[4] https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en

[5] https://globalforum.diaglobal.org/issue/november-2022/right-time-for-roadmap-to-harmonizing-rwe/

[6] https://globalforum.diaglobal.org/issue/november-2022/right-time-for-roadmap-to-harmonizing-rwe/

[7] https://www.imi.europa.eu/projects-results/project-factsheets/getreal-initiative