The submission that was wrong before it was sent

Three months assembling Module 3. Specifications transcribed from LIMS. Process descriptions written from batch records. Stability tables built from study reports. A thousand small decisions about what to include, how to phrase it, whether this version was current.

Three weeks after submission, a reviewer question: "The potency specification in 3.2.S.4.1 says 95-105%, but the release data in 3.2.S.4.4 shows testing against 98-102%. Which is correct?"

Both. At different times. The specification changed during development. The CMC team updated one section, missed another. Not a typo—an architecture problem. When source data lives in multiple systems and the submission is manual assembly, inconsistency is inevitable.

Generated from structured data

Most CMC packages are assembled. Export from LIMS, format in Word, paste into the submission. Write process descriptions from batch records. Build stability tables from study reports. Manual assembly. Manual formatting. Manual cross-referencing.

Seal generates CMC content from structured data. Specifications in your submission are the specifications in your LIMS—not copies, views. Process descriptions come from the process definition in your MES—not summaries, representations. Stability tables pull from actual study data—not transcribed, queried. This isn't about convenience. It's about truth. When the submission reflects the same data that runs your operations, inconsistency becomes structurally impossible.

Specifications change throughout development. Early phase limits are wide. Characterization narrows them. Validation confirms them. Seal tracks changes as versioned data. Generate CMC content with the version you specify—internal consistency guaranteed. When reviewers ask why a limit changed, the evidence is linked.

Stability tables in most submissions are snapshots—frozen the moment they're created. Three months later, new timepoints exist. The submission is stale. Seal treats stability as live data, tables as queries. New data comes in, regenerate in seconds. When FDA asks for an update, you provide current data.

Global submissions mean multiple CMC packages: FDA, EMA, PMDA, Health Canada. Different formats, different expectations. Seal maintains one source with multiple presentation layers. Specifications, process, stability data—all the same. Only formatting changes. Update source data, all markets reflect it.

Reality matches the promise

CMC submissions are promises. Inspectors verify that reality matches. If your CMC describes a process that isn't quite how you run it, the gap becomes a finding.

Seal eliminates the gap by deriving submissions from operations. Writing process descriptions is usually translation—someone reads the batch record, writes regulatory language. Interpretations vary. Descriptions drift from reality. Seal generates process descriptions from process definitions. Unit operations, parameters, equipment, controls—all structured data. The Module 3 description is a view of that data. When the process changes, the description reflects it automatically.

CMC evolves with the program. IND has limited data. BLA has complete characterization. Most organizations rebuild each submission from scratch. Seal maintains CMC as evolving views of evolving data—see what the IND said versus what the BLA says, trace the evolution to the data that drove it.

Managing change and answering questions

Post-approval changes ripple through CMC. New indication. Site change. Specification revision. For most organizations, managing these ripples is a major project. Seal tracks impact automatically. Specification changes? See every place it appears. Process changes? Description updates. Method changes? Analytical section reflects it.

CMC documentation requires specific language—regulatory expectations, scientific precision, consistent terminology. Writing CMC is a specialized skill. Seal applies AI to content generation. Structured data becomes regulatory-ready text. Process parameters become process descriptions. AI drafts; scientists review and approve. Accurate because it comes from data.

When FDA or EMA reviewers ask questions, response quality matters. Fast, accurate, well-documented answers build confidence. Seal makes underlying data queryable. "What's the basis for this specification?" Query the characterization studies. "Show me stability data at 25°C/60%RH?" Generate in seconds. Responses derived from live data, not assembled from archives.