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A biopharmaceutical industry executive recently shared a telling insight with one of my colleagues. He said all his peers want to “pick out the drapes in the penthouse of AI-powered drug development, but everyone forgot they have to pour the foundation and install the plumbing first.”
His sentiment perfectly captures the challenge facing biopharma today. As companies race to implement artificial intelligence across their operations, a significant obstacle threatens to derail these efforts: the scientific data crisis. Nowhere is this impediment more pressing than in Chemistry, Manufacturing, and Controls (CMC) writ large.
CMC, the processes ensuring therapeutics can be safely and consistently manufactured at scale, represent the bridge between promising lab discoveries and life-saving treatments for patients. Yet this essential function has become a key area of impact for biopharma’s data crisis, with wide-reaching implications for manufacturing efficiency, regulatory compliance, and ultimately patient access to treatments.
Hidden manufacturing bottleneck
While manufacturing innovation often focuses on equipment, facility design, and process optimization, the underlying data infrastructure connecting these elements remains surprisingly antiquated. CMC scientists designing purification methods, determining stability, and scaling up production are drowning in fragmented data across hundreds of proprietary systems.
Novartis CEO Vas Narasimhan candidly acknowledged this challenge when he noted in a podcast interview, “We’ve had to spend most of the time just cleaning the data sets before you can even run the algorithm. It’s taken us years.” Our recent research with CMC experts from leading companies, including Amgen, Bayer, GSK, Novartis, and Takeda, revealed that scientists waste 25-100 hours weekly on manual data transcription between systems. Today, skilled professionals spend up to 80% of their time wrestling with data instead of advancing science.
Why CMC data is uniquely challenging
The manufacturing environment presents unique data challenges that aren’t present in other areas of drug development. CMC data must bridge clinical development and commercial manufacturing, connect analytical and process data, and maintain robust lineage for regulatory compliance.
Three factors make CMC data particularly difficult to manage:
- Diverse instrumentation landscape: A typical manufacturing environment might utilize hundreds of different instrument types from various vendors, each producing data in proprietary formats.
- Regulatory documentation requirements: Manufacturing data must maintain traceability and provenance for regulatory filings and investigations.
- Document-centric workflows: Despite digitization efforts, most CMC processes remain document-centric rather than data-centric, with information trapped in PDFs and text files rather than structured databases.
The consequences extend beyond inefficiency. Critical connections between early predictive stability data and actual results become impossible to make, hampering process understanding. Predictable and preventable manufacturing deviations go undetected until they cause batch failures. Tech transfer between development and manufacturing sites becomes needlessly time-consuming and complex.
Regulatory storm on the horizon
This data fragmentation problem will face additional pressure as regulatory agencies move toward mandatory electronic CMC submissions by 2026. Without a fundamental rethinking of data infrastructure, companies face the prospect of costly, manual data aggregation exercises for each submission. Our study found that 80% of CMC organizations identified late-phase portfolio acceleration as their top business challenge. Yet this acceleration is impossible without addressing the underlying data foundation.
Forward-thinking companies are already addressing this challenge by implementing purpose-built scientific data platforms for manufacturing. These solutions deliver concrete results. One of the top 10 life sciences companies we worked with eliminated 25-100 hours weekly of manual transcription in their purification process development, saving $375,000 annually in just one slice of their operation. In their bioprocessing operations, another biologics manufacturer decreased time-to-insight from one week to one day. A gene therapy developer increased throughput 6X by automating qPCR data workflows.
The most successful implementations share three characteristics:
- They prioritize data replatforming: Moving data from proprietary formats to open, standardized formats in the cloud creates the foundation for all other capabilities. For example, instead of having data locked in vendor-specific files from each instrument, the platform automatically converts it to a consistent format like JSON or Parquet. Harmonizing data enables easier analysis and integration with other systems.
- They implement data context and lineage: Maintaining the relationships between samples, methods, instruments, and results enables regulatory compliance and scientific insights. A modern data platform tracks every step in the process, from the raw materials to the final product, recording who did what, when, and with what equipment. Such a platform provides immutable audit trails for regulatory purposes and allows scientists to understand how process parameters affect product quality.
- They focus on scientific use cases: Unlike generic data solutions, successful scientific data platforms build upon specific manufacturing workflows like bioprocess development, formulation, and product quality and stability testing. Instead of a general-purpose database, the platform provides tools tailored to the needs of each workflow, such as method performance trending in chromatography or real-time monitoring of bioreactor parameters.
Beyond efficiency–building scientific intelligence
The true potential of modern data infrastructure goes beyond efficiency gains. You get a new scientific intelligence when data flows seamlessly from process and analytical instruments into a unified platform.
Quality teams can predict stability outcomes based on manufacturing conditions because the platform provides a unified view of process and analytical data, enabling trend analysis and modeling. Engineers can identify process parameters influencing product quality through multivariate analysis due to the platform’s ability to integrate data from various sources and provide advanced analytics tools. Teams concerned with product lifecycle management can make informed decisions using historical manufacturing data from similar products. The platform creates a centralized knowledge base of manufacturing data accessible for analysis and comparison.
Scientific intelligence is the foundation for true manufacturing excellence. It’s not just making products right, but also making them correctly.
With regulatory changes approaching and competitive pressures mounting, pharmaceutical manufacturers can no longer afford to postpone addressing their CMC data infrastructure. The companies that build robust scientific data foundations today will gain significant advantages in manufacturing efficiency, regulatory compliance, and process knowledge.
The question for manufacturing leaders is no longer whether to modernize their scientific data infrastructure, but how quickly they can do so. The future of pharmaceutical manufacturing depends on it.
Ken Fountain is the VP of scientific applications at TetraScience and has more than 25 years of experience in the life sciences industry.
