Probability of Success Calibration

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.

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 Likelihood of Approval (LoA) is the product of all five transition rates. Source: BIO/QLS/Informa 2021; Wong et al. 2019.

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, with the source estimand (what the literature reports) separated from the applied path (how the engine treats it). Favorable multipliers (>1) increase PoS; unfavorable (<1) decrease it. 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.

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:

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.

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

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.