The FMEA nobody needed

Product B is another monoclonal antibody. Same CHO platform. Same expression system. Similar purification train. The team schedules a risk assessment kickoff—two days in a conference room, sticky notes on whiteboards, scoring matrices in Excel.

Someone asks: "Didn't we already characterize temperature effects on glycosylation for Product A?" Yes. Three years ago. Eighteen months of studies. The data proved temperature between 34-38°C has no impact on quality. That knowledge exists—in a report somewhere, in a JMP archive, in the memory of scientists who may have moved on.

But Product B starts from scratch. New FMEA. New DoE studies. New characterization reports. Twenty-four months to IND. Half of it rediscovering what you already know.

Rolling control strategy

What if your control strategy built from what you've already proven?

Temperature affects glycosylation the same way across your mAb portfolio. You don't need another DoE to prove it. You need to apply what Product A taught you to Product B. The studies exist. The evidence is there. The knowledge should transfer.

Seal builds control strategy incrementally from platform knowledge. When you start Product B, the system shows: "For CHO-based mAbs, your platform data indicates temperature 34-38°C is non-critical for glycosylation. Evidence: 47 studies across 6 products. Do you want to adopt this as baseline?"

The FMEA becomes a delta review. What's different about Product B? New excipient? Different target protein? Those need characterization. But the platform knowledge—the relationships you've proven across products—carries forward automatically. Start from knowledge, not from zero.

Evidence that compounds

Every characterization study teaches you something. Most organizations lose that teaching in reports. The DoE from 2019 sits in a shared drive. The edge-of-failure study from 2021 is in someone's notebook. When Product C starts, nobody can find them—so you run them again.

Seal captures characterization as structured data that accumulates. Studies link to parameters, parameters link to quality attributes, relationships link across products. "Show me every study where we characterized pH effects on charge variants"—that's a query, not a research project.

When patterns emerge across products, the system surfaces them. Three mAbs show the same temperature-glycosylation relationship. That's platform knowledge. It should inform every future mAb without repeating the studies. Control strategy for Product D inherits what Products A, B, and C proved.

The scientists who ran those studies in 2019 might be gone. Their knowledge isn't. It's structured, queryable, and automatically applied to new programs.

Unit operation models

Protein A chromatography behaves the same way whether you're purifying Product A or Product J. Load density affects yield. Flow rate affects resolution. These relationships are properties of the unit operation, not the product.

Through Seal's custom services, we can build unit operation models from your anonymized cross-project data. Strip the product identifiers. Keep the parameters, conditions, and outcomes. The model predicts: "At this load density and flow rate, expect 85-90% yield." Not because someone wrote that in a report—because that's what the data shows across your portfolio.

When Product K starts, the unit operation models provide starting points. Not guesses—predictions based on your actual historical performance. Each product makes the model better. By Product Z, your Protein A model has seen more conditions than any single scientist could run in a career.

Confidential products stay confidential. The models see parameters and outcomes, not product names or molecular structures. Platform knowledge without IP leakage.

From characterization to control

Traditional characterization is a phase. Plan studies, execute them, write reports, file them. Months of work before you can define control strategy. Then the reports go to regulatory and development moves on. The knowledge is frozen.

Seal makes characterization continuous. Results flow into control strategy as they're generated—not after a report is written. Control strategy evolves with understanding, not after understanding is "complete."

When an inspector asks "why is temperature not a CPP for this product?", the answer isn't a paragraph someone wrote. It's a query: 47 studies across 6 products, all showing no correlation between temperature and glycosylation in this range. The evidence is live, traceable, and comprehensive.

CPPs become the parameters you monitor in manufacturing. Proven acceptable ranges inform your control limits. And CPV data flows back—commercial experience that confirms or challenges your platform knowledge.

The FMEA that took two hours

Remember Product B—the one that was going to take 24 months? Same CHO platform, same expression system, same purification train as Product A. But now, instead of sticky notes and scoring matrices, the team opens Seal.

The system shows: here's what you already know. Temperature 34-38°C, non-critical. pH 6.8-7.2, non-critical. Load density on Protein A, characterized across six products. The platform evidence is ready. The only question is: what's different about Product B?

Different target protein—that needs characterization. Everything else? Adopted from platform knowledge. Two hours instead of two days. IND in 12 months instead of 24.

Your tenth mAb benefits from everything you learned on the first nine. Not because you're taking shortcuts—because you're standing on a decade of evidence.