Real-World Example: Biotech Speeds Safety Signals on Databricks

Real-World Example: Biotech Speeds Safety Signals on Databricks

1. Problem / Context

A Phase II oncology biotech (~$120M revenue) was running two CROs with a lean, three-person data team under FDA/EMA pharmacovigilance oversight. Safety data lived in multiple places: AEs/SAEs in the EDC, lab feeds arriving nightly, and MedDRA coding managed across vendor tools. Reconciliation was slow, and safety signal discussions happened weekly because it took days to prepare line listings, deduplicate cases, and draft initial narratives. The organization needed faster, more reliable signal detection—without risking audit findings or overburdening its small team.

2. Key Definitions & Concepts

• Adverse Events (AE) and Serious Adverse Events (SAE): Reported safety events that must be ingested, reconciled, and analyzed for emerging risk.
• MedDRA coding: Standardized clinical terminology required for consistent classification of AEs.
• CDISC SDTM: The structure used to harmonize clinical data for analysis and regulatory submission.
• Agentic AI: A governed approach where software agents monitor sources, take actions (e.g., ingest, transform, reconcile), and coordinate workflows while keeping humans in the loop.

Databricks components used:

• Unity Catalog for permissions, lineage, and audit trails.
• Databricks SQL (DBSQL) for parameterized line listings and dashboards.
• MLflow for model versioning, approvals, and traceability.

3. Why This Matters for Mid-Market Regulated Firms

Mid-market biotechs operate with tight budgets and lean teams, yet face the same pharmacovigilance obligations as larger organizations. Safety data arrives from CRO SFTP drops and APIs at irregular cadence; clinical teams need near-real-time visibility; QA expects audit-ready traceability; and IT must protect PHI/PII. Without disciplined governance, “pilot” notebooks sprawl into non-validated scripts—fueling the pilot graveyard that never makes it to production. A structured, governed approach on Databricks allows a small team to coordinate across CROs, compress cycle time, improve audit confidence, and scale methodically as trials expand.

4. Practical Implementation Steps / Roadmap

1. Establish a governed lakehouse foundation
   • Create production and non-production workspaces with Unity Catalog as the single source of permissions, data lineage, and audit logging.
   • Define roles for PV, Clinical Ops, QA, and IT; use service principals for agents and enforce least privilege.
2. Connect to CRO feeds and vendor systems
   • Agents monitor CRO SFTP folders and APIs for EDC extracts and lab feeds, quarantining unexpected file types and schema drift.
   • Land raw data to bronze Delta tables with metadata stamping (source, load time, checksum, provider).
3. Harmonize to CDISC SDTM
   • Apply transformation jobs to map raw extracts into SDTM structures (e.g., AE, DM, LB), with automated validation checks for domain conformance and referential integrity.
   • Record all transformation logic and validations so that QA can reproduce runs.
4. Deduplicate and reconcile cases
   • Use deterministic and probabilistic matching to deduplicate SAEs based on subject ID, visit, onset date, and coded term; flag conflicts for human review.
   • Maintain reconciliation status and comments as first-class, auditable records.
5. Accelerate narrative drafting with human-in-the-loop agents
   • Agents assemble case context from SDTM domains and propose narrative drafts using approved templates.
   • PV reviewers accept, edit, or reject suggestions; comments and final text are versioned and linked to the case ID.
6. Generate line listings with Databricks SQL
   • Parameterized DBSQL dashboards produce daily AE/SAE listings by treatment arm, grade, seriousness, and relatedness.
   • Security filters enforce row- and column-level access, ensuring only authorized teams see PHI/PII.
7. Govern the model and workflow lifecycle
   • Track models and narrative templates in MLflow; promote only after offline evaluation against historical narratives.
   • Use release gates and change control to move updates from dev to prod, with Unity Catalog lineage supporting audit checks.
8. Enable cross-functional collaboration
   • PV, Clinical Ops, QA, and IT work in governed workspaces with clear ownership, standardized SOPs, and shared dashboards.
9. Scale by design
   • Start with one CRO and one therapeutic area; expand to the second CRO and additional indications once controls prove out.

