Valuation Patient model Competition Deals Methodology
v1.7.0 · — deals
· The engine Begin · The engine

，。 Enter one asset, receive a deal blueprint.

——、、。。 Build the asset profile in the Valuation tab — economics, comparable deals and the patient model update from the same assumptions. Every figure drills down to its parameters.
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Complete the asset profile in the Valuation tab to generate the model →

Awaiting input

rNPV
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stage-gated PoS
Risk-adjusted deal value
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upfront + probability-adjusted milestones
Upfront
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cash at signing
Valuation
Patient forecast
Disease competition
Comps 0
Methodology
Complete the asset profile above and click “Match & value”.

Patient-based market forecast

Start with sourced epidemiology, apply an editable patient funnel, then transfer peak territory revenue into the valuation.
Search rare and common diseases. The lookup uses a source ladder: Orphadata for rare diseases; curated registries and CDC PLACES for supported US chronic diseases; then guarded Europe PMC extraction for prevalence, incidence, or an explicit patient pool.
Finds disease-specific source candidates for every funnel step; numerical assumptions remain reviewable.
Commercial assumptions
Keep the source URL with the model. Automatic results include the source, metric, geography, and validation status.
Includes treatment uptake, payer access and competitive share—these are not deducted again.
Public product-price lookup
Enter an approved product in Asset name, then search official US sources. openFDA confirms the indication; CMS NADAC supplies outpatient acquisition cost per unit where available. Annual price requires the indication-specific regimen.
Patient funnel and revenue forecast
Evidence and rationale by funnel step
Sourced figure or published benchmarkPlaceholder, proxy, edited, or unverified figure
Peak revenue is the highest base-case annual revenue in the selected forecast horizon.
Definitions matter: prevalence and incidence are not interchangeable. Teal figures are linked to a sourced estimate or published benchmark; red figures are placeholders, proxies, edited values, or still require validation. Drug price is product-, strength-, route-, geography-, and regimen-specific—not disease-only. Public CMS acquisition-cost or ASP data may support an estimate, but manufacturer net price is usually confidential.

Disease competition

US label matches from openFDA and active interventional studies from ClinicalTrials.gov. Label matches require confirmation and are not a substitute for regulatory diligence.

Search for a disease in Patient forecast to load therapies and active trials.

Comparable deals

— · / / · Ranked by similarity from — hand-curated cross-border deals · same indication / modality / stage prioritized · see methodology
Run the valuation first
Data transparency

——。 This section documents every step of the model — and what it cannot do.

v1.0.2 **** DEALS_DB — （ 2024-11 2026-02 ，）。 50 （、），——** `unknown`， (modality) **。 rNPV 50/50 ； <3 rNPV。 → The v1.7.0 engine contains — global BD&L transactions. Curated 2026 US, European, ex-US, ex-China and RoW licenses from the supplied BioBucks tracker supplement the existing public dataset. Some bulk-ingested rows retain an undisclosed stage or inferred modality, so they may appear in the reference table but only strict single-asset matches with disclosed USD economics and upfront consideration enter the comp calibration. Submit data →

Patient forecast · epidemiology-to-revenue model

epidemiology_patients = population × rate_per_100k ÷ 100,000 diagnosed_patients = epidemiology_patients × diagnosis_rate eligible_patients = diagnosed_patients × eligibility_rate product_patients = eligible_patients × product_market_share annual_revenue = product_patients × annual_net_price annual_net_price = annual_list_or_WAC_price × (1 - gross_to_net_discount) Product market share is defined as the share of eligible patients who ultimately receive the product. It already incorporates treatment uptake, payer access and competition, avoiding double-counting through separate treated and accessible reductions. Automatic epidemiology lookup: Orphadata for rare diseases; NLM terminology normalization for common diseases; curated cited estimates where available; CDC PLACES modeled adult prevalence for supported US chronic conditions; automated Europe PMC population-epidemiology retrieval and numeric extraction for other common diseases; and World Bank population. No single free public API covers every disease and geography. The engine therefore follows that source ladder and leaves a value blank when no defensible population estimate passes the checks; it does not turn terminology matches or unrelated study rates into patient counts. For literature-derived estimates, the engine ranks disease focus, population design, geography and metric context, normalizes percentages and rates to patients per 100,000, and labels the result as review-required. It does not silently treat a study cohort, confidence interval or unrelated outcome rate as population epidemiology. Each conversion rate, price basis, product-share assumption, growth, forecast horizon and share ramp remains editable; the source and rationale are retained and edited values are flagged. Competition searches use openFDA label text and active interventional studies from ClinicalTrials.gov; Excel exports retain the assumptions, formulas, sources and time series.

