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). Since model 3.0.0 (Methodology M4), every Phase I to NDA/BLA rate in all 264 cells is derived from named rows of BIO/QLS/Informa 2021, which analysed 12,728 phase transitions from 2011 to 2020 (Section 1.2). BIO reports no preclinical stage, so the preclinical rates are PhaseFolio assumptions, kept at their earlier values and labelled as such. Before M4 the matrix was a PhaseFolio composite: the small-molecule rows were informed by BIO 2021, the other modality rows were PhaseFolio adjustments, and 125 of the 264 cells were missing and fell back to the area's "other" row.

1.2 How each cell is derived

BIO 2021 reports success rates by disease area, by modality and by biomarker use, but never crossed. Each cell combines them in log odds: it starts from the area row and adds how far the modality row and the biomarker row sit from the all-indications row, stage by stage.

• Area rows. BIO 2021 Figures 2, 7 and 8b. Hematologic oncology uses the Figure 7 hematologic row. BIO has no dermatology group, so dermatology uses its Other row, labelled as a proxy.
• Modality rows. Figure 10b. Bispecifics use the monoclonal antibody row, labelled as a proxy. Cell therapy and other take no modality shift. Rows are smoothed as (k + 0.5) / (n + 1), so a 100% rate on a handful of transitions does not become certainty.
• Biomarker rows. Figure 11. No biomarker strategy uses the No biomarkers row; companion diagnostic uses the Preselection biomarkers row; enrichment takes half the preselection shift, a PhaseFolio assumption because BIO has only one preselection category.

Every BIO row used reproduces its printed likelihood of approval, and a test holds the model table to this derivation, so the two cannot drift apart.

Table 1. Baseline stage-transition probabilities by therapeutic area (small molecule, no biomarker strategy; dataset 2026-09-27). These are the values the model computes with. LoA from preclinical is the product of all five rates; LoA from Phase I is the product of the last four, which is the measure BIO 2021 reports. The BIO 2021 value is the area row each cell is derived from, across all modalities; the difference is the small-molecule and no-biomarker shift. Source: derived from BIO/QLS/Informa 2021 (Thomas et al.), Figures 2, 7, 8b, 10b and 11 (Section 1.2); preclinical rates are PhaseFolio assumptions. BIO 2021 has no dermatology group, so dermatology uses its Other group, which includes dermatology; immunology uses its Autoimmune group. Any change to the model's values ships as a new model major version.

A few published factors shift clinical success probabilities relative to the base rate. PhaseFolio applies them through a log-odds (logit) transformation, the usual method when a multiplier is an odds ratio. The cited sources, however, report effect sizes in different forms: relative success ratios (Minikel 2024²; Schwaederle 2016⁸), relative approval rates (Mullard 2016)⁴, and a published approval rate that the orphan gene therapy modifier is calibrated to (Phares, Phillip and Trusheim 2025)⁶. The biomarker-strategy effect is no longer a multiplier: since model 3.0.0 it is built into each matrix cell (Section 1.2).

The model 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. Multipliers, with the source estimand (what the literature reports) separated from the applied path (how the model treats it). Multipliers above 1 increase PoS; below 1 decrease it. The Basis column says where each value comes from: a named published source, or a PhaseFolio assumption where no published figure exists. Genetic validation, orphan designation and first-in-class apply only when a scenario is marked with them. ‡ The orphan gene therapy value is the per-stage odds ratio that takes the rare-disease gene therapy cell (no biomarker strategy) from 16.9% to the 18.5% likelihood of approval Phares, Phillip and Trusheim report for rare-disease gene therapies. That figure already reflects orphan status, so a gene therapy with orphan designation gets this modifier instead of Orphan Designation, not both. There is no CAR-T modifier: the same paper reports hematologic CAR-T and TCR therapies at 7.6% against 7.5% for comparable drugs, and the derived hematologic cell therapy cell is already 7.25%. Before M4 both modifiers were square-root splits of an older whole-pipeline advantage (NEWDIGS at Tufts Medical Center, 2023)⁷ applied beyond the scope that analysis measured. † Biomarker quality applies only to oncology solid tumor, and only after a program's classification is reviewed; see Section 6.

