Randomized controlled trials (RCTs) are the gold standard of evidence for establishing the value of an intervention and obtaining regulatory approval. However, patient-level RCT data from legacy trials is not available for further hypothesis testing and analysis. As interest in retrospective analysis grows, leveraging legacy and current data in drug development has become critical for maximizing scientific value from historical trials. It is worth noting that most of this data is available in the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) format and permissions need to be sought for aggregation and transformation.
Leveraging legacy and current clinical trial data
In addition to RCT data, the United States Food and Drug Administration (US FDA) and European Medicines Agency (EMA) have created guidelines to accommodate real world evidence (RWE) generated from claims, registries, and electronic medical records (EMR) data sets for demonstrating the efficacy, safety, and effectiveness of drugs. This data, if analysed as per approved guidelines, is accepted for regulatory and reimbursement approvals. However, real world data (RWD) is coded in multiple formats and needs to be processed further. Various organizations have started transforming this data to Observational Medical Outcomes Partnership (OMOP) common data model (CDM) formats for RWE generation, strengthening the role of FAIR data principles in life sciences.
Real-World Evidence and Regulatory Acceptance
This adds enhanced substantiation to the added “value” of the drug and supports data-driven drug discovery approaches across the development lifecycle.
Accelerating drug development using integrated data
As the pharmaceutical industry faces rising research and development (R&D) costs per drug brought to market, there exists a compelling need to optimize existing data assets and shorten the drug development life-cycle. Heterogeneity of treatment effect (HTE) is another challenge for the industry. HTE is defined as the difference in patient outcomes measured from post-launch RWD as compared to results observed in pre-launch RCTs. This variability highlights the importance of integrating legacy and current data in drug development to better predict real-world outcomes.
Addressing Heterogeneity of Treatment Effect (HTE)
HTE might occur due to real-world risk exposures not accounted for in the target population and has the potential to significantly impact drug accessibility and acceptance. While probable confounding and risk factors are identified from existing published research, estimating the impact of unobserved exposures remains challenging. Valuable evidence may already exist within aggregated RCT and RWE datasets, especially when supported by analysis-ready clinical datasets.
Transforming RCT and RWD into actionable insights
If data from RCTs and RWD could be aggregated in an analyzable format, it could be utilized for clinical trial planning, segmented patient targeting, predicting clinical outcomes, improving healthcare system efficiencies, and tracking safety outcomes with greater accuracy. Legacy and ongoing RCT data can be transformed by mapping CDISC SDTM datasets into OMOP CDM, enabling scalable clinical data services and advanced analytics.
Excelra’s approach to legacy and current data integration
Excelra understands the need of the scientific community to aggregate, extract-transform-load, standardize, visualize, and analyse complex clinical data. As stakeholders move toward “findability, accessibility, interoperability, and reusability” (FAIR) standards, our experts deliver scientific informatics solutions that enable seamless interaction with data on automated platforms.
An acute understanding of data provenance and lineage is central to successful insight generation. Excelra’s data scientists apply advanced analytics, visualization, and data science services to transform large-scale clinical datasets into efficient, decision-ready data engines.
Excelra’s “Molecule to Market” processes are HIPAA, EU GDPR, and 21 CFR Part 11 compliant, ensuring best practices in transforming confidential data into meaningful insights that accelerate drug discovery and development.
