Clinical trials, a massive hurdle in the biopharmaceutical world, are often the rate-limiting step in drug development. Not only are clinical trial success rates extremely low, but 40% of clinical trials never actually come to fruition due to an inability to meet patient recruitment numbers (1, 2). So how can we create more accurate clinical trials without having to recruit large patient numbers from inaccessible populations? This report explores the potential of real-world data (RWD) and real-world evidence (RWE) to revolutionize clinical trial methodologies, offering a pathway to more accurate and efficient drug development processes.
Understanding RWD and RWE
RWD refers to data collected outside the controlled environment of traditional clinical trials, derived from sources such as electronic health records (EHR), patient registries, and healthcare databases. RWE refers to insights derived from analyzing RWD. Unlike clinical trials, RWD reflects real-world scenarios, making it more accessible and potentially more representative of broader patient populations.
Early Successes in Clinical Trials
In the past, RWE has been used in select applications, such as post-approval safety monitoring and pharmacovigilance (the practice of monitoring the effects of medical drugs after they have been licensed for use, especially to identify and evaluate previously unreported adverse reactions) . However recent examples demonstrate the expanding role that RWE could have in the clinical trial space.
For instance, in 2019, Pfizer received expanded approval for their breast cancer drug, Ibrance, by using RWD from EHR to create an analysis that supported the approval (3). Additionally, in 2020 tafasitamab plus lenalidomide was approved for relapsed/refractory diffuse large B-cell lymphoma (rrDLBCL) after its single arm trial (SAT) was further validated by using RWD/RWE for a matching, retrospective, observational cohort of patients with rrDLBCL. This supported the regulatory submissions and helped to disentangle the contribution of individual agents in the combination therapy (4).
Key Opportunities in Clinical Trial Planning and Design
RWE has the potential to be utilized earlier in the clinical trial process, particularly in the design of randomized control trials. Due to rigorous eligibility criteria, clinical trial patients are often not wholly representative of a larger patient population. However, the use of RWE in clinical trial planning and design could help solve this issue (5).
So how can RWE be used pre-approval? RWE studies can be used in the research and development stages to contribute to the identification of Target Product Profiles (the desired characteristics of a drug), aiding internal decisions throughout the product development journey. RWE can also be used to inform drug-drug interaction studies. By analyzing RWE on medication use among patients with characteristics and medication regimens similar to the target population, researchers can identify potential negative drug interactions (6).
The optimization of RWD processing and insight extraction will rely on the development of more sophisticated data processing software
RWE analysis is also valuable in enhancing understanding of targeted indications by refining estimates of disease prevalence and incidence. This analysis is particularly valuable for rare diseases where small changes in population size can have huge impacts on program viability, and insights into the changing size of a patient population can influence decisions on developing therapies in niche but expanding markets. In many cases, this could be the difference between an indication being treated or not— many therapies that are necessary may not be developed if they do not provide a profitable opportunity (6). Beyond just population size, data on healthcare costs and mortality estimates can be used to better elucidate the business opportunity side of a pharmaceutical decision, developing market access, pricing, and health outcomes predictions.
Challenges and Limitations of RWE
Despite the potential of RWE, RWD analysis comes with its set of challenges and limitations. The growing complexity and size of RWD makes it hard to extract meaningful insights. This complexity is due in part to the often-disorganized manner in which RWD is collected and stored, and the variability in types of data. Take EHR for example. Much of the valuable information in EHR is unstructured data like clinician notes, which are in sentence format. The optimization of RWD processing and insight extraction will rely on the development of more sophisticated data processing software, such as artificial intelligence-based natural-language processing software . While early adopters resorted to manual EHR processing, a new wave of artificial intelligence technology holds promise for the future of automated EHR processing. Dyania Health, a startup founded in 2019, has developed a natural language processing model that they claim can draw conclusions from EHR data with greater than 94 percent accuracy (7).
Conclusion
As we navigate an evolving landscape of clinical research, RWE has emerged as a powerful tool for enhancing the validity and generalizability of trial findings. Recent successes in clinical trials, such as the integration of RWE to identify optimal patient populations or to validate outcomes, underscore its potential to complement traditional trial methodologies. While RWE is unlikely to replace randomized controlled trials, it is growing rapidly as a mechanism to support and improve the clinical trial process. Leveraging insights from routine healthcare delivery, EHR data, and patient registries offers great promise for the entire lifecycle of clinical validation, from refined drug design to cohort and endpoint selection.
Authors: Piper Boss and Dane Callow
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Pfizer wins expanded Ibrance approval using real world data https://www.biopharmadive.com/news/pfizer-wins-expanded-ibrance-approval-using-real-world-data/552135/
Real-world data in drug development strategies for orphan drugs: Tafasitamab in B-cell lymphoma, a case study for an approval based on a single-arm combination trial https://www.sciencedirect.com/science/article/abs/pii/S1359644622000757
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