Technological advances in an ever-changing healthcare environment, including the digitization of patient health records, have had a significant impact on the development of new therapies. Since first being used to support a drug’s value, real-world data (RWD) is now being pulled into the R&D process at earlier stages to impact the development of new medicines on a more fundamental level.
We are now at a point where a clear understanding of real-world patients must become central to every stage of life science research and drug development, including at the discovery and pre-clinical stages. However, to truly advance R&D, clinical phenotype data needs to go hand-in-hand with biological data.
If a patient’s disease progresses in a certain way or the patient doesn’t respond to treatment, it’s important for researchers to understand both the clinical as well as the biological underpinnings behind each scenario. Correlating clinical data observations to the underlying biology historically has been hard to achieve, but new platforms and tools make it possible.
Why RWD is Important
In recent years, RWD has been increasingly embraced by a variety of players in the healthcare industry, including regulators, payers, providers and patients. Although the randomized clinical trial (RCT) remains the gold standard for determining safety and efficacy of therapeutic candidates, the industry is increasingly recognizing the limitations of RCTs in providing insights into the long-term effects of therapy or the understanding of how a therapy may affect different sub-types of a patient population. These limitations largely are due to the relatively small size of an RCT compared to the number of patients who may use the therapy once approved.
RWD offers more extensive patient information that can provide insights into relevant health outcomes, unmet needs, patient clinical pathways and treatment efficacy. It also can add depth of understanding across the therapeutic lifecycle.
A Brief History
The trend toward using RWD first emerged in the last decade or so, when drug development companies and payers began acquiring claims data as a way to estimate marketing success and better define and support the case for reimbursement. In addition, the RWD obtained through payer claims, phase IV open-label studies, patient registries, pharmacoeconomic studies and other evidence-based studies, helped provide life science marketers with support for a drug’s value beyond pivotal clinical trials.
This trend was accelerated when the demand for health economics outcomes research and reliance on RWD increased further, driven by changes in the US healthcare system including the implementation of the Affordable Care Act. Looking beyond efficacy in a controlled trial setting, industry players sought to demonstrate the impact of a therapy and evaluated outcomes in a diverse range of patients in a real-world clinical setting.
Use of RWD in Clinical Development
Recognizing the value of RWD in helping determine the impact of a therapy, clinical researchers began incorporating RWD along with RWD-enriched human biospecimens into the clinical development process through the model known as a “hybrid” trial. In this scenario, gold standard RCT data collection is integrated with RWD obtained through clinical sources to enable a more robust understanding of drug performance.
This integration of RWD with RCT results can offer researchers valuable insights at critical points. For example, data from hybrid trials may inform decisions to refine trial parameters. The pharmaceutical industry organization, PhRMA, notes that less than 12 percent of drugs entering clinical trials result in an approved medicine, so the insights derived from hybrid trials could prove valuable when moving from phase 2 testing into the expensive and time-intensive phase 3 trial. The data could also be used to expand the data set available for assessment of trials results.
RWD: Powerful in Discovery
At the same time, according to the NIH, in some therapeutics areas up to 85 percent of potential drugs fail in early clinical trials. A Tufts research report estimates that discovery costs are 30.8 percent of the total R&D expenditure for compounds that make it to market and are growing at a faster pace than other areas of R&D. Therefore, the potential for RWD to improve the discovery process offers great hope. In the last three to five years, researchers have begun to look for ways to incorporate RWD into early research and discovery.
Historically this has not been possible due to the complexity and expense involved, but now new digital tools and platforms that leverage health data for research are becoming available. And while researchers have been employing animal models with good results, access to human biospecimens, such as, for example, blood, urine and tissues enriched with real-world clinical data has the potential to generate even more relevant insights.
Rich clinical data linked to the biological information inherent in diverse human biospecimens enable researchers to explore hypotheses and define specific patient sub-populations with a high probability of responding to new treatment modalities. For example, researchers may find that patients expressing a certain antibody may experience an adverse event with a therapy in development, or that patients with a specific phenotypical characteristic may respond better than average.
This is especially true if the patient and their disease progression can be tracked over time, providing access to many more real-world data points, helping to increase the probability of success for new molecules or other treatments in clinical trials and post-launch.
The ability to better access real-world patients that have specific clinical characteristics holds promise for creating more sophisticated study cohorts in advance of the expensive and time-consuming randomized clinical trial process. Multiple hypotheses can be pursued at low risk, and researchers can gain greater confidence in the significance of in vivo study results.
For researchers, RWD and RWD-enriched biospecimens hold promise for increased success in discovery, translational medicine and clinical studies through better defined patient cohorts, better trial protocol, ultimately improving study success.
For patients, the promise is better medicine focused on individual need.