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Pharma Evidence Intelligence Suite

Pharma Evidence Intelligence Suite uses advanced AI to discover, analyze, and synthesize clinical, real‑world, and regulatory evidence across the global literature and key data sources. It automatically surfaces relevant studies, extracts critical endpoints and safety signals, and generates traceable, regulator-ready insights to support drug development and medical affairs. This accelerates evidence generation, reduces manual review effort, and improves decision quality across the pharmaceutical R&D lifecycle.

The Problem

“Slow, inconsistent evidence review and safety coding delay regulatory and R&D decisions”

Organizations face these key challenges:

1

FDA accepts a limited number of RWE requests per cycle, so weak narratives create costly missed opportunities

2

Study design narratives are reviewed manually against complex and evolving FDA criteria

3

Clinical, real-world, and regulatory evidence is spread across many databases and document formats

4

Patient-reported ADR narratives are unstructured, ambiguous, and often contain multiple symptoms in one report

5

Manual MedDRA coding is slow, inconsistent, and difficult to scale during case volume spikes

6

Teams need regulator-ready traceability, confidence scoring, and audit logs for every recommendation

7

Cross-functional review cycles create bottlenecks between safety, epidemiology, regulatory, and medical affairs teams

Impact When Solved

Reduce RWE proposal quality-check time from days to hours before FDA submission windowsIncrease first-pass completeness of study design narratives against FDA selection criteriaAutomate large-scale multi-label ADR coding from patient free text with human-in-the-loop reviewImprove traceability with source-linked evidence extraction and rationale generationStandardize evidence synthesis and safety coding across teams, vendors, and regionsLower operational cost per reviewed document or coded case

The Shift

Before AI~85% Manual

Human Does

  • •Define evidence questions and review scope for development, safety, or medical affairs needs
  • •Search literature and data sources manually, then collect and organize relevant studies
  • •Read full texts and abstracts to extract endpoints, comparators, outcomes, and safety findings
  • •Discuss findings across stakeholders and decide what evidence supports next actions

Automation

    With AI~75% Automated

    Human Does

    • •Set evidence objectives, inclusion criteria, and acceptable use boundaries for each review
    • •Validate high-impact findings, adjudicate ambiguous evidence, and handle exceptions
    • •Approve evidence summaries, safety interpretations, and regulator-facing conclusions

    AI Handles

    • •Continuously scan approved literature and data sources to identify relevant new evidence
    • •Rank, cluster, and triage studies by indication, population, endpoint, and relevance
    • •Extract key study details, endpoints, safety signals, and real-world outcomes into structured summaries
    • •Generate traceable evidence syntheses and draft regulator-ready insight packages with citations

    Operating Intelligence

    How Pharma Evidence Intelligence Suite runs once it is live

    Humans set constraints. AI generates options.

    Humans choose what moves forward.

    Selections improve future generation quality.

    Confidence89%
    ArchetypeGenerate & Evaluate
    Shape6-step branching
    Human gates2
    Autonomy
    50%AI controls 3 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapebranching

    Step 1

    Define Constraints

    Step 2

    Generate

    Step 3

    Evaluate

    Step 4

    Select & Refine

    Step 5

    Deliver

    Step 6

    Feedback

    AI lead

    Autonomous execution

    2AI
    3AI
    5AI
    gate
    gate

    Human lead

    Approval, override, feedback

    1Human
    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    Humans define the constraints. AI generates and evaluates options. Humans select what ships. Outcomes train the next generation cycle.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Technologies

    Technologies commonly used in Pharma Evidence Intelligence Suite implementations:

    LLM evaluatorOther
    4 mentions
    Regulatory rubric/criteria engineOther
    4 mentions
    Submission checklist databaseOther
    4 mentions
    Medical and biostatistics reviewerOther
    3 mentions

    Key Players

    Companies actively working on Pharma Evidence Intelligence Suite solutions:

    IQVIACertaraParexelSyneos Health

    Real-World Use Cases

    AI quality-checking of RWE study design narratives against FDA selection criteria

    An AI reviewer checks whether an RWE proposal clearly explains fit-for-use data, study design quality, and regulatory conduct so the sponsor can improve the package before submission.

    evaluation and gap analysisproposed workflow with strong source grounding in explicit fda selection criteria, but not described as deployed.
    10.0

    Automated coding of patient-reported adverse drug reactions from free-text reports

    An AI system reads patients’ written descriptions of side effects and automatically tags the adverse drug reactions, reducing manual review work for pharmacovigilance teams.

    Multi-label clinical text classification and coding from unstructured patient narrativespilot/validated prototype with external validation, not yet proven as fully production-grade at scale.
    10.0

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