--- name: drug-development-pipeline-probability-of-success-evaluator description: "Estimates an asset's probability of technical and regulatory success (PTRS) by combining published phase-transition benchmarks with asset-specific risk adjustments across mechanism, target validation, clinical data quality, regulatory path, and competitive context — then outputs a structured confidence score, stage-by-stage cumulative LOA, and the top risk drivers and de-risking actions. Use when evaluating a pipeline asset's likelihood of approval, comparing portfolio candidates, building risk-adjusted forecasts, or diligencing an in-licensing or acquisition target." argument-hint: "[therapeutic-area] [development-stage]" allowed-tools: ["Read"] --- # Drug Development Pipeline Probability-of-Success Evaluator ## 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. 1. **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. 2. **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. 3. **Safety / tolerability**: Clean profile relative to class → positive; dose-limiting toxicities, black-box-class signals, or narrow therapeutic window → negative. 4. **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. 5. **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. 6. **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 This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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