“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard
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 of clinical data arises, 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.
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. This adds enhanced substantiation to the added “value” of the drug.
As the pharmaceutical industry faces rising research and development costs (R&D) costs per drug brought to the market, there exists a compelling need to optimize the 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. It might occur due to real-world risk exposures not accounted for in the target population and has the potential to have a significant impact on the accessibility to and acceptance of the drug by the end-user. While probable confounding factors and risk factors are identified from existing published research, it is sometimes difficult to estimate the impact of unobserved exposures on treatment impact. This evidence might be available in the vast amounts of data existing in RCTs and RWE.
If the data from RCTs and RWD could be aggregated in an analyzable format, it has the potential to be utilized for clinical trial planning, segmented patient targeting, predicting clinical outcomes, improving efficiencies in health care systems, and tracking safety outcomes with increased accuracy. The legacy and ongoing RCT data, can be transformed by mapping CDISC SDTM data sets into OMOP CDM for RWD.
Excelra’s Approach
Excelra understands the need of the scientific community to aggregate, extract-transform-load, standardize, visualize, and analyse this data. As key stakeholders in the research community move towards “findability, accessibility, interoperability, and reusability” (FAIR) data standards for improving biopharma productivity, our data scientists can help create scalable clinical data repositories for interacting with data in a convenient and efficient manner on an automated platform. An acute understanding of data provenance and lineage is key to successful insight generation and our skilled team leaves no stone unturned while transforming big data into effortless data engines for bespoke client solutions.
Excelra’s “Molecule to Market” processes are Health Insurance Portability and Accountability Act (HIPAA), Europe General Data Protection Regulation (EU GDPR), as well as 21 Code of Federal Regulations (CFR) part 11 compliant to ensure implementation of best practices while transforming confidential data into meaningful insights for accelerating your drug discovery needs.
