Eighteen months of data. One number that changes everything.

The regulatory submission was three weeks away. Eighteen months of stability data, all trending perfectly—until the 18-month timepoint came back. Assay: 89.2%. Specification: NLT 90.0%.

Nobody saw it coming. The 12-month result was 94.1%. The 15-month was 92.3%. But the degradation rate wasn't linear, and nobody had run the regression. Nobody had projected when the curve would cross the specification limit. The data existed—it just sat in columns waiting for someone to ask the right question.

The submission deadline slipped by six months. The commercial launch slipped with it. The competitor who filed two months later beat them to market. All because a spreadsheet full of numbers never told anyone what those numbers meant.

Stability is a program, not a collection of samples

A single product stability program spans years—multiple protocols at different storage conditions, dozens of timepoints, hundreds of individual tests. One missed pull can force you to restart a study. One chamber excursion can invalidate months of data. One trending issue discovered too late can delay your submission by a year.

Most LIMS treat stability as an afterthought: just more samples to test. They track sample IDs and test results. They don't understand that stability is a program with structure—protocols define conditions, conditions define timepoints, timepoints generate samples, samples generate results, and results build trends that predict the future. Without that structure, you're not managing stability. You're managing a spreadsheet.

The 6-month pull nobody noticed

It happens more often than anyone admits. A timepoint was due last week. The calendar reminder got buried in email. The analyst who usually handles pulls was on vacation. Nobody noticed until the monthly report revealed a gap. Now the study has missing data that auditors will question, and there's no way to go back in time.

The investigation takes three days. Was the protocol violated? Can the study continue? Does the gap affect the registration timeline? In the end, the study continues with a documented deviation—but the auditor will ask about it, and the answer will be "human error in a manual tracking process."

Chamber excursions are worse. The alarm went off overnight. Maintenance silenced it and noted "temp spike, resolved." But which samples were affected? How long were they out of spec? Someone pulls up the data logger, exports to Excel, cross-references against the chamber loading log (which is in a different system), and tries to figure out which of the 200 samples in that chamber were actually compromised. Hours of work that could have taken minutes.

The most painful failure is trend blindness. Eighteen months into a critical study, the assay result comes back at 89%—just below the 90% specification. Nobody saw it coming because nobody was watching the trend. The data was there all along—twelve individual results that, plotted on a graph, clearly showed the trajectory. But who has time to plot graphs manually for every attribute on every study?

Protocol-driven from the start

Seal treats stability as a first-class concept, not an afterthought bolted onto sample management. You define protocols with storage conditions, timepoints, and testing requirements. The system generates the complete schedule automatically—every pull date from month zero through month sixty, calculated and tracked.

ICH conditions are built in: long-term at 25°C/60% RH, intermediate at 30°C/65% RH, accelerated at 40°C/75% RH. Refrigerated, frozen, and photostability conditions are ready to use. Custom conditions for specific product requirements work the same way.

Proactive alerts mean the system watches the calendar so humans don't have to. Two weeks before a timepoint, the responsible analyst gets notified. If a pull becomes overdue, escalation begins automatically. Missed pulls become genuinely difficult rather than routine.

When chambers drift

Stability chambers are the heart of your program, and Seal monitors them continuously. Connect your chamber sensors and see real-time temperature and humidity on a dashboard. Historical data logs automatically for your audit trail.

When conditions drift outside limits, the alert fires immediately—not when someone checks the data logger the next morning. The system identifies which samples were in that chamber during the excursion, calculates the duration and temperature range automatically, and generates a pre-populated impact assessment. What used to take hours of manual cross-referencing happens in minutes.

Seeing the future in your data

The real power of systematic stability management is trending. As results accumulate over months and years, patterns emerge. Products don't usually fail suddenly—they degrade gradually, and that degradation is visible in the data long before it crosses a specification limit.

Seal generates trend charts automatically as each result is entered. Live visualization shows how every stability attribute is changing over time. Statistical regression projects when each attribute will reach its specification limit. If your product is trending toward OOS at month 30, you know at month 12—not at month 30 when it's too late to do anything but watch your timeline slip.

The projections aren't just lines on a graph. The system calculates confidence intervals based on data density and variability. When there's enough data to make a reliable prediction, you see it. When the data is too sparse or too variable, the system tells you that too. No false confidence in shaky projections.

The AI learns from your historical data. Products that look similar to past failures get flagged early. Degradation patterns that match known failure modes trigger alerts. You have time to investigate, adjust formulation, or plan for the business impact. You stop being surprised by stability failures.

The week before submission

Regulatory filing deadlines used to mean weeks of compilation work. Someone would pull stability data from the LIMS. Someone else would export chamber logs. A third person would build trend charts in Excel, manually adjusting axis scales and adding trendlines. A fourth would compile everything into the CTD Module 3 format, cross-referencing page numbers and table numbers across hundreds of pages.

The errors were inevitable. Chart 47 showed data through month 18, but the table showed data through month 15 because someone forgot to update it. The trend line in Figure 12 used different regression parameters than the one in Figure 8 because different analysts built them. Page 234 referenced "See Table 23" but Table 23 was actually Table 24 after someone inserted a table earlier in the document.

Seal compiles submission packages automatically. All data for a product, organized by protocol and condition, with complete history. Trend charts generate from the underlying data—no manual chart creation, no possibility of the chart not matching the data. Tables and figures reference each other correctly because they're generated from the same source. Before you export, the system shows any gaps: missed timepoints, pending tests, incomplete analyses. You find problems before the submission, not when reviewers send questions three months later.

The full lifecycle

From the moment you initiate a study to the day you archive it, every step is tracked. Define the product, batch, protocol, and testing panel. The system creates sample placeholders and generates the complete schedule. As pulls happen and testing completes, results flow in and trends update. When the study concludes, generate final reports with one click. Archived studies remain accessible for regulatory queries years later.

Integration without double entry

Already running stability tests in another LIMS? Seal connects to LabWare, STARLIMS, Benchling, and major instrument data systems like Empower and OpenLab. Your analysts continue testing in the systems they know. Results flow into Seal automatically, and trending happens without anyone re-entering data. The stability program runs on top of your existing infrastructure.