Beyond the Lab: Real World Evidence in Clinical Trials
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 at 7.9%, but 40% of clinical trials never actually see the light of day due to an inability to meet patient recruitment numbers (1, 2). So how can we create more accurate clinical trials without recruiting huge patient populations? Here enters real-world evidence (RWE). Real-world evidence refers to data collected from various sources outside the realm of traditional clinical trials. Instead of controlled settings, RWE is derived from real-world scenarios, such as electronic health records, patient registries, and healthcare databases.
Currently, real-world evidence is 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) (3). But recently, the biopharmaceutical space has realized the potential that RWE could have during the clinical trial process, particularly in the design of randomized control trials. Due to the rigorous eligibility criteria, the patients in a clinical trial are not often wholly representative of a larger patient population. However, the use of RWE in clinical trial planning and design could help solve this issue (4).
So how can RWE be used pre-approval? This use case seems a little counterintuitive, as in the past it has been collected to see the effects of an on-the-market drug. RWE studies can be used in the research and development stages to contribute to the development 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 similar to the target population, researchers can identify promising candidates for further exploration (5).
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 (5). Beyond just market size, data on healthcare costs and mortality estimates can be used to further develop the business opportunity side of a pharmaceutical decision, developing market access, pricing, and health outcomes predictions.
While RWE presents exciting opportunities, it also comes with its set of challenges and limitations. The growing complexity and size of RWE datasets makes it hard to extract meaningful insights. This complexity is due in part to the often-disorganized manner in which RWE is collected and stored, and the variability in types of data—people often think of data as just being numbers that can be processed via advanced statistical analysis, but that is not the case with RWE. Take electronic health records (EHR), for example. Much of the information that can be extracted from EHR is clinician notes, which are in sentence format. The optimization of RWE data processing and insight extraction will rely on the development of more sophisticated data processing software, such as artificial intelligence-based natural-language processing software (6).
As we navigate the evolving landscape of clinical research, real-world evidence emerges as a powerful tool for enhancing the validity and generalizability of trial findings. While RWE cannot replace randomized controlled trials, by leveraging data from routine healthcare delivery, EHR, and patient registries, researchers can gain a more comprehensive understanding of treatment outcomes in diverse populations. This extends to informing trial and drug design, addressing challenges such as optimal drug characteristics, trial sample size, endpoints, and follow-up.
Factors Affecting Success of New Drug Clinical Trials https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173933/#:~:text=Clinical%20trials%20are%20an%20essential,high%20risk%20for%20biopharmaceutical%20companies
Patient Recruitment, Education and Retention in Global Clinical Trials https://lifesciences.welocalize.com/patient-recruitment-education-and-retention-in-global-clinical-trials/
Oxford Dictionary https://languages.oup.com/google-dictionary-en
Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.2480
Real-world data quality: What are the opportunities and challenges? https://www.mckinsey.com/industries/life-sciences/our-insights/real-world-data-quality-what-are-the-opportunities-and-challenges
Introduction to real-world evidence studies https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323556/
What is Real-World Evidence in Clinical Trials? https://vial.com/blog/articles/what-is-real-world-evidence-in-clinical-trials/
Real World Evidence Studies: Getting started https://www.iqvia.com/locations/united-states/blogs/2020/07/real-world-evidence-studies-getting-started