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Pipeline & Clinical Intelligence

Clinical Trial Success Rates

PoS by phase × TA × modality × sponsor.

A defensible view of clinical-trial success rates — by phase, therapy area, modality and sponsor — for portfolio risk and asset valuation.

Decision angle

"What success rate should we apply to this asset for portfolio and valuation decisions?"

TL;DR

Phase 1→approval averages ~10% in pharma but varies enormously by TA and modality. Defensible PoS uses TA × phase × modality × sponsor — never a global average.

Probability of success is the single most leveraged number in pharma portfolio strategy. Using a global average is the most common mistake. Defensible PoS is always conditioned on therapy area, phase, modality and sponsor.

Build PoS as a system

Publish PoS values per asset as live numbers updated on every readout. Track the historical accuracy of your own PoS calls vs realized outcomes — that is what makes the system defensible.

Key insights

What we’re seeing in the data.

01

Onc P1→approval is far below industry avg

Solid-tumor IO and ADC programs have unique attrition curves.

02

Rare disease has unusually high P3 PoS

Smaller trials, biomarker selection and accelerated pathways shift PoS up.

03

Big pharma sponsors lift PoS

Better trial design, regulatory experience and resource depth measurable in PoS.

04

Endpoint type matters

Surrogate vs hard endpoints affect both PoS and label-quality.

~10%
P1→approval avg
Industry
~50–60%
P3→approval avg
Industry
5–25%
P1→approval onc
TA-specific
High
Rare disease P3
PoS lift
Decision framework

How to think about it.

  1. 01

    Anchor on TA × phase baseline

    Use published TA-specific PoS curves.

  2. 02

    Adjust for modality

    mAb, ADC, mRNA, ATMP, small molecule.

  3. 03

    Adjust for sponsor track record

    Big pharma vs first-time biotech.

  4. 04

    Adjust for endpoint risk

    Surrogate vs hard endpoint.

  5. 05

    Update on each readout

    PoS evolves with every interim, primary, and AdComm.

Considerations

What separates a good answer from a defensible one.

Bias in published rates

Published PoS often overstates due to survivorship bias.

Sub-group data

PoS for sub-group label may differ from overall PoS.

Manufacturing risk

Some "approval" delays are manufacturing not science.

AdComm and CRL volatility

Late-stage regulatory risk doesn’t always show in trial data.

Sources & tools

Where the signal comes from.

BIO/Pharmaprojects PoS data Cortellis success rates FDA / EMA approval database Internal historical PoS data
FAQ

Common questions.

Should we use industry average PoS?

No — always use TA × phase × modality × sponsor. Industry average is misleading.

How often does PoS materially change?

On every interim or primary readout — sometimes by 30+ percentage points.

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