Frontier Conviction Scorer

Frontier Conviction Scorer — Discovery-Stage Trajectory Conviction, Anchored to Base Rates

Most early-stage biology is scored on narrative. A founder pitches a "bulletproof" target, a novel modality, a paradigm shift — and the listener's prior quietly drifts toward the optimism in the room. This skill exists to refuse that drift. Drug-development success is mostly priced by a small set of observable, discovery-stage features — therapeutic area, modality maturity, human genetic support, biomarker strategy, orphan status — and the base rates for those features have been stable across fifteen years of meta-analyses. The honest answer at discovery is never "model the cash flows"; it is "borrow a base rate and adjust it explicitly."

The core object is a Trajectory Conviction score = P(biology holds) × P(modality deliverable) × position-on-arc × competitive-timing, each factor on 0–1, every factor calibrated against an empirical anchor. The decomposition is the value: it localizes where the binding risk sits (the target, the platform, the arc position, or the clock) so a reader knows what to diligence next. The number is ordinal and relative — it ranks programs and flags risk; it is not an rNPV and must never be sold as one.

This skill is dual-use by construction. A clinical scientist learning the field uses it as a falsification discipline: it forces them to separate "is the target causal?" (biology) from "can this modality reach it?" (platform), and to confront that a genetically bulletproof target delivered by an unproven modality still fails on delivery. An investor screening early opportunities uses the same machinery to triage a deal flow against the class prior, anchor a term sheet to a defensible LOA, and know exactly when to retire the qualitative score for a phase-weighted rNPV. The base rates are the shared backbone of both readings.

How to Run

Input

Steps

Step 1 — Fix the Measurement Convention (do this first or every number is wrong)

Before quoting any base rate, decide the unit of analysis and never mix conventions:

• CDP / program-indication based (BIO/Informa/QLS) → overall ~7.9% LOA from Phase 1. Use when scoring a specific target-indication pair.
• Molecule based (ME) (Wong/Lo, DiMasi) → overall ~13.8%. Runs roughly 2x the CDP rate because one molecule can rescue a failed indication.

A discovery-stage candidate is a program-indication, so default to CDP (~7.9% as the general anchor; ~5% for the most recent 2015–2023 novel-modality-heavy window). Pick one and label it in the output. Silent conflation is the most common error in secondary citations.

Step 2 — Score P(biology holds) — is the target causal for the disease?

This factor speaks to the target, not the platform. Grade it on the human-genetics and validation evidence (hand off the genetic grading to target-validation-ladder when a graded rung exists):

• Human genetic support is the deepest signal: 2.6x relative success (Minikel/Nelson, Nature 2024; 9,704 programs; refining Nelson 2015's 2.0x). The lift rises with confidence in the causal gene and is independent of effect size, allele frequency, and discovery year — so grade the quality of the genetic link, not its magnitude. A clean coding variant or allelic series scores high; a confounded non-coding GWAS hit does not. Query Open Targets Genetics for L2G causal-gene confidence, gnomAD for constraint (pLI/LOEUF), Consensus/PubMed for replication.
• Biomarker availability / mechanistic clarity — a confident causal-gene target with a selection biomarker sits near the top of this factor (biomarker selection is itself a ~3x LOA lever, see Step 3).
• Orthogonal convergence — human genetics + animal model + tissue/expression + a selective dependency raises the score; a phenotypic-screen hit with no genetic anchor sits low.

Anchor scoring: confident causal gene + biomarker → 0.8–1.0; moderate association, plausible mechanism → 0.4–0.6; phenotypic hit, no genetic anchor → 0.1–0.3.

Step 3 — Score P(modality deliverable) — can it reach the target, at dose, safely, manufacturably?

Genetic support de-risks biology, not platform — keep them orthogonal. Anchor each modality to its empirical LOA, then haircut for delivery/CMC/regulatory novelty:

The CGT structural insight: the 5.3% blend hides bimodality. Durable CGT in rare/inherited disease shows Phase 2/3 success of 68–75% and submission→approval 81–90% ("no durable-CGT BLA has failed to eventually gain approval"). Risk is concentrated early (delivery, manufacturing, dose-finding). For a CGT program, weight Phase 1→2 and CMC risk heavily, the back end lightly — the opposite of a small-molecule oncology asset.

Full credit (0.8–1.0) only if the platform has prior human proof-of-concept. New modality, no clinical history → discount hard (see Step 4). Cross-reference modality-lifecycle for whether the delivery unlock has landed.

Step 4 — Handle a Zero-Clinical-History Modality (the hardest, most decision-relevant case)

Never start from an optimistic de novo number. The disciplined procedure:

1. Identify the nearest validated class and adopt its transition matrix as the prior (novel covalent SM → small-molecule matrix; new viral-vector → AAV 13.6%, front-loaded; novel-conjugate oligo → ASO/biologic blend; engineered cell → CAR-T matrix).
2. Split risk into biology vs modality. Genetic/biomarker support credits biology only.
3. Apply explicit haircuts to the analog's early transitions for (a) unproven delivery/biodistribution, (b) CMC/manufacturing novelty, (c) regulatory pathway novelty, (d) unknown chronic tolerability. Concentrate the haircut on Phase 1→2, where platform risk resolves.
4. Watch the de-risking inflection: platform risk collapses the moment any asset on the platform clears proof-of-mechanism in humans. Before that, weight platform dominant; after, revert toward the analog matrix.
5. Down-weight "AI-discovered" novelty unless the AI acted in molecular design with independent wet-lab/clinical validation — AI compresses discovery/preclinical but does not yet move Phase 2/3 attrition.