1 · rNPV ()1 · New drug rNPV (parametric path)

rNPV_licensor = PoS × Σ_t [Peak × Ramp(t) × ModPremium × RoyaltyRate] ÷ (1 + WACC)^t rNPV_asset = PoS × Σ_t [Peak × Ramp(t) × ModPremium × NetMargin] ÷ (1 + WACC)^t PoS · PoS · cumulative probability to approval preclin 5% · Ph1 10% · Ph2 17% · Ph3 52% · NDA 88% · approved 100% WACC 11% · NetMargin 30% · 10 Default WACC 11% · NetMargin 30% · 10y post-approval patent runway Ramp(t) = min(1, year_after_launch ÷ sourced_TA_time_to_peak) Time-to-peak defaults use the US or worldwide therapeutic-area medians in Alvarez, Saucy & Saacks (2026). The paper supports the duration, while the straight-line annual interpolation is a transparent editable assumption. ModPremium: sm 1.00 · mab 1.05 · bsab 1.25 · adc 1.40 · cart 1.50 · gt 1.55 · pep 1.10 · olig 1.20 · rc 1.45 Order-of-entry is not applied as a second multiplier to user-entered peak sales. It sets the patient model's peak-share proxy: 1st 40% · 2nd 33% · 3rd 19% · 4th 13% · 5th 8% · 6th+ 2%.

2 · ()2 · Comparable-deal regression (data path)

// DEALS_DB // For each deal with disclosed economics, compute similarity weight weight(d) = 0.50 if d.indication == p.indication else 0.25 (adjacent) | 0.10 (same TA) + 0.30 if d.modality == p.modality else 0.10 × ratio(ModPremium) + 0.20 if d.stage == p.stage else 0.10 (±1) | 0.04 (±2) // weight > 0.20 ; N < 3, // Require direct/adjacent indication plus modality or stage support; abort if N < 3 // // Exclude acquisitions, portfolios and platforms; probability-adjust milestones expected_total = upfront + (headline_total - upfront) × milestone_probability total_implied = median(weighted_samples.expected_total) × stage_correction × mod_correction × terr_factor upfront_implied= median(weighted_samples.upfront) × (same correction) P10 / P90 = 20%/80% quantiles of resampled distribution stage_correction = StageMult(p.stage) / weighted_avg(StageMult(d.stage)) mod_correction = ModPremium(p.mod) / weighted_avg(ModPremium(d.mod)) terr_factor = 1.0 (global) / 0.90 (ex-China) / 0.75 (ex-US) / regional scaling StageMult: preclin 0.35 · Ph1 0.7 · Ph2 1.0 · Ph3 1.6 · NDA 2.4 · approved 3.0

3 · (blended) — 3 · Evidence calibration

risk_adjusted_value = 0.75 × rNPV_total + 0.25 × expected_comp_total // when N ≥ 3 upfront_blended = 0.75 × rNPV_upfront + 0.25 × upfront_implied P10 / P90 = the same 75/25 blend of scenario bands comp guardrail = 0.5× to 2.0× the asset rNPV anchor : DEALS_DB < 3 , = 100% rNPV()Fallback: when matched samples in DEALS_DB < 3, output = 100% rNPV (a fallback notice is shown in the result panel)

4 · 4 · Biosimilar profit-share model

NPV_licensor_share = PoS × Σ_t [OrigPeak × (1 - Discount) × ShareRamp(t) × GM × ProfitShare] ÷ (1 + BS_WACC)^t :Defaults: share 35-45% · discount vs originator 25-35% · GM 55% · BS_WACC 9% PoS:Biosim PoS: preclin 15% · Ph1 25% · Ph2 45% · Ph3 70% · NDA 85% · approved 100% ShareRamp: 5/20/40/55/65/70 (% market share vs originator by year) v1.0 ( comp-blended), DEALS_DB 。Biosim valuation uses the parametric path only (no comp-blending) in v1.0 — DEALS_DB carries too few biosim transactions to support the regression.

