Global-ClinicalTrials
Top drivers
⌁ mcp.call("adw-377") vADW-377-live-1.0 Is the global clinical-trial landscape becoming more or less complex?
Global-ClinicalTrials
Top drivers
⌁ mcp.call("adw-377") vADW-377-live-1.0 A clinical-operations planning agent polls ADW-377 monthly and uses the 5-dimension breakdown (late-phase share, large-enrollment share, industry-sponsor share, RCT share, blinded-design share) from the enriched top_drivers field to forecast CRO capacity pressure: when the index rises above 55 on a rising trend, the agent automatically flags open site-selection RFPs as operating in a high-competition environment and adjusts site fee bids upward by a configurable percentage to maintain recruitment competitiveness. The methodology_version ensures the agent's 'complexity environment' designation references the same ClinicalTrials.gov active/recruiting snapshot that underpins the score, making the bid-adjustment logic defensible to clinical leadership.
A VP of Clinical Operations at a biotech uses ADW-377 to time the launch of new Phase 2 and Phase 3 trials. When the index is near or above 55 — indicating the landscape is shifting toward more large-enrollment, industry-sponsored, late-phase trials that compete for the same investigator sites and patients — she knows site activation timelines will extend and adjusts her enrollment milestone commitments to the board accordingly. This replaces the current practice of subjectively estimating competitive pressure from lagged industry conference reports, which often arrive 3–6 months after the conditions they describe.
weighted share composite normalized to 50 baseline
Version ADW-377-live-1.0 · validated to beat a naive baseline · benchmark: No free equivalent; CRO pipeline DBs are proprietary
One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-377.
{
"product_id": "ADW-377",
"entity": "Global-ClinicalTrials",
"score": 50,
"trend": "stable",
"confidence": 0.82,
"top_drivers": [
{
"factor": "late_phase_share_pct",
"contribution": 10.48
},
{
"factor": "large_enrollment_share_pct",
"contribution": 9.89
},
{
"factor": "industry_sponsored_share_pct",
"contribution": 17.81
},
{
"factor": "randomization_rate_pct",
"contribution": 60.09
},
{
"factor": "high_blinding_rate_pct",
"contribution": 22.35
},
{
"factor": "total_recruiting_active_trials",
"contribution": 87372
}
],
"recommended_use": "Track the structural complexity of the active global clinical-trial landscape. Rising scores indicate the R&D environment is shifting toward more resource-intensive designs (later-phase, large randomized blinded studies). Useful for CRO capacity planning, biotech/pharma investment screening, and regulatory burden forecasting. Descriptive only — not a drug-efficacy signal.",
"methodology_version": "ADW-377-live-1.0",
"freshness": "2026-06-27T04:00:21.192Z",
"coverage": "All actively recruiting or active-not-recruiting clinical studies registered on ClinicalTrials.gov (FDAAA 2007 mandatory registry). Global scope including US, EU, and international sites. Covers interventional and observational designs.",
"source_lineage": [
"clinicaltrials.gov/api/v2/studies — filter.overallStatus=RECRUITING,ACTIVE_NOT_RECRUITING",
"clinicaltrials.gov/api/v2/studies — filter.advanced=AREA[Phase]PHASE3",
"clinicaltrials.gov/api/v2/studies — filter.advanced=AREA[Phase]PHASE4",
"clinicaltrials.gov/api/v2/studies — filter.advanced=AREA[EnrollmentCount]RANGE[1000,MAX]",
"clinicaltrials.gov/api/v2/studies — filter.advanced=AREA[LeadSponsorClass]INDUSTRY",
"clinicaltrials.gov/api/v2/studies — filter.advanced=AREA[DesignAllocation]RANDOMIZED",
"clinicaltrials.gov/api/v2/studies — filter.advanced=AREA[DesignMasking]TRIPLE|QUADRUPLE"
],
"allowed_use": "informational",
"validation_status": "descriptive",
"total_recruiting_active_trials": 87372,
"phase3_count": 6267,
"phase4_count": 2886,
"large_enrollment_count": 8637,
"industry_sponsored_count": 15565,
"interventional_count": 64674,
"randomized_count": 38860,
"triple_blind_count": 3585,
"quadruple_blind_count": 5099,
"dimension_shares": {
"late_phase": 0.1048,
"large_enrollment": 0.0989,
"industry_sponsored": 0.1781,
"randomized": 0.6009,
"high_blinding": 0.2235
},
"composite_raw": 0.20425,
"baseline_composite": 0.20428,
"next_fresh_at": "2026-07-27T04:00:21.192Z",
"failed_fetches": 0,
"data_notes": [
"Enrollment counts are registrant-estimated; some studies over-report anticipated size.",
"Phase is self-reported; occasional mislabeling possible for novel platform trials.",
"ACTIVE_NOT_RECRUITING includes studies near wind-down (may slightly inflate complexity).",
"Score baseline 50 calibrated to 2026-06-25 live CT.gov snapshot (87,290 active trials)."
]
} Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.
Dashboard
Read the score + drivers in the console.
REST API
/v1/intelligence/adw-377
MCP tool
adw.adw_377
Marketplace
Discoverable by any MCP agent via the MCP registry.
White-label
Embed under your own brand (Platinum).
Is healthcare inflation running meaningfully above or below general core inflation right now?
Method: Medical CPI YoY minus Core CPI YoY spread; z-score vs 36mo trailing window × 15 → 0-100
How intense is pharma/device industry financial engagement with US physicians, and growing?
Method: score=clamp(50*recent_count/3yr_baseline_mean,0,100)
Is drug labeling activity accelerating or decelerating vs baseline?
Method: Rolling 12-mo label count vs prior 12-mo baseline (openFDA drug label API). Score 50 = neutral 10% YoY.