Real World Data and Real World Evidence: Shaping the Future of Drug Development and Healthcare Decision-Making
The COVID-19 pandemic put a spotlight on the importance and the utility of real world data (RWD) and real world evidence (RWE). Indeed, RWD and RWE had a pivotal role during the pandemic, enabling biopharma companies to, first of all, better and faster understand the new disease, and then to bring novel vaccines and therapies to patients in record time (1). But RWD and RWE were already used by pharma companies for decades before the pandemic, to inform their decision making, respond to rising requests from external stakeholders and support their value claims. However, the promises of RWD and RWE are to bring much broader benefits to biopharma companies and ultimately to patients, potentially disrupting the biopharmaceutical industry in the near future.
Real World Data (RWD) and Real World Evidence (RWE) Defined?
RWD is healthcare data gathered from various data sources outside of randomized controlled clinical trials (RCTs).?Examples of RWD data sources are:
RWE is defined by the FDA as ‘’the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD’’ (2).
Applications and Benefits of RWE
Generally, RWE has been used to augment evidence from randomized controlled trials. Traditional applications of RWE are related to understanding disease progression and burden, understanding treatments effects in subpopulations, patient safety monitoring and clinical and cost-effectiveness assessment. However, there are several new and growing opportunity areas for RWE, considering that they can be applied and can benefit the entire product life cycle.
One of the most important new use cases refers to using RWE to support regulatory submissions, with 90% of new drug approvals in the US including? RWE as part of the submission at the end of 2020 (3). Indeed, RWE can be used to demonstrate product safety and effectiveness (e.g. supporting label revisions related to safety), provide therapeutic context (e.g., contextualize the natural history of a disease), and expand label indications. This use case has been driven and accompanied by a growing regulatory acceptance, with the FDA actively releasing guidelines and working on different projects over the past years, in order to explore the potential role of RWE in regulatory decision-making. However, much more needs to be done from regulatory institutions to provide a comprehensive guidance on RWD and RWE.???
The adoption of RWE continues to grow also in research and development (R&D). RWE is able to support the R&D process from the very early stage of decision-making on target product profiles and development strategy, up to? the clinical trial stage. RWE can contribute to the optimization of clinical trials in several ways. First, RWE can inform protocol design, potentially reducing the number of costly protocol amendments and reducing trial failure rates through improved protocol design and site selection strategy (4). RWE also enables clinical trials’ adaptive designs and the use of synthetic control arms to accelerate trial execution, accelerate? the delivery of drugs to the market and decrease the overall cost. Last but not least, another bigger gain of RWE in R&D comes from a reduction in the cost of post-marketing regulatory commitments. Thanks to the pandemic and the increased regulatory openness towards the use of RWE in R&D, it is very likely that there will be a further acceleration of RWD and RWE application in R&D.
At the end of the product life cycle, at the commercial stage, biopharma companies are using RWE to understand which factors impact healthcare professionals’ decision making in order to determine brand marketing effectiveness and how to better position their products (5).
Limitations and Challenges in Implementing RWE
The major limitation for RWE implementation is accessing high quality,reliable RWD (4) . Data access limitations, concerns around reliability and accuracy of RWD collection and the nature itself of RWD data, which is unstructured data, strongly limit the adoption and the utility of RWE. As unstructured data, quality is often poor, requiring cleaning, standardization and normalization from researchers, which in turns involves? significant labor and costs. There are no standards for RWD curation, hence cleaning methods used by researchers are different and not widely accepted for statistical validity (6).
A second challenge that comes limiting the potential of RWE is the RWD data sources being independent of one another, prohibiting an accurate, comprehensive overview of a specific patient, which is the starting point towards precision medicine and better quality of care.?The healthcare data environment is highly fragmented, with siloed information and lack of interoperability, and the same situation applies when it comes to RWE.
Last but not least, security issues and patient protection concerns can limit the adoption of RWD and RWE. Data breaches concerns can hinder investments in RWE from companies and regulatory institutions and/or patients to consent to the use of their data.
The role of technology and AI
Advanced analytics technologies and Artificial Intelligence (AI) are the solution, or at least part of the solution, to overcome these? challenges.
A central data management and analytics platform which is able to collect, harmonize and analyse rich RWD & RWE datasets, to represent a centralized knowledge repository and act as a collaboration platform, is a must-have to leverage RWE potential. Centralizing data coming from different sources, the platform not only removes traditional healthcare fragmentation and siloed information but at the same time fosters collaboration and drives efficiencies in evidence generation, allowing for a more comprehensive and robust long term view at patient level. AI and advanced RWE analytics (predictive models, machine learning, probabilistic causal models, and unsupervised algorithms) are then able to extract deeper insights from rich data sets, with important? use cases such as understanding patient behaviors, segmenting patients for trial matching, and enabling a data-driven understanding of disease progression.
McKinsey estimates that an average top-20 pharma company that adopted advanced RWE analytics across its whole value chain for in-market and pipeline products could unlock more than $300 million a year over the next three to five years (7). In addition to the cost savings, introducing advanced RWE analytics has the potential to enhance identification of new targets for molecules, accelerate time to market, improve formulary position and payer negotiations, and generate stronger evidence of differentiation and benefit/risk balance for in-market products. However, the use of AI for RWD and RWE analysis seems to be still at the early stage, with biopharma companies having yet to discover its full potential and value.?
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Patient demographics, clinical and treatment data, anatomical, biomarker-based measures, and descriptive and optional statistical analyses can be accessed to drive decisions based on hard evidence across strategies and programs for R&D and? commercial.
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Conclusion
There is still a long road ahead to fully realize RWD and RWE transformative potential in shaping the future of medicine development, commercialization, and reimbursement. To harness their full capabilities, regulatory challenges, data quality concerns, and standardization issues must be effectively addressed. At RetinAI, we are committed to doing our part in this endeavor, offering cutting-edge AI software solutions to partners and customers, ultimately aiming at elevating the quality of care for every patient. It is indeed only through a concerted effort from researchers, policymakers, and industry stakeholders, that RWD and RWE can pave the way for a more efficient, patient-centered approach to healthcare decision-making and treatment strategies.
References:
(1) ?Terri Park, “Behind COVID-19 vaccine development,” MIT News, May 18, 2021.
(2) FDA, Real-World Evidence. 2022. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence?
(3) Christina A. Purpura et al., “The role of real-world evidence in FDA-approved new drug and biologics license applications,” Clinical Pharmacology & Therapeutics 111, no. 1, 2022: pp. 135–44.?
(4) Deloitte Insights. RWE focus is shifting to R&D, early investments begin to pay off. 2020
(5) Deloitte Insights. Real-world evidence’s evolution into a true end-to-end capability. 2022
(6) Deloitte Insights. Getting real with real-world evidence. 2017
(7) McKinsey & Company. Creating value from next-generation real-world evidence. 2020