Evidence Synthesis & Living Knowledge Base

From fragmented evidence to continuously updated, decision-ready intelligence to support clinical development

NouStarX transforms disconnected evidence into a continuously updated, AI-curated Living Knowledge Base. By integrating published literature, clinical trials, real-world evidence, and internal insights, we convert evidence into structured, version-controlled evidence objects, such as endpoints, outcomes, populations, comparators, and safety signals, that can be queried, recombined, and reused across clinical development, medical affairs, and market access workflows.

Knowledge graph visualization

Key Capabilities

Automated Literature Reviews

Automated systematic and targeted literature reviews (PRISMA-aligned)

Structured Data Extraction

Structured extraction of endpoints, outcomes, comparators, and safety signals

Living Evidence Maps

Living evidence maps by indication, mechanism of action, or asset

Cross-Functional Intelligence

Reusable evidence components across clinical development, regulatory, medical affairs and HEOR teams

Active

Literature Evidence Layer

Turn published research into structured, reusable intelligence

Data Sources
Peer-reviewed publications
Clinical guidelines (NCCN, ESMO, AHA, etc.)
Conference abstracts and posters
AI & use cases

What the AI Does

  • Automates Systematic Literature Review (SLR)/Targeted Literature Review (TLR) workflows with transparent inclusion/exclusion logic
  • Extracts structured elements:
    • Endpoints (such as OS, PFS, QoL, biomarkers, etc.)
    • Effect sizes & safety outcomes
    • Study populations, comparators, lines of therapy
  • Grades and tags evidence by study design and risk of bias

Use Cases

  • Clinical Development: De-risk protocol design by grounding endpoints and eligibility in historical precedent.
  • Biostatistics: Power your trials with precision through quantitative benchmarking and rigorous simulation.
  • Medical Affairs: Translate complex trial data into strategic evidence roadmaps for expert engagement.
  • HEOR/Market Access: Secure market access by building predictive models that prove long-term value to payers.
  • Portfolio Strategy: Compare evidence strength across assets or MOAs
Active

Clinical Trial Evidence Layer

Learn from precedent to design better trials

Data Sources
Global trial registries (ClinicalTrials.gov, EU CTR, WHO ICTRP)
Protocols, SAPs, CSRs (where available)
Governed internal trial metadata
AI & use cases

What the AI Does

  • Parses trial records and protocols to extract:
    • Inclusion/exclusion criteria
    • Endpoints & assessment schedules
    • Enrollment assumptions & timelines
  • Links trials to downstream publications and outcomes
  • Builds trial precedent maps by indication, phase, and design

Use Cases

  • Clinical Development: Benchmark protocols vs. historical designs
  • Clinical Operations: Anticipate enrollment risk
  • Regulatory Affairs: Justify endpoint and comparator selection
  • Early Development: Identify white space or over-crowded designs

LIVING
KNOWLEDGE
BASE

Active

Real-World Evidence (RWE) Layer

Ground development decisions in real-world patient experience

Data Sources
Claims and EHR-derived datasets
Registries and observational studies
Published RWE
External RWD summaries (when direct access is constrained)
AI & use cases

What the AI Does

  • Harmonizes heterogeneous RWE into comparable evidence objects
  • Extracts:
    • Treatment patterns & lines of therapy
    • Real-world outcomes & safety signals
    • Patient journeys & attrition points
  • Links RWE insights to trial eligibility and endpoint strategy

Use Cases

  • HEOR/RWE: Quantify unmet need and disease burden
  • Clinical Development: Improve generalizability of trial criteria
  • Medical Affairs: Support real-world effectiveness narratives
  • Market Access: Strengthen HTA submissions with complementary RWE
Active

Internal Insights

Preserve institutional memory and accelerate decision-making

Data Sources
Internal study reports & slide decks
De-identified medical insights (governed access)
Prior regulatory & HTA submissions and responses
Internal evidence summaries & lessons learned
AI & use cases

What the AI Does

  • Securely indexes and summarizes internal knowledge
  • Links internal insights to external evidence
  • Enables semantic search across historical learnings
  • Flags gaps and inconsistencies between internal and external data

Use Cases

  • Regulatory Affairs: Reuse prior rationales and responses efficiently
  • Medical Affairs: Align field insights with published evidence
  • Portfolio Teams: Avoid repeating past mistakes
  • Leadership: Enable faster, evidence-backed strategic decisions

How the Layers Work Together

Example: Phase II Trial Planning

Literature LayerIdentifies prior endpoint failures
Trial Layer Benchmarks comparable designs and enrollment timelines
RWE LayerValidates patient availability and real-world treatment patterns
Internal Insights LayerSurfaces lessons learned from similar programs

Outcome: A more defensible, feasible, and submission-ready trial design—powered by continuously updated, cross-validated evidence.