[WEBINAR] From Fragmented Data to Faster Process Characterization

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Process characterisation is often limited not by experimentation, but by how effectively data and knowledge are used. Historical insights are frequently fragmented, making it difficult to design studies with a clear, complete understanding of the process. This can lead to unnecessary experimentation or overlooked risks.

At the same time, increasing process complexity and regulatory expectations require strong linkage between parameters and CQAs, well-defined ranges, and confidence in the design space ahead of PPQ.

The Challenge of Fragmented Data

One of the most persistent hurdles during process characterization is that historical process information is rarely consolidated. This fragmentation creates several risks:

• Data quality varies, and critical knowledge is often isolated with individual team members rather than accessible systems.

• Without a complete prior knowledge base, teams risk over-experimenting or missing the critical factors that truly matter.

• Translating this fragmented understanding into a definitive control strategy to support process performance qualification (PPQ) becomes significantly harder.

A Structured, Digital-First Solution

A more structured, risk-based approach can help address this. By consolidating existing knowledge early, applying representative scale-down models, and using statistically robust experimental design, teams can focus effort where it matters most. Consistent data handling and clear traceability throughout execution further improve confidence in results and reduce rework.

Ultimately, integrating data, expertise, and experimental strategy from the outset enables more efficient study design, stronger process understanding, and faster progression towards PPQ without compromising scientific rigor.

Accelerating the Path to Market

By unifying workflows and breaking down data silos, a structured digital approach compresses timelines and generates highly robust process models. In a recent program involving a complex second-generation mammalian process, this strategy enabled the rapid evaluation of dozens of DOE designs and hundreds of digital experiments, keeping the program strictly on track for its impending PPQ campaign.