5. Governance, Compliance & Risk Controls Needed

• GxP validation package: Document intended use, requirements, design, test protocols, and acceptance results for each workflow and model-assisted step.
• Change control and release gates: Every transformation, prompt template, and model version moves through defined approvals before production.
• Unity Catalog controls: Centralize permissions, enforce least privilege, and capture audit trails and lineage for data, notebooks, jobs, and dashboards.
• MLflow traceability: Register models and prompts, log training data references, and pin versions used in production for full reproducibility.
• Human-in-the-loop checkpoints: Narrative proposals are suggestions; final medical judgment remains with qualified PV reviewers.
• Data minimization and masking: PHI/PII is restricted to the minimum necessary; sensitive columns are masked except for authorized roles.
• Vendor and portability safeguards: Use open formats (Delta, SDTM mappings) and documented APIs to avoid lock-in and ensure continuity across CROs.

6. ROI & Metrics

In this program, SAE processing time dropped by 40%, moving signal review from weekly to daily cadence and improving responsiveness without adding headcount.

Teams tracked:

• Cycle time: Elapsed time from SAE receipt to reconciled case and first narrative draft.
• Deduplication accuracy: Match precision/recall against a curated truth set.
• Narrative turnaround: Draft-to-approval time, including reviewer edits.
• Line listing freshness: Time-to-dashboard and query completion rates in DBSQL.
• Audit readiness: Percentage of workflows with complete validation and change-control artifacts.
• Meeting cadence shift: Weekly to daily signal meetings with actionable listings.

These metrics make ROI tangible: less manual rework, fewer meeting hours spent collecting data, and faster risk escalation—while maintaining regulatory confidence.

7. Common Pitfalls & How to Avoid Them

• Pilot graveyard: Notebooks in a sandbox with no validation rarely make it past QA. Solve by defining a GxP validation package early, with clear release gates.
• Schema drift and broken feeds: CRO exports change. Use schema versioning, quarantine rules, and alerting so agents escalate rather than silently failing.
• Uncontrolled narrative generation: Keep templates, prompts, and models versioned in MLflow; require reviewer approval to avoid unvetted language.
• Role ambiguity: Without clear ownership, security and quality erode. Assign responsibilities across PV, Clinical Ops, QA, and IT, reflected in Unity Catalog roles.
• Over-customization: Hard-coded mappings won’t scale across CROs. Standardize SDTM transformations and make mappings configuration-driven.

30/60/90-Day Start Plan

First 30 Days

• Discovery: Inventory AE/SAE workflows, CRO feeds, MedDRA coding tools, and current reconciliation and narrative steps.
• Data checks: Confirm SDTM targets, identify PHI/PII fields, and define masking/minimization rules.
• Governance boundaries: Stand up non-prod and prod workspaces; configure Unity Catalog; draft validation plan and SOP templates.
• Metrics baseline: Measure current SAE cycle time, narrative turnaround, and listing freshness.

Days 31–60

• Pilot workflow: Connect to one CRO’s SFTP/API and build the ingestion-to-SDTM pipeline with deduplication.
• Agentic orchestration: Enable agents to monitor feeds, trigger transforms, and open review tasks.
• Narrative assist: Introduce template-driven narrative drafts with mandatory human review.
• Security controls: Enforce role-based access, column masking, and DBSQL row filters. Track all artifacts in MLflow.
• Evaluation: Compare against baselines; capture validation evidence and user feedback.

Days 61–90

• Scale and harden: Add the second CRO or a second therapeutic area using the same SDTM mappings and controls.
• Monitoring and alerting: Implement health checks for feeds, schema drift, and job SLAs.
• Metrics and reporting: Publish dashboards for cycle time, dedup accuracy, and narrative turnaround to PV/QA leadership.
• Stakeholder alignment: Formalize change control, set release cadence, and schedule daily signal reviews.

9. Industry-Specific Considerations

• Oncology complexity: Lab trends (e.g., neutrophils), combination therapies, and overlapping toxicity profiles increase the need for precise deduplication and context-rich narratives.
• Multi-CRO reality: Standardized SDTM mappings and controlled line listings allow faster onboarding of additional CROs without rewriting pipelines.

10. Conclusion / Next Steps

This mid-market biotech compressed safety timelines, improved daily situational awareness, and passed inspection without findings by pairing agentic automation with strong governance on Databricks. Unity Catalog enforced permissions and lineage, MLflow managed models and templates, and DBSQL operationalized line listings—together enabling a three-person team to scale PV workflows across two CROs. If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone—helping with data readiness, MLOps, and compliant workflow orchestration from pilot to production.

Kriv AI is a governed AI and agentic automation partner focused on regulated mid-market companies. In this example, the team avoided the pilot graveyard by standing up a GxP validation package, change control, and release gates—turning a promising notebook into an auditable, production-ready system. With the right controls, you can achieve similar outcomes: faster signal detection, reliable inspection readiness, and measurable ROI—without growing your team.