Complete the asset profile in the Valuation tab to generate the model →
等待输入Awaiting input
rNPV
—
stage-gated PoS
Risk-adjusted deal value
—
upfront + probability-adjusted milestones
Upfront
—
cash at signing
估值分析Valuation
Patient forecast
Disease competition
参考交易Comps 0
方法学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.
同类可比交易Comparable deals
自精选录入的 — 笔跨境授权交易中按相似度排序 · 优先同适应证 / 同药物类型 / 同阶段 · 见 方法学Ranked by similarity from — hand-curated cross-border deals · same indication / modality / stage prioritized · see methodology
请先完成估值测算Run the valuation first
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 · 创新药 rNPV 模型(参数路径)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 · 阶段累计上市概率PoS · cumulative probability to approval
preclin 5% · Ph1 10% · Ph2 17% · Ph3 52% · NDA 88% · approved 100%
默认 WACC 11% · NetMargin 30% · 上市后 10 年专利期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 · 可比交易回归(数据路径)2 · Comparable-deal regression (data path)
// 在 DEALS_DB 中对每笔有披露金额的交易计算相似度权重// 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)
// 仅保留 weight > 0.20 的样本; 若 N < 3, 跳过本路径// Require direct/adjacent indication plus modality or stage support; abort if N < 3
// 加权重采样后取分位数// 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 · 融合(blended) — 把两条路径合起来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
退化情况: 当 DEALS_DB 中匹配样本 < 3 时, 输出 = 100% rNPV(并在结果区显示退化提示)Fallback: when matched samples in DEALS_DB < 3, output = 100% rNPV (a fallback notice is shown in the result panel)
4 · 类似药利润分成模型4 · Biosimilar profit-share model
NPV_licensor_share = PoS × Σ_t [OrigPeak × (1 - Discount) × ShareRamp(t) × GM × ProfitShare]
÷ (1 + BS_WACC)^t
默认值:Defaults: share 35-45% · discount vs originator 25-35% · GM 55% · BS_WACC 9%
类似药 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 类似药估值仅采用参数路径(不进行 comp-blended), 因为 DEALS_DB 中类似药交易样本极少。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 · 交易结构拆分5 · Deal structure decomposition
Upfront : Milestone : Royalty ≈ 8% : 40% : 52% (新药全球均值)(global new drug average)
按阶段首付占比 (参数模型):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 由参数+回归融合给出, P10/P90 由模型敏感性 ∪ 可比分位数 给出。P50 from blended path; P10/P90 = union of model sensitivity and comp quantiles.
数据来源 & 已知局限Data sources & known limitations
数据来源Data sources
- PoS 默认值: BIO/QLS/Informa《Clinical Development Success Rates 2011-2020》行业基准。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.
- 可比交易数据库 (DEALS_DB): 公司新闻稿、SEC 8-K/20-F 文件、BioPharma Dive / FiercePharma / Endpoints News 报道,人工录入,聚焦中国-海外跨境授权交易。当前 — 笔。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.
- 买方财务: FY25 年报(MNC)或最近季报年化(中等市值);现金 = 现金+短期投资。Buyer financials: FY25 annual reports (MNCs) or latest quarterly annualized (mid-caps); cash = cash + short-term investments.
已知局限Known limitations
- 样本量小: 仅 — 笔可比交易,统计置信度有限,P10/P90 区间会偏宽。Small sample: only — deals — statistical confidence is limited; P10/P90 bands run wide.
- 选择偏差: 数据库偏向中国 license-out 交易,纯海外 in-licensing 覆盖较少。Selection bias: public disclosures overrepresent large headline deals; regional transactions and undisclosed economics remain underrepresented.
- 未披露金额未纳入: 公开仅披露"undisclosed terms"的交易未参与回归。Undisclosed deals excluded: transactions with "undisclosed terms" are not included in regression.
- biobucks 膨胀: "总额"通常含发生概率较低的远期里程碑(销售里程碑常被高估)。Biobucks inflation: reported "total deal value" often inflates distant sales milestones with low realization probability.
- 类似药未做回归: 类似药交易在 DEALS_DB 中样本极少,v1.0 仅用参数路径。No biosim regression: too few biosim deals in DEALS_DB; v1.0 uses parametric only.
- 买方近期活跃度更新: 2025-Q4 起的并购/授权动作需要社区帮助持续刷新。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.
本工具仅用于 BD 评估参考,不构成投资建议。所有源代码、参数默认值、公式实现均在 GitHub
index.html 公开可见;欢迎挑战、修改、改进。
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.