TheraRadar

Pharma Pipeline Health

MC SIMULATION
Data updated: May 20, 2026

Expected drug approvals across 200 industry sponsors, computed via Monte Carlo simulation over 3,492 drug-indication programs using Wong probability-of-success rates.

What "pipeline health" measures

Pharma pipeline health is a forward-looking measure of how many FDA drug approvals a biopharma company is likely to produce from its current clinical-trial portfolio. We count distinct (drug × indication) programs at their lead Phase 1, Phase 2, or Phase 3 trial — about 3,492 programs across 200 western FDA-track sponsors — and run a 10,000-iteration Monte Carlo simulation using industry-standard Wong probability-of-success rates (Ph1 10%, Ph2 30%, Ph3 60%). The output is reported as median [80% CI] expected approvals per company over the program lifetime (~5-10 years).

Use this league to compare biopharma pipeline depth across competitors, identify companies with concentrated late-stage assets, or stress-test approval forecasts under different success-rate assumptions via the configurator below. Already-acquired companies (Shire, Allergan, Actelion, Karuna etc.) are excluded. Full methodology ↓

What this is not: expected approvals are unweighted by commercial value. One niche rare-disease approval ($50M/yr peak) counts the same as one oncology blockbuster ($20B/yr peak). A company with 5 high-value oncology Ph3 programs and a company with 5 small-population rare-disease Ph3 programs both score "5 expected approvals" — but the revenue outcomes differ by 100×. Cross-reference Compare's revenue and patent-cliff sections for the value dimension.

3,492
Active programs
Drug × indication × phase
1,331
Expected approvals
Industry-wide, all phases
99
Leader expected
AstraZeneca
200
Sponsors ranked
Top by MC expected
Probability of success:
Wong et al. 2019 global rates

Big Pharma (22)

Tier 1

Companies with revenue data tracked in Compare — top 5 free, rest in Pro.

Company Programs (P1/P2/P3) Expected approvals
(median [80% CI])
Top indications Recent terminations Compare
Company Programs (P1/P2/P3) Expected approvals
(median [80% CI])
Top indications Recent terminations Compare

Big Pharma — full league

17 more sponsors with full MC distribution.

Unlock with Pro

Mid-cap Biotech (29)

Tier 2

Companies with approved drugs and at least one Phase 3 program.

Company Programs (P1/P2/P3) Expected approvals
(median [80% CI])
Top indications Recent terminations
Company Programs (P1/P2/P3) Expected approvals
(median [80% CI])
Top indications Recent terminations

Mid-cap Biotech — full league

24 more sponsors.

Unlock with Pro

Clinical-stage with Phase 3 (99)

Tier 3

No approved drugs yet, but at least one Phase 3 program. Highest M&A target density.

Company Programs (P1/P2/P3) Expected approvals
(median [80% CI])
Top indications Recent terminations
Company Programs (P1/P2/P3) Expected approvals
(median [80% CI])
Top indications Recent terminations

Clinical-stage Ph3+ — full league

94 more sponsors.

Unlock with Pro

Early-stage hot bets (50)

Tier 4 · sorted by heat

Phase 1/2-only sponsors operating in therapeutic areas where big pharma has been actively acquiring. Wong PoS at this stage isn't precise enough to rank — heat captures "in-demand" instead.

Company Programs (P1/P2) Hot TA match Recent acquirers in space Top indications
Company Programs (P1/P2) Hot TA match Recent acquirers in space Top indications

Early-stage hot bets — full list

40 more sponsors.

Unlock with Pro

How the math works

Programs at lead active phase. For each company we count (drug × indication) programs at their highest currently-active phase — that's Wong's denominator. A drug running 12 active Ph3 trials in different indications counts as 12 programs (one per indication), not 12 trials of one program.

Monte Carlo simulation. We run 10,000 iterations per company with industry-average probability-of-success rates (Phase 1 → 10%, Phase 2 → 30%, Phase 3 → 60% from Wong). Each iteration flips a biased coin per program at its current phase; the simulation produces a distribution of total approvals over the program lifetime (~5-10 years).

Global rates only (not TA-specific). The same 10/30/60 rates apply to every program regardless of therapeutic area. Real success rates differ substantially — oncology is the worst, hematology and infectious disease the best. TA-specific rate weighting is on the roadmap; for now treat the league as TA-agnostic.

Wong TA-specific PoS reference table (not currently applied)
Therapeutic area Phase 1 → approval Phase 2 → approval Phase 3 → approval
Hematology26.1%56.5%73.5%
Allergy23.4%51.5%75.0%
Endocrine17.7%38.3%65.0%
Ophthalmology13.6%27.8%65.0%
Respiratory13.2%36.4%70.6%
Infectious disease13.2%41.2%70.9%
Cardiovascular14.4%31.0%65.5%
Neurology14.2%30.5%51.9%
Gastroenterology11.0%35.0%65.0%
Oncology5.1%24.6%35.5%
Global average (used here)10.0%30.0%60.0%

Source: Wong, Siah, Lo. Estimation of clinical trial success rates and related parameters. The oncology PoS is dramatically lower than other areas — a heavy-oncology pipeline (Roche, Merck) is meaningfully riskier than the global rates suggest, while a metabolic / endocrine company (Lilly's GLP-1 portfolio) sees higher real success rates.

Display: median [80% CI]. "9 [5–15]" means median 9 expected approvals across 10K MC runs, with 80% confidence the count lands between 5 and 15. The range matters more than the point estimate — small-pipeline companies have wide tails.

Recent terminations (last 12 months). Counts trials with status TERMINATED / WITHDRAWN / SUSPENDED whose primaryCompletionDate falls in the last 12 months. A noisy quality signal — termination can mean "drug failed" or "sponsor deprioritized" or "slow enrollment" — but elevated counts (≥5/yr) suggest execution issues worth investigating. Not the same as PoS.

Scope: FDA-track sponsors only. League covers western pharma developing for FDA approval. Chinese, Korean, Russian and other non-FDA-track sponsors are filtered out — outside the scope of this dashboard. Already-acquired big pharma (Shire, Allergan, Actelion, Loxo, Karuna etc.) are excluded too; their leftover trials persist in the data but the entities don't exist as standalone league participants.

Caveat: programs treated as independent. Correlated outcomes (same drug across indications, shared TA shocks) are not modeled. Use as baseline; apply your own judgment about correlation. Adjust PoS rates at top of page to test sensitivity.

Data: 147,532 trials from ClinicalTrials.gov (Phase 1-4, 2008+). Industry sponsors only.

PoS source: Wong industry-average rates (Ph1 10% / Ph2 30% / Ph3 60% — global, not TA-specific in this build).