In the pursuit of AI-powered drug development, the foundational importance of a robust data infrastructure often gets overlooked. As a biopharmaceutical executive noted, "everyone wants to hang the curtains in the AI penthouse, but forgot they have to pour the foundation and install the plumbing first." This sentiment highlights a critical challenge in Chemistry, Manufacturing, and Controls (CMC), where an antiquated data infrastructure is impeding progress.
CMC, the vital link between lab discoveries and life-saving treatments, is severely impacted by a pervasive scientific data crisis. CMC scientists spend up to 80% of their time on data management rather than scientific advancement. Experts from leading companies report scientists wasting 25-100 hours weekly on manual data transcription between systems. This inefficiency stems from data trapped in proprietary formats and reliance on document-centric workflows (e.g., PDFs instead of structured databases). This hinders critical connections between early predictive data and actual results, leading to undetected manufacturing deviations and costly batch failures.
The manufacturing environment presents unique data management complexities:
- Diverse Instrumentation: Hundreds of instrument types from various vendors generate proprietary data.
- Rigorous Regulatory Requirements: Manufacturing data demands meticulous traceability and provenance.
- Document-Centric Legacy: Many CMC processes remain rooted in manual, document-based approaches.
The pressure is mounting, with regulatory agencies moving towards mandatory electronic CMC submissions by 2026. Without a fundamental shift in data infrastructure, companies face expensive, manual data aggregation for every submission, impacting portfolio acceleration, a top business challenge for 80% of CMC organizations.
However, a clear path forward exists. Forward-thinking companies are implementing purpose-built scientific data platforms that prioritize:
- Data Replatforming: Converting proprietary data to open, standardized cloud formats (e.g., JSON, Parquet) for easier analysis.
- Data Context and Lineage: Tracking relationships between samples, methods, instruments, and results for compliance and insights.
- Scientific Use Cases: Tailoring solutions to specific manufacturing workflows (e.g., bioprocess development, quality testing).
These solutions eliminate manual transcription, reduce time-to-insight, and increase throughput. Beyond efficiency, modern data infrastructure builds "scientific intelligence." Seamless data flow enables quality teams to predict stability, engineers to identify critical process parameters, and product lifecycle management teams to make informed decisions. This integrated approach fosters manufacturing excellence, ensuring products are made correctly every time.
The urgency for manufacturing leaders is not whether to modernize their scientific data infrastructure, but how swiftly they can do so. Laying this essential data foundation today is crucial for the future of pharmaceutical manufacturing.
Read my article, “Scientific Data Crisis Holding Back CMC,” on GEN.