Phase 3 Pipeline Analysis
Which Phase 3 candidates are most likely to reshape your TA.
A structured framework for analyzing the global Phase 3 pipeline — to inform pre-launch competitive intelligence, BD targeting, portfolio risk and forecast decisions.
"Which Phase 3 assets will materially reshape our TA economics in the next 36 months?"
Phase 3 success rates average 50–60% globally but vary 30–80% by therapy area, modality and sponsor. Probability-of-success-weighted pipeline analysis exposes which competitors actually matter.
The Phase 3 pipeline is the single most important leading indicator for TA-level competitive dynamics. The wrong way to analyze it: count assets. The right way: PoS-weight them and apply timeline and endpoint risk.
Three numbers per asset
Probability of success, expected launch year, expected peak share — those three numbers turn a list into a forecast. Apply them, sum across credible launches, and the answer becomes “which competitors actually matter,” not “which competitors exist.”
Run the pipeline as a live competitive engine
Update on every readout. Tag every primary endpoint, every AdComm, every regulatory action. The team that runs this system, not the team that buys quarterly reports, owns the launch sequencing decision.
What we’re seeing in the data.
PoS varies massively by TA
Onc Phase 3 PoS is much lower than cardio/metabolic.
Sponsor track record matters
Big-pharma sponsors with strong Phase 2/3 track record have measurably higher PoS.
Endpoint risk is the silent killer
Trials with novel endpoints face higher regulatory uncertainty even after positive readouts.
Crowded indications ≠ crowded launches
30 candidates in Phase 3 doesn’t mean 30 launches — most slip, fail or de-prioritize.
How to think about it.
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01
Pull all P3 assets in scope
Citeline / Cortellis / ClinicalTrials.gov.
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02
Score PoS per asset
TA, modality, sponsor, prior data, endpoint.
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03
Apply timeline risk
Recruitment status, primary completion, regulatory pathway.
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04
Build PoS-weighted launches
Forecast over realistic launches, not raw counts.
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05
Update on every readout
P3 readouts and sub-group data shift PoS — refresh quarterly.
What separates a good answer from a defensible one.
Positive ITT can mask weak sub-group results that affect label and pricing.
Surrogate endpoints face HTA scrutiny — model the gap.
Accelerated/conditional approval changes peak revenue trajectory.
Some P3 assets only succeed within combination — model fully.
Where the signal comes from.
Common questions.
How accurate are PoS estimates?
At TA × phase × modality, ±10% — useful for portfolio decisions but never over-precise.
How often does PoS change after readout?
Materially. Positive primary endpoint shifts asset PoS to >85% in most cases; failure pushes it to <10%.
Want this answered on your data?
We build decision systems on top of analyses like this — so the next question takes minutes, not weeks.
Talk to a strategist