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Probability of Success Calibration

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PhaseFolio derives stage-transition probabilities from observed clinical outcomes rather than expert opinion, following Thomas et al. (2021) and Wong et al. (2019). The benchmark matrix is three-dimensional (11 indications × 8 modalities × 3 biomarker strategies = 264 cells); seven evidence-based multipliers are applied through a log-odds (logit) transformation to keep results bounded and reflect diminishing returns at high baselines.

1. Three-dimensional benchmark matrix

Rather than applying a single set of industry-average transition rates, PhaseFolio stratifies PoS by three independent classification axes that are known to materially affect clinical outcomes: therapeutic area, drug modality, and biomarker strategy.

  • Therapeutic Area. 11 indications from oncology (solid and hematologic) through cardiovascular, neurology, metabolic, and rare disease. Oncology solid tumor has the lowest overall LoA (~2.5%); rare disease the highest (~9.4%). Source: Thomas et al. 2021.
  • Drug Modality. 8 modalities including small molecule, monoclonal antibody, bispecific, ADC, cell therapy, gene therapy, and peptide. Modality affects safety profiles and regulatory pathways. Source: Citeline 2024.
  • Biomarker Strategy. 3 levels: none, enrichment (biomarker-selected population), and companion diagnostic (required for Rx). Biomarker use drives Phase II and III success rates up to 4×. Source: Parker et al. 2015.

The full matrix contains 11 × 8 × 3 = 264 unique indication–modality–biomarker combinations, each specifying five stage-transition probabilities (Preclinical → Phase I → Phase II → Phase III → NDA/BLA → Approval). Values are derived from meta-analysis of 12,728+ clinical-stage transitions (BIO/QLS/Informa 2021) with modality-specific adjustments from Citeline 2024 pipeline data.

Table 1 — Baseline stage-transition probabilities by therapeutic area (small molecule, no biomarker strategy; Overall LoA is the product of all five transition rates):

  • Oncology (Solid): Preclinical 5.0%, Phase I 40.0%, Phase II 24.0%, Phase III 55.0%, NDA/BLA 90.0%, Overall LoA 2.4%
  • Oncology (Hematologic): Preclinical 7.0%, Phase I 72.0%, Phase II 42.0%, Phase III 63.0%, NDA/BLA 90.0%, Overall LoA 12.0%
  • Rare Disease: Preclinical 8.0%, Phase I 56.0%, Phase II 38.0%, Phase III 64.0%, NDA/BLA 93.0%, Overall LoA 9.4%
  • Neurology: Preclinical 4.0%, Phase I 46.0%, Phase II 20.0%, Phase III 47.0%, NDA/BLA 88.0%, Overall LoA 1.5%
  • Immunology: Preclinical 6.0%, Phase I 49.0%, Phase II 30.0%, Phase III 58.0%, NDA/BLA 91.0%, Overall LoA 4.6%
  • Infectious Disease: Preclinical 7.0%, Phase I 52.0%, Phase II 36.0%, Phase III 62.0%, NDA/BLA 92.0%, Overall LoA 7.4%
  • Cardiovascular: Preclinical 5.0%, Phase I 48.0%, Phase II 28.0%, Phase III 55.0%, NDA/BLA 90.0%, Overall LoA 3.3%
  • Metabolic: Preclinical 6.0%, Phase I 50.0%, Phase II 32.0%, Phase III 58.0%, NDA/BLA 91.0%, Overall LoA 5.1%
  • Respiratory: Preclinical 5.0%, Phase I 47.0%, Phase II 26.0%, Phase III 54.0%, NDA/BLA 90.0%, Overall LoA 2.9%
  • Dermatology: Preclinical 6.0%, Phase I 50.0%, Phase II 34.0%, Phase III 60.0%, NDA/BLA 91.0%, Overall LoA 5.6%
  • Ophthalmology: Preclinical 6.0%, Phase I 50.0%, Phase II 30.0%, Phase III 58.0%, NDA/BLA 91.0%, Overall LoA 4.7%

Source: BIO/QLS/Informa 2021; Wong et al. 2019.

2. Multiplier adjustments

Several evidence-based factors are known to shift clinical success probabilities relative to the population base rate. PhaseFolio applies these multipliers via a log-odds (logit) transformation — the mathematically correct method when a multiplier is a true odds ratio. The cited sources, however, report effect sizes in different forms: relative success ratios (Minikel 2024), phase success-rate comparisons (Parker 2015), relative approval rates (Mullard 2016), and a cumulative pipeline advantage (Tufts NEWDIGS 2023).

The engine currently treats all of these through the OR-style logit path as a deliberately conservative approximation — this under-credits favorable modifiers at higher baselines, never saturates to 1.0, and avoids stacked-modifier overshoot. Per-modifier estimand declarations (_source_estimand and _applied_as) are recorded in the machine-readable source and explained in the model card. Each multiplier is applied only to the clinical phases where the underlying evidence was measured.

