Purpose
Produce a defensible, benchmark-anchored probability-of-technical-and-regulatory-success (PTRS) estimate for a single development asset, decomposed by phase transition and adjusted for asset-specific risk factors — so R&D strategy, portfolio, and BD teams can risk-adjust forecasts, prioritize candidates, and pressure-test diligence assumptions with a transparent scoring rationale rather than a black-box number.
When to Use
Invoke this skill when the user asks to:
- Estimate the probability of approval or likelihood of approval (LOA) for a pipeline asset
- Compare two or more candidates for portfolio prioritization or go/no-go decisions
- Build a risk-adjusted NPV or forecast input that needs a credible PTRS
- Diligence an in-licensing, partnering, or acquisition target's lead asset
- Stress-test an internal team's success-probability assumption against industry benchmarks
- Identify the specific risk factors dragging an asset's probability down and what would de-risk it
Inputs Required
Before starting, collect:
- Therapeutic area / indication: Specific disease and patient population (e.g., "2L metastatic NSCLC, EGFR-mutant")
- Mechanism of action / modality: Target, MoA, and modality (small molecule, mAb, ADC, cell/gene therapy, etc.)
- Current development stage: Preclinical, Phase 1, Phase 2, Phase 3, or filed
- Available data readouts: Efficacy, safety, biomarker, PK/PD, and any prior phase results with magnitude and significance
- Regulatory context: Designations held (Fast Track, Breakthrough, Orphan, PRIME), planned approval pathway, and chosen primary endpoint
- Competitive / precedent context: Approved or late-stage assets with the same target or in the same indication
- Target product profile (optional): The efficacy/safety bar required for commercial and regulatory viability
If competitive context or TPP is missing, infer from the indication and mechanism and flag the assumption explicitly.
Instructions
Step 1: Establish the Benchmark Base Rate
Anchor to published phase-transition success rates before any asset-specific adjustment.
- Select the base rate by stage and therapeutic area, since success rates vary materially by indication.
- Use these directional industry benchmarks for phase transition probabilities (cite as approximate, sourced from BIO/Biomedtracker/Pharmaprojects-type analyses; the user's own data supersedes these):
- Phase 1 → Phase 2: ~52% overall; oncology lower (~57% but high attrition downstream), hematology higher
- Phase 2 → Phase 3: ~29% overall — the highest-attrition transition; oncology ~25%, infectious disease higher
- Phase 3 → Filing: ~58% overall
- Filing → Approval: ~85–90% overall
- Cumulative Phase 1 → Approval (LOA): ~7–10% overall; oncology ~5–8%; rare/orphan and well-validated targets higher (~15–25%)
- State the base rate used and the source class. Compute the cumulative LOA from current stage to approval by chaining the remaining transitions.
Step 2: Score Asset-Specific Risk Modifiers
Adjust the base rate up or down across six risk dimensions. For each, assign a modifier: strong positive (+), positive (slight +), neutral, negative (slight −), strong negative (−−), with one-line reasoning tied to the inputs.
- Target / mechanism validation: Clinically validated target (approved drug hits it) → positive; novel/unprecedented MoA with only preclinical rationale → negative. Human genetic validation is a meaningful positive.
- Clinical data strength: Magnitude of effect vs. standard of care, statistical significance, consistency across endpoints, and whether biomarker enrichment is working. Strong, replicated efficacy signal → positive; single-arm or underpowered hints → negative.
- Safety / tolerability: Clean profile relative to class → positive; dose-limiting toxicities, black-box-class signals, or narrow therapeutic window → negative.
- Regulatory pathway / endpoint risk: Validated, accepted endpoint and a precedented approval path → positive; surrogate endpoint requiring agency acceptance, accelerated-approval confirmatory risk, or first-in-disease endpoint → negative. Breakthrough/PRIME designation → positive.
- Trial design & execution risk: Realistic powering, achievable enrollment, appropriate comparator, and defined patient selection → positive; overly optimistic effect-size assumptions, hard-to-recruit population, or comparator that has moved → negative.
- Competitive / standard-of-care risk: Differentiated against current and emerging SOC → positive; me-too profile entering after the standard has advanced, or risk of the bar moving before approval → negative.
Step 3: Synthesize the Adjusted PTRS
- Translate the modifier profile into a quantified adjustment band. Use a disciplined rule of thumb: each strong modifier shifts the relevant transition by roughly ±10–20% relative, each slight modifier by ±5–10% relative; cap total adjustment so the result stays within a credible range (do not exceed ~2x or fall below ~0.4x of the base LOA without exceptional justification).
- Present per-transition adjusted probabilities and the cumulative adjusted LOA.
- Show the math: base LOA → net modifier direction → adjusted LOA range (give a point estimate and a low/high band, not false precision).
Step 4: Assign a Confidence Rating
Rate confidence in the estimate itself — separate from the probability:
- High confidence: Mature data, validated target, precedented path, clear competitive read.
- Medium confidence: Partial data, some novel elements, or moving competitive landscape.
- Low confidence: Preclinical/early, novel MoA, unvalidated endpoint, or thin competitive context. State the single largest source of uncertainty driving the confidence rating.
Step 5: Rank Risk Drivers and De-Risking Actions
- List the top 3–5 risk drivers dragging the probability down, ranked by impact on the estimate.
- For each, give one concrete de-risking action and the data readout that would resolve it (e.g., "biomarker-stratified Phase 2 expansion cohort to confirm enrichment hypothesis").
- Identify the single most informative upcoming readout — the inflection point that would most move the PTRS.
Output Format
- Structure: Headed report progressing from benchmark base rate → risk modifier scoring table → adjusted PTRS → confidence rating → ranked risk drivers
- Length: 800–1,200 words
- Format: Prose for synthesis sections; a scoring table for the six risk dimensions; per-transition probabilities in a small table
- Sections: Asset Summary → Benchmark Base Rate (with source class) → Risk Modifier Scorecard → Adjusted PTRS (per-transition + cumulative LOA range) → Confidence Rating → Top Risk Drivers & De-Risking Actions → Most Informative Upcoming Readout
- Tone: Portfolio strategist — quantitative, source-aware, willing to commit to a number while bounding it honestly; never hand-waves a probability without showing the derivation
Quality Checklist
Before delivering output, verify:
- [ ] A specific benchmark base rate is stated by stage and therapeutic area, with its source class named
- [ ] All six risk dimensions are scored with a modifier direction and one-line reasoning each
- [ ] The adjusted PTRS is shown as a point estimate plus a low/high band — not a single false-precision figure
- [ ] Cumulative LOA is computed by chaining the remaining phase transitions, with the math shown
- [ ] Confidence rating is assigned separately from the probability, with the top uncertainty named
- [ ] Top risk drivers are ranked by impact, each paired with a concrete de-risking action and the readout that resolves it
Notes
- Probability and confidence are distinct: an asset can have a high PTRS estimate with low confidence (early, thin data) or a low PTRS with high confidence (mature data showing it won't clear the bar). Always separate them.
- Phase 2 → Phase 3 is the highest-attrition transition for most areas — weight scrutiny there for any pre-pivotal asset.
- Oncology base rates are systematically lower than the all-indication average; do not let a generic 10% LOA flatter an oncology asset.
- When the user provides their own internal benchmark data, that supersedes the directional figures here — anchor to their numbers and note the swap.
- Flag any case where the stated effect-size assumption appears inconsistent with the observed data; optimistic powering is a leading cause of Phase 3 failure.
How to use this skill
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Installing in Claude Code:
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- Drag this file into a new chat, or copy and paste its contents
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