Situation
A mid-size global pharma company operates with the business model of licensing in new molecules from biotech and academic world, developing in research organization from non-clinical up to clinical phase 2.
- Emerging data strategy: the company’s ambition is to develop a robust pipeline with multiple Phase 1 and 2 programs in parallel, which required an overall automation and digitalization program for the company and more specifically for managing clinical R&D processes.
- Managing a network of collaborators: Working with many external partners and CRO’s worldwide means that clinical data sets are generated by a variety of CRO’s using different standards, formats and definitions; even within the standard CDISC format
- Standardization: To be able to analyze and report on these combined datasets at program level, all data sets have to harmonized into one standard structure
The company recognizes an opportunity to gain more value from the large amount of existing data on compounds still in investigation, ultimately boosting the selling price of advanced clinical drug candidates to partners.
Challenge
- Inefficiency due to insurmountable manual workload: the company has spent a significant amount of money, time and effort to manually harmonize a fraction of data coming from multiple sources in various formats
- Clinical trial data is under-utilized: Since harmonized data availability is limited, decisions are not always data driven
- Inefficiency in the drug development: Unnecessarily long throughput timelines in clinical programs since decisions were not always data driven
- Lack of automated solution available from traditional vendors that could handle the harmonization effort
Action
- Approached BC Platforms with the opportunity to collaborate on a data harmonization framework, tailored and configurable towards organization needs
- Developed one source of truth: Solution features a company specific dictionary that maps all formats into ONE standard
- New formats or standards will be recognized and flagged for manual confirmation or new entry in dictionary: it is an agile and learning system
Impact
Cost and time effective:
- one time investment developing the solution
- less manual work shortening timelines from months to weeks to process the data
- on-board external partners (CRO’s)
Quality:
- avoiding manual handling reduces errors
- data integrity and data quality improved (ready for GCP validation)
Integrated with Azure data warehouse to store, process and report in one platform
- enabling MS365 and PowerBI reporting
