Pipeline Landscape by Therapy
Which competitors actually matter — and where the whitespace is.
A structured framework for mapping the pipeline landscape inside any therapy area — separating noise from credible competitors and surfacing modality whitespace.
"Where is real pipeline whitespace inside this therapy area — and which competitors do we genuinely have to plan against?"
A typical TA has 200–800 reported pipeline assets but fewer than 50 credible Phase 2+ competitors. The map that matters separates noise from real plays.
Pipeline maps are the most overproduced and least useful artifact in pharma intelligence. The fix is to filter ruthlessly for credibility, then cluster by modality and segment to expose real competition and real whitespace.
The map that drives portfolio decisions
Live, sponsor-credibility-filtered pipeline view, clustered by MoA and segment, with PoS-weighted forward sums. This is what BD, R&D and IC teams should use to make license-vs-build decisions.
What we’re seeing in the data.
Most pipeline noise dies in Phase 1
Phase-1 attrition removes 60–70% of headline pipeline counts.
Modality clustering reveals risk
Five candidates with the same MoA = peak share cap.
Whitespace lives in segments
Specific lines of therapy, biomarker subgroups, or geographies often remain undertargeted.
Sponsor profile shapes credibility
Big pharma + late-stage = real; first-time biotech in P2 = high uncertainty.
How to think about it.
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01
Pull all assets in scope
Phase 1+ in target indication & modality.
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02
Filter by credibility
Sponsor profile, prior data, completion status.
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03
Cluster by modality / MoA
Same MoA = direct competitor.
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04
Map whitespace
Segment, line, biomarker, geography.
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05
Score PoS and timeline
Project forward to launch year.
What separates a good answer from a defensible one.
Combination strategies redistribute share between MoA classes.
Accelerated approval candidates change competitive timing.
Patent expiry timing alters the competitive arc.
Country-by-country launch sequence affects local competition.
Where the signal comes from.
Common questions.
How granular should the map be?
Indication × line × biomarker × modality. Anything coarser misleads.
How do you score "credibility"?
Sponsor track record, prior trial data, regulatory path and trial-design quality.
Want this answered on your data?
We build decision systems on top of analyses like this — so the next question takes minutes, not weeks.
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