Quotient Sciences and Intrepid Labs Partner on AI to Speed Early Drug Development

Quotient Sciences + Intrepid Labs: What AI in Early Drug Development Means for Product Teams

A new partnership between Quotient Sciences and Intrepid Labs signals a clear shift: AI is moving upstream into early drug development. For product leaders, the takeaway is simple-focus AI on decision points that burn time and budget. Small, validated wins beat grand ambitions that never ship.

Below is a practical playbook to plan, pilot, and scale AI where it matters most.

Start with high-value decisions

• Candidate triage: Prioritize assets with the highest probability of technical and regulatory success.
• Dose selection: Use model-informed approaches to narrow first-in-human dose ranges.
• Protocol optimization: Simulate inclusion/exclusion criteria and visit schedules to reduce amendments.
• Site and cohort selection: Predict screen failure risk, enrollment velocity, and dropout likelihood.
• Formulation and CMC: Shortlist viable formulations, forecast manufacturability, and flag scale-up risk early.

Data foundations that actually work

• Inventory what you have: nonclinical, phase 0/1, CMC, real-world data. Note access limits and quality issues.
• Standardize where it counts: common IDs, controlled vocabularies, and tidy data tables for repeatable analysis.
• Provenance by default: track data source, version, and transformation steps for auditability.
• Privacy first: clear de-identification rules, access controls, and vendor data boundaries.

Model strategy: simple first, then sophisticated

• Baseline > fancy: Start with strong statistical baselines; add ML only if it beats them in offline tests.
• Right-size the risk: Classify model risk by decision impact; scale documentation and controls accordingly.
• Human-in-the-loop: Keep experts in review for dosing, safety, and any patient-impacting output.
• Reproducibility: Version data, code, features, and models; enable one-click reruns.

Validation that stands up in reviews

• Predefine success metrics: AUROC/PR for classification, calibration curves, decision lift, and cost impact.
• Use external validation where possible; mix temporal and site-based splits for realistic performance.
• Stress test on edge cases: rare AEs, small cohorts, and shifts in patient mix.
• Explainability: Provide feature importance and example-level rationale for clinical review.

Regulatory alignment early

Anchor AI-enabled decisions to existing guidance and pathways. Align with model-informed drug development and pre-IND touchpoints to reduce downstream friction.

• FDA Model-Informed Drug Development (MIDD) Pilot Program
• FDA resources on Real-World Evidence

Vendor selection checklist

• Evidence: peer-reviewed results or audited case studies on similar use cases.
• Data governance: clear boundaries, IP clauses, model ownership, and deletion SLAs.
• Security: SOC 2/ISO 27001, access controls, encryption in transit and at rest.
• Transparency: documented pipelines, monitoring, and drift alerts.
• Total cost: setup, usage, model maintenance, and exit fees. No black boxes.

Metrics that matter to product leaders

• Cycle time: days to candidate decision, protocol finalization, and site activation.
• Quality: reduction in protocol amendments, screen failure rate, and batch failure rate.
• Predictive lift: improvement over baseline decisions (e.g., enrollment forecasts, dose recommendations).
• ROI: time saved, cost avoided, and probability-of-success delta per use case.

90-day pilot plan

• Week 0-2: Select one decision problem; write a one-page brief (users, data, metric, risk level).
• Week 2-4: Assemble core triad-product lead, data scientist, domain expert (clinical pharmacology or CMC).
• Week 4-8: Build baseline model and dashboard; run offline validation; document assumptions.
• Week 8-12: Limited production with shadow mode; compare model vs. human decisions; finalize go/no-go.

Team roles and handoffs

• Product: defines problem, success metrics, and change management.
• Data engineering: ingestion, standardization, and secure access.
• ML/biostatistics: modeling, validation, monitoring.
• Domain experts: clinical pharmacology, toxicology, or CMC to review outputs.
• Quality/regulatory: documentation, audit trail, and governance.

Common failure modes to avoid

• Starting with weak data and expecting models to fix it.
• No clear decision owner-tools ship, behavior doesn't change.
• Skipping validation under "research only," then struggling to productionize.
• Vendor lock-in with unclear IP and export paths.

Bottom line

Partnerships like Quotient Sciences and Intrepid Labs point to a practical future: AI embedded in early decisions where weeks and dollars slip away. Pick one decision, measure rigorously, and scale what works. That's how momentum builds-and sticks.

If your team needs a structured upskilling path to support pilots and production rollouts, explore focused programs for product roles here: AI courses by job.