Table 2 — Evidence-based multipliers (source estimand separated from applied path; favorable multipliers >1 increase PoS; unfavorable <1 decrease):

  • Genetic Validation: 2.6× (RR estimand, OR-applied), Phase II/III, Minikel et al. 2024 Nature
  • Companion Diagnostic: 2.0× (RR, OR-applied), Phase II/III, Parker et al. 2015 ASCO
  • Orphan Designation: 1.5× (RR, OR-applied), Phase II/III, Mullard 2016 Nat. Rev. Drug Disc.
  • Biomarker Enrichment: 1.5× (RR, OR-applied), Phase II/III, Parker et al. 2015; BIO 2021
  • First-in-Class: 0.85× (RR, OR-applied), Phase II/III, BIO/QLS 2021
  • CAR-T / TCR Therapy: 1.73× per stage (RR cumulative 3×, OR-applied), Phase I/II, Tufts NEWDIGS 2023
  • Gene Therapy (Orphan): 1.41× per stage (RR cumulative 2×, OR-applied), Phase I/II, Tufts NEWDIGS 2023

CAR-T and gene-therapy-orphan per-stage values are sqrt(cumulative) splits of the source's whole-pipeline advantage.

A critical design decision: preclinical PoS is never adjusted by any multiplier. Preclinical attrition is dominated by toxicology, pharmacokinetics, and formulation failures (Sun et al. 2025) — factors orthogonal to the clinical efficacy signals that these multipliers capture. Similarly, NDA/BLA approval rates reflect regulatory filing quality rather than drug-specific clinical attributes, and are therefore held constant.

3. Logistic transformation method

Applying odds ratios to bounded probabilities requires care. Naive multiplication (PoS × OR, capped at 1.0) produces mathematically unsound results: a drug with 50% base PoS and a 2.6× genetic-validation multiplier would yield 130%, capped to 100% — falsely claiming certainty. With multiple favorable multipliers stacking, this problem cascades rapidly.

PhaseFolio instead applies multipliers in log-odds (logit) space — the standard biostatistical transformation for adjusting bounded probabilities by a multiplicative factor. The three-step transformation:

Equation 1 — Logistic odds-ratio adjustment. Step 1: odds = PoS_base / (1 − PoS_base). Step 2: odds_adj = odds × OR. Step 3: PoS_adj = odds_adj / (1 + odds_adj).

This approach has three desirable mathematical properties:

  1. Bounded output. The result is always in (0, 1) — it can never reach 0% or 100%, regardless of how many multipliers are stacked.
  2. Diminishing returns. A 2.6× OR applied to a 24% base PoS yields 45.1% (+21.1pp). Applied to a 70% base, it yields 85.8% (+15.8pp). The higher the base, the harder it is to push higher — matching clinical reality.
  3. Composability. Multiple ORs applied sequentially produce the same result regardless of order, because multiplication in log-odds space is commutative.

4. Worked example: PoS derivation

Consider a rare disease small molecule with genetic validation and orphan designation. We derive the Phase II PoS step-by-step.

Base rate (from benchmark matrix): Phase II PoS = 38.0%.

Apply genetic validation (factor 2.6, applied as OR): odds = 0.38 / (1 − 0.38) = 0.613; odds × 2.6 = 1.594; PoS = 1.594 / (1 + 1.594) = 61.5%. Factor source: Minikel et al. 2024 (RR); applied as OR.

Apply orphan designation (factor 1.5, applied as OR): odds = 0.615 / (1 − 0.615) = 1.597; odds × 1.5 = 2.396; PoS = 2.396 / (1 + 2.396) = 70.6%. Factor source: Mullard 2016 (RR); applied as OR.

Adjusted Phase II PoS: 70.6%.

Note: naive multiplication would yield min(1.0, 0.38 × 2.6 × 1.5) = 100% — clearly incorrect. The logistic method produces 70.6%, reflecting appropriate diminishing returns.

Key facts

Benchmark matrix size11 indications × 8 modalities × 3 biomarker = 264 cells
Source N (stage transitions)12,728+ (BIO/QLS/Informa 2021)
Multiplier count7 evidence-based factors
Transformation methodLog-odds (logit) — bounded, diminishing-returns, commutative
Preclinical adjustmentNever adjusted (orthogonal failure modes)

References

01Thomas, D.W., Burns, J., Audette, J., Carroll, A., Dow-Hygelund, C., & Hay, M. (2021). Clinical Development Success Rates and Contributing Factors 2011–2020. BIO, QLS Advisors, Informa Pharma Intelligence.

02Wong, C.H., Siah, K.W., & Lo, A.W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273–286.

03Citeline (2024). Pharma Intelligence Global Clinical Trials Database. Modality-specific pipeline data used to calibrate transition rates for bispecifics, ADCs, cell therapy, and gene therapy.

04Minikel, E.V., Painter, J.L., Dong, C.C., & Nelson, M.R. (2024). Refining the impact of genetic evidence on clinical success. Nature, 629, 624–629.

05Sun, J., Wei, Q., & Zhou, Y. (2025). Dynamic success rates of drug clinical trials. Nature Communications, 16, 1629.

06Mullard, A. (2016). Parsing clinical success rates. Nature Reviews Drug Discovery, 15, 447.

07Parker, J.L., Zhang, Z.Y., & Buckstein, R. (2015). Clinical trial risk in hematology and oncology: the effect of biomarker use. ASCO Annual Meeting Abstracts.

08Tufts Center for the Study of Drug Development / NEWDIGS (2023). Cell and Gene Therapy Success Rates.

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