A critical design decision: preclinical PoS is never adjusted by any multiplier. Preclinical attrition is dominated by toxicology, pharmacokinetics, and formulation failures [Zhou et al. 2025]³, 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 smaller the change.
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.

The benchmark figures are reference data. They describe how often programs in a therapeutic area, modality and biomarker strategy moved from one stage to the next in the published cohort (BIO/QLS/Informa 2021 covers phase transitions from 2011 to 2020)¹. They are not a prediction for any specific asset, and PhaseFolio does not adjust them with research of its own.

• They prefill the asset modeler. Each stage's PoS starts at the matching cell, with any multiplier the scenario is marked for. Every value can be changed, and the model computes with the values you keep.
• Your changes are recorded. A saved scenario records which stage probabilities are the benchmark default and which you set, and signed exports state it.
• Multipliers are optional. Genetic validation, orphan designation and first-in-class apply only when the scenario is marked with them. Biomarker quality applies only when a reviewed classification exists (Section 6).
• They change when the sources change. Each update is a new methodology version with an entry in the version history. Exports keep the version they were computed with.

Biomarker quality refines the biomarker-strategy axis of Table 1 by the kind of biomarker a program is built on, not only whether one is present. It applies to oncology solid tumor programs at Phase II and Phase III; no other indication or stage is adjusted.

Source. The 1.35× and 0.85× values are the relative response and success ratios reported by Schwaederle et al. 2016⁸, a meta-analysis of 346 phase II precision-medicine studies (13,203 patients), applied through the same log-odds path as the other multipliers.

Review before use. A program's classification is extracted from its public sources and has no effect until it is reviewed and approved. Unreviewed classifications (reviewed_at IS NULL) leave PoS unchanged. The review date is recorded on signed exports.

Some attributes are shown as flags, each with its source. They give context and do not change PoS or the rNPV.

Phase 1 objective response rate. For oncology solid tumor programs with Phase 1 efficacy data, the response rate is recorded with its source and placed in a high, mid or low band for its modality.

Table 4. The reported rate is adjusted toward blinded-independent-central-review values (Zhang et al. 2017¹¹ reports central assessment slightly lower than local, with high concordance) and discounted for early-phase optimism (Vreman et al. 2019¹⁰) before banding.

Table 5. Flags appear in the dossier and on signed exports, so a reader can see which fired. The rNPV is the same whether they are present or absent.

PhaseFolio maintains this section. The baselines follow their published sources: when a source is revised, or a better-matched source is published, we update the figures in a new methodology version and say what changed. Drug-specific attributes are extracted with the help of an AI model and reviewed before they affect a scenario.

M2 (2026-09-27) is a correction with no model value changed. Table 1 now shows the baseline table the model computes with, beside the nearest BIO 2021 group. For five therapeutic areas, exports stamped before M2 were computed on the model table, not on the Table 1 published at the time. M2 also removes an unsupported Citeline attribution for the modality rows (PhaseFolio has never licensed or used Citeline data), corrects the BIO 2021 author list and links the NEWDIGS analysis itself.

M4 (2026-09-28) ships model 3.0.0 and changes model values. Every Phase I to NDA/BLA rate is now derived from named BIO 2021 rows (Section 1.2), which fills the 125 cells that previously fell back to the area's “other” row and corrects hematologic oncology, whose earlier rows were well above BIO's hematologic cancers group. The biomarker strategy moves into the matrix, the CAR-T modifier is removed, orphan gene therapy gets one calibrated modifier instead of two stacked, and first-in-class is relabelled a PhaseFolio assumption.

M5 (2026-09-28) restates this section as reference data and changes no value. It removes the internal cohort analyses, the multiplier governance discussion and the research roadmap that earlier versions published. M4 remains readable, and exports stamped with any earlier version still verify at /verify.