Jessica Johnson

The cost of bringing a medicinal product through all phases of drug development to the clinic is upwards of $1.5 to 2.5 billion, with clinical trials making up a significant proportion of related R&D spend.

 It’s a complicated—and expensive—endeavor that pharma companies face. Submitting regulatory dossiers to global regulatory authorities, based on the outcomes of these clinical trials, requires a combination of harmonized data assets connected with 

 (RWD)—a powerful tool that can help drive clinical trial management and data-driven clinical decision-making.

Access to globally sourced and previously untapped, line level RWD and real-world evidence (RWE) is of increasing value when making regulatory submissions, meeting cost and speed business challenges head on. This, coupled with the clinical evidence about the usage and potential benefits or risks of a medical product derived from the analysis of RWD, is fundamental to drug development, programmatic risk reduction, commercial expansion prioritization, health care provision, and patient outcomes. But using RWE in clinical submissions is not without challenges.

BC Platforms and SAS formed a partnership

 in 2022 to support the use of RWE in regulatory submissions for medicinal products—delivering powerful analytic solutions and access to diverse global patient data, combined with repeatable, modular, and easy-to-use management capabilities that provide swift and controlled data access. By tapping into the power of data and analytics, drug developers can avoid major pitfalls encountered when using RWE to make regulatory submissions for medicinal products and improve regulatory submission success.

1. Develop a robust prespecified protocol and statistical analysis plan

A common mistake when seeking to leverage RWE to support or demonstrate a product’s safety and efficacy is failing to share a prespecified protocol and statistical analysis plan or SAP with the specified regulatory agency. Taking a proactive approach and aligning with FDA’s RWE framework recommendations can help guard against this risk. This includes ensuring that multiple analyses in electronic data sets are carried out quickly and inexpensively, making it possible to conduct numerous retrospective studies until the desired result is obtained.

Recent guidelines also recognize the need for a prespecified protocol and SAP as a potential risk to undermining study validity and recommend sponsors and requesting applicants provide draft versions of their proposed protocol and SAP for regulatory agency review and comment, prior to finalizing these documents and before conducting the study analyzes.

The key objective is ensuring cross-sponsor stakeholder visibility—visibility through project-specific dashboards, repeatable and audited analytics, and a clear, iterative package that sponsors can bring forward for pre-submission alignment with regulatory bodies.

One of the most common causes for regulatory submission failure in RWE studies is ‘data missingness.’ This can be due to misalignment with ‘real world’ patient visit cadences and resulting data, when compared to the predetermined cadences of clinical trial visits and data collection. The industry is also being challenged to incorporate and represent more diverse patient data across many therapeutic areas, including cancer, cardiovascular, and rare diseases.

To facilitate more complete data, stakeholders need direct access to complete, line level longitudinal electronic health records (EHRs), lab values, imaging, genomic, other ‘omic,’ and sample data, without compromising on security or legal privacy requirements. Aggregate level or claims data only do not suffice in telling the full patient journey and lack insight to physician notes and practice behaviors or reported outcomes.

, can address completeness through the ability to access entire patient journey retrospectively and prospectively, including access to the healthcare provider sites, and to patient recontact to generate genomic data, recruit to clinical trials, or collect biospecimens. Baseline characteristics are made more easily verifiable, and comparable patient cohorts from an existing trial can be easily lifted from populations across wider regions of interest if additional data, including more diverse data, is required.

Completeness of EHR data with additional enrichments like genomics, lab values, and imaging—which reflect high accuracy in the patient journey—have significant value in regulatory submission success and achieving comparability with active trial cohorts. This is especially noteworthy within rare and ultra-rare disease populations. Having a global reach and catchment from which to source patient counts, confirm the regional incidence of disease, as well as to engage providers, sites and patients saves critical time, reduces patient burden, and can also enhance cohort sizes.

In cases where cohort sizes are inherently small, look to recent FDA guidance regarding data linkage and the combining of data as possible solutions. Data governance and quality control procedures not only ensure completeness, but data provenance as well. As these methods can also introduce new methodological challenges—notably around data heterogeneity—turning to analytics can help demonstrate data quality, statistical confidence, bias omission, and complete audits and audit trails.

4. Ensure aligned RWE across different regions

The fourth most common failure in RWE study acceptance is the challenge of variability in physician practice and different standards-of-care across the globe regarding EHR data outcomes—a variability that can result in unclear outcome measures. It’s imperative for pharma companies to engage sites from all regions of the world and verify standard-of-care practices in regions of interest.

In the ‘provider questionnaire phase,’ regional feedback can help confirm standard-of-care practices that ensure well-defined diagnostic criteria within treatment pathways specific to their populations. This phase delivers an essential risk mitigation strategy for trial site identification and when developing data sourcing plans for RWE projects.

When incorporating underrepresented global populations within drug development, it is also critical to meet diversity and inclusion guidance that can underpin new market entries. Pharma companies must address the data needs and methodological issues, while in parallel aligning solutions that meet global regulatory guidance enforcements.

The bottom line? It is possible to overcome major areas of failure when using RWE in regulatory submissions. But the linchpin to that success is utilizing a vast network of provider sites to assemble the data within a GDPR+ technology platform and the analytics to enable access to the high-quality genomics and real-world data networks. Using a network of readily available line level patient data in analysis ready format, predefined statistical tools, visualizations, and advanced analytics can unlock previously untapped RWD assets from across the globe to accelerate insight generation for R&D and commercialization efforts. And those tangible results are accelerating drug development, healthcare provision, and clinical decision-making, which, ultimately, will positively impact patients and their families around the world.

, VP, strategic partnerships, BC Platforms; and 

, director of health and life Sciences, SAS

Subbiah, V (2023). The next generation of evidence-based medicine. 

https://doi.org/10.1038/s41591-022-02160-z

US Food and Drug Administration (2018). Framework for FDA’s Real-World Evidence Program. 

https://www.fda.gov/media/120060/download

US Food and Drug Administration (2021). Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products, Draft Guidance for Industry. 

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory

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Utilizing a broad network of provider sites is key in overcoming failure and enabling access to high quality data.

Jessica Johnson

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