Real World Data key to drug discovery, reducing carbon footprint of clinical trials
"If employed effectively, the use of RWD in clinical studies has the potential to be potent in many aspects of the drug discovery and regulatory pipeline," writes Dov Greenbaum
While RCTs have had an excellent run as the preeminent method for conducting clinical trials, many have reported on the numerous downsides to the continued unwavering focus on their use. These include the tendency for researchers employing RCTs to exclude patients with comorbidities or other confounding factors, such as pregnancy, from drug trials. Moreover, endpoints in RCTs tend to focus on safety, efficiency and efficacy, but often fail to also consider various socials concerns. Further, results from RCTs are not always applicable in broader more real-world contexts, and RCTs in their drive to present the cleanest and unbiased data, also often oblige the clinical researcher to follow one-size-fits all treatment protocols that for each patient, are potentially inefficient and inexact.While studies that use RWD are a potentially powerful successor to RCTs, these types of studies have for the most part been technological wallflowers. The use of RWD in clinical studies has really only now come of age, finding its place in a COVID/post-COVID world were the use of RCTs have becoming increasingly complicated to administer and time consuming, especially when time is of the essence. Thus, while the US 21st Century Cures Act, which advocates for greater use of RWD in drug approval, was signed into law way back in 2016, its arguable that the rush to find vaccines and drugs during the COVID pandemic has finally provided the necessary traction to push RWD forward in the agenda. If employed effectively, the use of RWD in clinical studies has the potential to be potent in many aspects of the drug discovery and regulatory pipeline, including in pre-clinical drug development, through the clinical trial and authorization process, in the area of payor reimbursements, and finally in for post-approval follow-up. Notably, however, while RWD is now actively promoted by the U.S. FDA as an important tool in drug discovery, there are many potential pitfalls in applying RWD in clinical trials. Notably, the use of RWD in clinical studies still needs substantial oversight in its implementation. The most recent FDA guidance discusses some of the more practical ramifications of the use of RWD, such as the need for standardization. This is especially important such that data, regardless of where it is captured is usable within the data curation, data transformation, and other data processing parts of the drug application process. Further, there are numerous potential biases and concerns that can arise from the use of the often unstructured data that makes up RWD, including worries regarding data provenance and trackability, uncertainty regarding data accuracy and data validation –especially when the data is self-reported by the patient, and the inability to effectively extract relevant data that is complete.
Prof. Dov Greenbaum is the director of the Zvi Meitar Institute for Legal Implications of Emerging Technologies at the Harry Radzyner Law School, at Reichman University