Stability Intelligence
Cross-program stability trending from governed portfolio-wide data. ICH-compliant monitoring, degradation signal detection, CQA alerts, and AI-assisted shelf-life prediction — built on traceable stability data from every instrument, batch, temperature, and timepoint in the portfolio.
Stability Data at Portfolio Scale
Stability studies generate massive datasets — hundreds of active studies producing thousands of data points per quarter across multiple ICH conditions, temperatures, and timepoints. Trends that matter are buried in isolated spreadsheets and standalone LIMS modules. Cross-program degradation signals — like polysorbate degradation appearing across multiple IgG1 products — remain invisible when each program tracks stability independently. Regulatory reporting deadlines compound the burden: every annual product review requires trending data assembled manually from all active conditions.
Four Capabilities for Portfolio-Scale Stability
ZONTAL transforms fragmented stability data into portfolio-level intelligence — ICH-compliant trending, cross-program signal detection, proactive monitoring, and AI-assisted shelf-life prediction, all built on governed, traceable data.
ICH Q1A/Q5C Trending
Full ICH-compliant stability trending personalized to active programs. Multi-condition monitoring at 5°C, 25°C/60%RH, and 40°C/75%RH with spec-limit alerts for monomer, HMW, charge variants, and sub-visible particles.
AI-Assisted Shelf-Life Prediction
AI-enabled Arrhenius model-based shelf-life prediction at target storage conditions, running on governed batch, temperature, and timepoint data. Query stability data directly: “Predict shelf life at 5°C for product X.”
Cross-Program Signal Detection
Portfolio-level degradation signal detection across programs. PS-20 degradation flagged across three IgG1 products triggers a recommendation: add lipase activity to the stability panel. Shared degradation pathways that manual review misses become visible.
Proactive CQA Monitoring
Critical quality attribute tracking with risk levels (green/yellow/red). Alerts fire when trending approaches specification limits — before failures occur. Each CQA links to its originating instrument, method, and raw data file for full traceability.
Measurable Stability Outcomes
Stability Intelligence delivers operational results from the first quarter of deployment — measurable impact on data throughput, prediction quality, and cross-program visibility.
How Stability Intelligence Gets Sharper Over Time
Stability Intelligence compounds from four capabilities working together. Each stage adds capability that the next one builds on — from raw instrument data to predictive shelf-life decisions.
Predictive shelf-life models require governed data with full scientific
context and lineage.
Evaluate readiness with the
five-question framework →
Integration Industrialization
Data Hub connects 150+ vendors and 400+ instrument models through governed Integration Factories. Every data point is validated and traced from source instrument to scientific context with full provenance. Integration factories bring stability data from HPLC, SEC, IEX, and particle counters with full provenance from every condition and timepoint.
- 150+ vendor instruments supported
- 400+ instrument models connected
- 8 core techniques · 80+ variants
- Stability-indicating method data from every ICH condition and timepoint
Scientific Context & Lineage
Digital Lab and Platform Modules build the scientific context graph — ontology mapping, cross-system identity reconciliation, and full lineage from instrument through result to insight. Stability study registry, ICH condition mapping, and CQA-to-study lineage connect every study, condition, timepoint, and result in the context graph.
- Ontology mapping across all data domains
- Cross-system identity reconciliation
- Full lineage graph: instrument → method → sample → result
- ICH Q1A/Q5C condition mapping with CQA-to-study lineage
Scientific Intelligence
Cross-program analytics surfaces trends, anomalies, and predictive signals that manual review misses — proven AI capabilities running on governed, validated scientific data. Portfolio-level analysis identifies shared degradation pathways across products and surfaces specification limit alerts before they become OOS events.
- Cross-program trend detection and comparison
- Anomaly detection and signal identification
- Predictive modeling on governed data
- Portfolio-level CQA risk dashboards with specification limit alerts
AI-Enabled Decisions
AI-assisted workflows consume governed data, scientific context, and intelligence to generate actionable outputs — with configurable oversight from full autonomy to human-in-the-loop approval gates. Arrhenius model-based shelf-life prediction, proactive alert routing, and automated regulatory reporting deliver actionable stability decisions.
- Guided workflow orchestration
- Configurable oversight — from approval gates to full autonomy
- Role-based views and reporting
- Arrhenius shelf-life prediction with automated CTD stability reporting
Trusted by 6 of the 10 largest pharmaceutical companies in the world
And leading biotechs and agrochemical companies
“Manual investigation currently takes 5–6 weeks just to find and aggregate the data. A site had to be pulled from initial filing.”
Plan Your Path to Stability Intelligence
ICH-compliant trending, Arrhenius shelf-life prediction, and cross-program degradation signals require a governed data foundation. Start building it today.