Integrating real world data into clinical trials is an emerging frontier, offering a transformative approach to clinical trial designs, recruitment, and analysis. CuriMeta, a pioneering entity in this realm, is uniquely positioned to guide the industry through this paradigm shift.
I had the privilege of discussing CuriMeta’s strategy, distinctiveness, and future outlook with Craig White, Chief Data Officer at CuriMeta. Our conversation shed light on the company’s innovative approach, distinct edge, and vision for the future of clinical trials in the age of real-world health data integration. Here’s what he had to say.
Moe Alsumidaie: Can you tell me about CuriMeta and elaborate on the mission to propel promising science forward?
Craig White: CuriMeta was founded in 2022 through the BioGenerator incubator in Saint Louis in collaboration with Washington University of Saint Louis School of Medicine and BJC HealthCare. As academic medical centers, these institutions provide a rich dataset that includes a vast range of medical records, pathology data, radiology data, and more. Our primary mission is to make this data available for research purposes, and our dataset’s depth and breadth set us apart. Our approach offers a more comprehensive view than typical claims or RWD datasets, capturing the intricate nuances of patient care and medical records across all departments and specialties.
Moe Alsumidaie: How does CuriMeta envision the future of clinical trials and research with increasing integration of real-world health data?
Craig White: I believe using real-world data will increase significantly. Not too long ago, real-world data was barely a consideration in clinical trial design. However, its importance has grown exponentially in recent years since the 21st Century Cures Act paved the way for its use. I’m optimistic that most clinical trials will use retrospective data or prospective technology-driven data collection methods in some form, beginning in oncology and rare disease trials, but eventually more broadly.
This transition is already starting to happen with a clear shift from traditional paper records to automated electronic data capture systems. The use of electronic systems validated to 21-CFR-Part 11 standards to capture data is analogous to whether the data are collected prospectively or retrospectively. This electronic capture has its roots in studies like the 2015 Salford Lung study, which was conducted in real-world clinical practice to consider adherence, co-morbidities, polypharmacy, and real-world factors.
Moe Alsumidaie: How is real-world data applied to clinical trials?
Craig White: There are several ways these data can be leveraged in clinical trials:
- Identifying Areas of Unmet Need: In the early stages of development, the data can pinpoint areas lacking satisfactory treatment or coverage. For pharmaceutical or biotech sponsors looking to develop a drug, targeting areas with genuine needs is crucial, ensuring their product fills a significant gap in the market.
- Locating Specific Sites and Patients: The data aids in identifying specific locations or practices where potential trial participants reside. Traditionally, finding suitable sites for trial recruitment has been a challenge. With comprehensive real-world data, sponsors can locate and engage with patients fitting specific criteria, streamlining and accelerating recruitment.
- Supporting Trial Designs: Real-world data can be instrumental in designing clinical trials. By understanding patient profiles, treatment outcomes, and other factors, sponsors can create more relevant, efficient trials likely to yield actionable insights.
- To Achieve Greater Representation: Traditional trials conducted in academic medical centers tend to overrepresent affluent, ethnically-focused groups and do not typically reflect the underlying US population. RWD more accurately represents the underlying population, and the data exist in these systems in proportion to the rates in which different groups, based on ethnicity, age, gender, or any other characteristics, utilize healthcare. For example, if 80% of patients with a specific disease are Black males, that is what you will get from an RWD sample. If you were to try to recruit prospectively for that trial, the proportions would likely be significantly different.
CuriMeta is a relatively young company in the early stages of some groundbreaking projects. One such initiative involves using real-world data to assist physicians in real-time treatment decisions. Although not directly related to clinical trials, this project exemplifies the potential of integrating real-world data into medical practice. As for our involvement in clinical trials, given our company’s nascent stage, I would like to highlight the experiences our team brings to CuriMeta. Over the past several years, team members have contributed to external control arm studies, some leading to successful drug approvals in multiple cancer indications. While these projects predate CuriMeta, they offer a glimpse into our expertise. Our focus at CuriMeta is on projects that cannot be realized with traditional data sources. We are delving into trials that require intricate molecular data or rely on analyzing changes in radiological or pathology images over time, especially in contexts like tracking cancer progression.
Moe Alsumidaie: How does CuriMeta envision the future of clinical trials and research with the increasing integration of real-world health data?
Craig White: Envisioning the future, especially considering the trajectory over the past seven years, we’ve seen a transformative shift in using real-world data in clinical trials. Not long ago, real-world data didn’t factor into clinical trial designs. However, the scene has changed dramatically, with numerous companies integrating real-world data at various stages of clinical trials, from recruitment to analysis. I am optimistic that in the next decade or two, most clinical trials will use RWD to identify unmet need for discovery work, then leverage it for patient and physician recruitment for trials alongside retrospective or prospective technology-driven data collection methods.
Moe Alsumidaie: Using real-world data in clinical trials often emphasizes oncology and rare diseases. Do you see this trend continuing, or do you anticipate shifts in focus?
Craig White: Using real-world data has been prevalent in oncology and rare diseases primarily because accessing patients in these areas can be challenging. Typically, the comparator arm in these trials is the standard of care, making it straightforward to argue for using real-world data, especially when compared to placebo-controlled trials. There is also a high degree of competition for patients due to the volume of research in oncology.
Furthermore, a significant amount of effort is channeled into translational epidemiology. This occurs even before the design of studies, sometimes preceding phase one trials. With real-world data, researchers can conduct genome-wide association studies, analyze RNA expression data, or perform whole genome sequencing. These exploratory analyses not only guide the design of the trial but also the broader research direction. Essentially, real-world data, especially molecular and genomic data and images, helps researchers focus on the most promising areas and targets. The demand for whole genome sequencing and RNA expression data to support such studies has steadily increased and represents a significant trend in the space that is worth watching.
