Real-world data (RWD), such as claims and billing information, have long been used to support drug development and access evaluation. Now, after more than a decade of increased adoption of electronic health records (EHR), EHR-derived RWD has become a valuable source of detailed, longitudinal, individualized patient clinical journeys.
In recent years, with linkages to genomics and the development of real-world clinical outcomes (such as mortality and progression), EHR-derived data has expanded how RWD can be used to inform scientific discovery, enhance clinical trial efficiency, and improve patient access to new medical products. Below you’ll find some examples where EHR-derived RWD has been applied to inform and enhance key decisions for drug research and development.
1. Generate hypotheses about biomarker resistance alterations and co-alterations
EHR-derived RWD, coupled with genomic information, is a powerful combination for generating hypotheses regarding new biomarker-mediated therapies. This can include assessing possible resistance biomarkers, as RWD that incorporates clinical outcomes can be used to identify potentially novel genetic alterations that arise when cancer has progressed after treatment with targeted therapies. In addition, co-alterations can be assessed to either reconfirm the driver mutation for a disease or assess additional predictive biomarkers when evaluating combination therapy strategies.
See examples:
- RWD used to examine biomarkers associated with response and resistance to checkpoint inhibitors
- RWD used to assess co-occurence of NTRK fusions with other genomic biomarkers in cancer patients
2. Characterize the natural history of a disease
RWD with clinical depth can significantly strengthen descriptive analyses by reflecting the natural history of a disease, especially when limited information is available in medical literature, registries or clinical trials. For example, using RWD that includes a patient’s clinical and genomic information as well as real-world clinical outcomes, it is now possible to understand how rare biomarker status correlates with clinical outcomes in a specific patient population. Biotechs interested in understanding the natural history of a specific disease can leverage contemporaneous EHR data to discern the disease’s possible trajectories.
See example:
- RWD used to assess the predictive value of a biomarker alteration
3. Assess the unmet need of a patient population
RWE can be used to assess the unmet need of a patient population. This is ideal for biotechs who are defining which patient sub-population(s) would benefit from a drug under investigation. For example, evaluation of unmet needs between patient populations with different genomic alterations and treated with standard of care therapies can be performed to inform prioritization for clinical development.
See examples:
- RWD used to assess real-world overall survival of two patient populations with different biomarker alterations in NSCLC and DLBCL
4. Select the appropriate comparator population for a clinical trial
RWD can be used to inform aspects of trial design for randomized controlled trials for biotech companies as part of developing the target product profile for a new molecule. For example, when developing comparator patient populations, RWD can be applied to understand how different inclusion / exclusion criteria might impact clinical outcomes in a trial. In addition, RWD can help estimate whether it will be feasible to recruit the patient population of interest, as well as perform time-to-outcome calculations to estimate the appropriate timing of interim analyses.
Closing thoughts
Real-world data, and especially those linked to genomics and real-world outcomes information, can help to enable better and more efficient decision-making across the drug development pipeline.
Reach out to us to learn more about how EHR-derived RWD can help to advance and accelerate your drug development program.