Step 5 — Score position-on-arc — the literal base-rate gate

Multiply by cumulative survival to date. This is where the empirical LOA enters as a hard floor:

This is why discovery-stage conviction must be relative — this program vs. the base rate for its class — not absolute. Cross-reference moa-analog-engine for where the candidate sits on the historical five-phase arc.

Step 6 — Score competitive-timing — will it matter when it arrives?

Order-of-entry, IP runway, standard-of-care drift, crowding. Query ClinicalTrials.gov and EDGAR/S-1 for the competitive set; patents (PCT/WO, 18-month lag) for undisclosed programs. A 4th-in-class asset entering an area where SoC will have moved gets discounted even if the science scores well. First-in-class to a validated, biomarker-defined population with IP runway scores high.

Step 7 — Apply the Therapeutic-Area & Lever Adjustments

Overlay the area base rate and the discovery-knowable levers onto the composite. Do not naively stack selection-biased designations onto genetic + biomarker effects (double-counting):

Step 8 — Trigger the rNPV Handoff

The moment a clinical asset and a defined lead indication exist, retire the trajectory score. Switch to a phase-weighted rNPV (hand off to asset-valuation/rnpv-modeler and probability-of-success/pos-calculator). The handoff PoS ladder:

Discount at 15–20% for early-stage biotech (10–15% mid-cap, 8–10% large pharma). Adjust each base PoS up/down for the Step 7 levers.

Output

FRONTIER CONVICTION SCORE — [Target] × [Modality] in [Indication]
Date: [assessment date]
Convention: [CDP / ME — labeled]   Stage: [lead-opt / IND-ready / P1 cleared]

TRAJECTORY CONVICTION = P(biology) × P(modality) × position-on-arc × competitive-timing
                      = [0.x] × [0.x] × [0.x] × [0.x] = [composite, ordinal]

Score decomposition:
  P(biology holds):        [0.x]  — [genetic rung; causal-gene confidence; biomarker; orthogonal convergence]
  P(modality deliverable): [0.x]  — [nearest validated class + LOA anchor; delivery unlock landed? Y/N; haircuts]
  Position-on-arc:         [0.x]  — [stage → unconditional P(approval) gate; arc phase from moa-analog-engine]
  Competitive-timing:      [0.x]  — [order-of-entry; IP runway; SoC drift; crowding]

Base-rate anchors used:
  Therapeutic area LOA:    [x]% ([area])
  Modality LOA:            [x]% ([class]; bimodality noted if CGT)
  Levers applied:          [genetic 2.6x / biomarker 3x / orphan 17% — list, no double-count]

BINDING RISK (where to diligence next):
  [Biology | Platform/delivery | Arc position | Competitive timing] — [one-line rationale]

For zero-clinical-history modality:
  Borrowed matrix:         [class] | Haircut concentrated on: P1→P2 | De-risking inflection to watch: [first platform PoC event]

rNPV HANDOFF:
  Trigger met? [Yes — clinical asset + indication exist / No — pre-clinical, stay ordinal]
  If yes → hand to rnpv-modeler + pos-calculator with PoS ladder anchored above.

Caveats:
  - Score is ORDINAL and RELATIVE — not a cash-flow estimate.
  - Genetic support de-risks biology, NOT platform.
  - [recency: 5% vs 7.9% anchor choice rationale]

Error Handling

Cross-Domain Connections

This skill is the synthesizer at the bottom of the Frontier Discovery analog engine; it depends on four PoS/valuation skills and serves both a learning clinical scientist and a screening investor.

• probability-of-success/pos-base-rates (depends_on): supplies the actuarial tables — the CDP/ME conventions, the by-area and by-modality LOAs, the genetic/biomarker/orphan multipliers. This skill is the discovery-stage front end to those base rates; pos-base-rates is the canonical data source both readings cite.
• probability-of-success/pos-calculator (depends_on): the rNPV-handoff target. When a clinical asset exists, the ordinal trajectory factors retire and pos-calculator applies the phase-weighted PoS ladder. The clinical scientist uses it to formalize a hunch; the investor uses it to anchor a term sheet.
• asset-valuation/rnpv-modeler (depends_on): consumes the handoff PoS ladder to produce the absolute, cash-flow-anchored, deal-ready valuation. Trajectory conviction decides what to advance; rNPV decides what it's worth once advancing.
• probability-of-success/mechanism-risk-adjuster (depends_on): refines P(biology holds) — applies mechanism-class and first-in-class penalties on top of the genetic-support credit so the biology factor reflects mechanism risk, not just target causality.
• modality-trajectory/modality-lifecycle: supplies P(modality deliverable) — whether the delivery unlock has landed and whether the modality is maturing or regressing on the arc.
• modality-trajectory/moa-analog-engine: supplies position-on-arc — the nearest validated analog and the still-pending ignition readout to watch.
• frontier-discovery/target-validation-ladder: supplies the graded genetic rung that feeds P(biology holds) directly.
• competitive-intelligence/pipeline-mapper: supplies the competitive set and order-of-entry feeding competitive-timing.