5 · 5 · Deal structure decomposition

Upfront : Milestone : Royalty ≈ 8% : 40% : 52% ()(global new drug average) ():Upfront % by stage (parametric): preclin 4% · Ph1 7% · Ph2 11% · Ph3 18% · NDA 28% · approved 42% royalty rate: Ph1 8-11% · Ph2 10-13% · Ph3 12-16% · Mkt 14-20% P50 +, P10/P90 ∪ 。P50 from blended path; P10/P90 = union of model sensitivity and comp quantiles.

& Data sources & known limitations

Data sources

  • PoS : BIO/QLS/Informa《Clinical Development Success Rates 2011-2020》。PoS defaults: BIO/QLS/Informa Clinical Development Success Rates 2011-2020 industry benchmark.
  • Order-of-entry market share: Cha & Yu, McKinsey, Pharma's first-to-market advantage. Average sales share in year 10 after the first class launch is used as an editable peak-share proxy.
  • Time to peak sales: Alvarez, Saucy & Saacks, Nature Reviews Drug Discovery (2026). US/worldwide therapeutic-area medians drive the default ramp duration.
  • Common-disease epidemiology: disease names use the NLM Conditions API. Curated examples include rheumatoid arthritis from the GBD 2021 analysis and WHO, and bladder cancer from IARC GLOBOCAN and SEER. Supported US adult chronic diseases use the CDC PLACES 2025 release; other normalized queries run through the Europe PMC REST API for disease-specific population epidemiology.
  • (DEALS_DB): 、SEC 8-K/20-F 、BioPharma Dive / FiercePharma / Endpoints News ,,-。 — 。Comp database (DEALS_DB): public company disclosures and specialist BD&L reporting, supplemented with the supplied BioBucks 2026 tracker. Coverage spans US, Europe, Asia and RoW regional and global licenses. Currently — deals.
  • : FY25 (MNC)(); = +。Buyer financials: FY25 annual reports (MNCs) or latest quarterly annualized (mid-caps); cash = cash + short-term investments.

Known limitations

  • : — ,,P10/P90 。Small sample: only — deals — statistical confidence is limited; P10/P90 bands run wide.
  • : license-out , in-licensing 。Selection bias: public disclosures overrepresent large headline deals; regional transactions and undisclosed economics remain underrepresented.
  • : "undisclosed terms"。Undisclosed deals excluded: transactions with "undisclosed terms" are not included in regression.
  • biobucks : ""()。Biobucks inflation: reported "total deal value" often inflates distant sales milestones with low realization probability.
  • : DEALS_DB ,v1.0 。No biosim regression: too few biosim deals in DEALS_DB; v1.0 uses parametric only.
  • : 2025-Q4 /。Buyer recency: deal activity since 2025-Q4 needs ongoing community refresh.
  • Automated epidemiology: there is no universal, free, unauthenticated disease-burden API. CDC PLACES covers a defined set of US adult measures, Orphadata covers rare diseases, and other literature-derived values are source-linked, geography-ranked and marked review-required. Publication abstracts can omit population definitions or report heterogeneous estimates, so users should confirm the full paper before external use.
BD ,。、、 GitHub index.html ;、、。 For BD evaluation reference only; not investment advice. All source code, parameter defaults and formula implementations are visible in index.html on GitHub. Challenge it, fork it, improve it.
© 2026 BD·Match PRO v1.7.0 ,。Built on public deal data and industry-standard valuation frameworks. Actual deal terms subject to final executed agreement.