Introduction
The probability of success (PoS) at each clinical phase is the most important input in an rNPV model. It determines how much of the projected cash flow stream is "real" (probability-weighted) versus aspirational. Getting the PoS assumptions wrong by even 10 percentage points can swing a pipeline asset's value by 50-100%. Healthcare bankers use historical success rate data, adjusted for therapeutic area and program-specific factors, to set the probability weights in biotech valuations.
Overall Success Rates by Phase
The cumulative probability of a drug progressing from Phase I entry to FDA approval is approximately 10-14%, meaning roughly 9 out of 10 drugs that enter human testing ultimately fail.
| Phase Transition | Historical Success Rate | Cumulative PoS (from Phase I) |
|---|---|---|
| Phase I → Phase II | 60-65% | 60-65% |
| Phase II → Phase III | 30-35% | 20-22% |
| Phase III → NDA/BLA filing | 55-65% | 11-14% |
| NDA/BLA → Approval | 85-90% | 10-13% |
Success Rates by Therapeutic Area
Probability of success varies dramatically by therapeutic area, reflecting differences in disease biology, endpoint clarity, and regulatory standards.
| Therapeutic Area | Overall PoS (Phase I to Approval) | Key Factors |
|---|---|---|
| Hematology | 20-26% | Well-defined endpoints, biomarker-selected |
| Rare Disease/Orphan | 20-25% | Smaller trials, unmet need, FDA flexibility |
| Infectious Disease | 15-20% | Clear endpoints (viral load, cure rates) |
| Metabolic/Endocrine | 12-18% | Established biomarkers (HbA1c, LDL-C) |
| CNS/Neurology | 6-10% | Heterogeneous diseases, subjective endpoints |
| Oncology | 3-7% | High bar for efficacy, complex tumor biology |
| Immunology | 8-12% | Moderate, depends on mechanism specificity |
Program-Specific Adjustments
Within a therapeutic area, several factors can increase or decrease the probability of success relative to historical averages:
Biomarker Selection
- Biomarker-Selected Clinical Trial
A trial that enrolls only patients whose tumors (or disease) express a specific biomarker or genetic mutation that the drug targets. Biomarker selection enriches the trial population for patients most likely to respond, increasing the observed treatment effect and the probability of the trial succeeding. Programs using biomarker-based patient selection have approximately 1.5-2x higher probability of success than unselected programs in the same therapeutic area.
The impact of biomarker selection on PoS has been one of the most significant trends in drug development. In oncology, where overall PoS is only 3-7%, biomarker-selected programs can achieve PoS of 10-15%, nearly doubling the probability. This is one of the key drivers of the nichebuster strategy in pharma, where companies target smaller but better-defined patient populations.
Mechanism of Action Validation
If a drug targets a mechanism that has been validated by other approved drugs (e.g., a new PD-1 inhibitor when several are already approved), the probability of success is higher than for first-in-class mechanisms with no prior validation. The logic is straightforward: if the mechanism works for one drug, it is more likely to work for similar drugs. Conversely, if multiple drugs targeting the same mechanism have failed (e.g., amyloid-beta in Alzheimer's prior to aducanumab, or CETP inhibitors in cardiovascular disease), the probability is lower because the failures suggest the mechanism itself may be flawed.
Prior Clinical Data Quality
For assets entering later phases, the quality of earlier-phase data affects PoS. A Phase III program with robust Phase II data (large effect size, narrow confidence intervals, consistent across subgroups) has a higher Phase III PoS than one entering Phase III based on marginal Phase II data. Analysts adjust PoS upward for programs with strong Phase II signals and downward for programs advancing on borderline data, with adjustments typically ranging from plus or minus 10-15 percentage points relative to the therapeutic area baseline.
Endpoint Selection
The choice of clinical endpoint also affects PoS. Trials using surrogate endpoints (biomarkers that correlate with clinical benefit, such as tumor shrinkage rates in oncology or HbA1c in diabetes) tend to have higher success rates than trials using hard clinical endpoints (overall survival, major adverse cardiovascular events). This is partly because surrogate endpoints are more sensitive to treatment effects and require smaller patient populations and shorter trial durations. However, the FDA increasingly requires confirmatory trials with hard endpoints for full approval, meaning success on a surrogate endpoint may not guarantee ultimate regulatory success.
The next article covers pipeline SOTP and peak sales estimation, which brings together PoS data with market sizing to produce the aggregate biotech valuation.
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