Vose SoftwareModelChoice decision tree · Pharma and biotech
Gene Therapy Development Decision
Develop an in-vivo or an ex-vivo gene therapy, license it to big pharma, or shelve the programme?
From the model’s decision tree
Best first decision: Ex-vivo Cell Therapy
The expected value of each first option when every later decision is also made well (the tree rolled back).
| First decision | Expected value | |
|---|---|---|
| In-vivo AAV Gene Therapy | $61,786,500 | |
| Ex-vivo Cell Therapy | $187,875,000 | ✔ best |
| License to Big Pharma | $124,250,000 | |
| Shelve Program | $0 |
| Measure | Value |
|---|---|
| Expected value of the best strategy | $187,875,000 |
| Chance of a loss on the best strategy | 79.0% |
| Best outcome on the best strategy | $1,640,000,000 |
| Worst outcome on the best strategy | −$210,000,000 |
S-curve (cumulative probability) of Ex-vivo Cell Therapy from the saved simulation.
The decision
A mid-stage biotech company has discovered a promising gene therapy approach for a rare monogenic disease affecting approximately 5,000 patients worldwide. The therapy could provide a one-time functional cure, but development costs are substantial and manufacturing at scale remains a key challenge.
Objective
Maximize expected NPV. Gene therapies command extreme pricing ($1-3M per patient) but serve tiny patient populations, creating unique risk/reward.
The options
- Option 1: In-vivo AAV Gene Therapy ($80M to IND)
Develop an adeno-associated virus (AAV) vector to deliver the corrective gene directly in vivo. If preclinical data is strong, decide whether to self-fund Phase 1/2 or seek a partner. Manufacturing scale-up is a critical risk factor. Potential pricing: $1.5-3M per patient for a one-time cure.
- Option 2: Ex-vivo Cell Therapy ($60M to IND)
Harvest patient's own cells, correct them ex vivo using CRISPR/gene editing, and re-infuse. Lower vector risk but requires complex cell processing infrastructure. If preclinical succeeds, decide: self-fund or partner.
- Option 3: License to Big Pharma ($5M upfront)
Out-license the program to a large pharma company with gene therapy capabilities. Receive upfront payment plus development milestones and royalties. Lower risk but gives up most of the value.
- Option 4: Shelve Program
Preserve IP and wait for technology to mature. No further investment.
The decision tree
The tree has 4 decisions, 12 chance events and 33 outcomes. The best path is open: at every decision the best choice is marked, and the expected value beside each branch counts every cash flow from the start. Open any other branch to see what follows it.
DecisionChance event+$ cash flow on the branch
In-vivo AAV Gene Therapy −$80,000,000expected $61,786,500
Chance: AAV Preclinical Results30% Strong Efficacy + Safetyexpected $267,205,000
Decision: Fund AAV Phase 1/2?Self-Fund Phase 1/2 −$120,000,000expected $267,205,000 ✔ best
Chance: Manufacturing Scale-Up55% Scale-Up Succeedsexpected $453,100,000
Chance: Phase 1/2 Clinical Efficacy35% Durable Responseexpected $1,266,000,000
Chance: FDA Pathway- 45% Accelerated Approval +$2,000,000,000payoff $1,800,000,000
- 40% Standard Approval +$1,340,000,000payoff $1,140,000,000
- 15% Complete Response Letter +$200,000,000payoff $0
- 40% Partial Response +$350,000,000payoff $150,000,000
- 25% No Efficacypayoff −$200,000,000
30% Scale-Up Partial −$40,000,000expected $160,000,000
Chance: Delayed Clinical Results- 25% Durable Response +$1,200,000,000payoff $960,000,000
- 40% Partial Response +$250,000,000payoff $10,000,000
- 35% No Efficacypayoff −$240,000,000
- 15% Scale-Up Failspayoff −$200,000,000
Partner for Phase 1/2expected $252,500,000
Chance: Partnership Outcome- 40% Strong Partner + Milestones +$600,000,000payoff $520,000,000
- 35% Modest Deal +$250,000,000payoff $170,000,000
- 25% No Partner Interest +$20,000,000payoff −$60,000,000
35% Moderate Efficacyexpected $27,500,000
Decision: Moderate Efficacy - Next Step?Self-Fund Phase 1/2 −$120,000,000expected $27,500,000 ✔ best
Chance: Moderate: Clinical Outcome- 20% Surprising Efficacy +$800,000,000payoff $600,000,000
- 45% Marginal Benefit +$150,000,000payoff −$50,000,000
- 35% Trial Failurepayoff −$200,000,000
Seek Partnerexpected −$28,000,000
Chance: Moderate: Partnership- 30% Partner Found +$150,000,000payoff $70,000,000
- 70% No Interest +$10,000,000payoff −$70,000,000
- Abandon Program +$5,000,000payoff −$75,000,000
- 35% Preclinical Failurepayoff −$80,000,000
Ex-vivo Cell Therapy −$60,000,000expected $187,875,000 ✔ best
Chance: Ex-vivo Preclinical Results35% Strong Cell Correctionexpected $622,500,000
Decision: Fund Ex-vivo Phase 1/2?Self-Fund Phase 1/2 −$100,000,000expected $622,500,000 ✔ best
Chance: Ex-vivo Clinical Results- 25% Functional Cure +$1,800,000,000payoff $1,640,000,000
- 35% Significant Improvement +$900,000,000payoff $740,000,000
- 25% Minimal Benefit +$100,000,000payoff −$60,000,000
- 15% Safety Concern −$50,000,000payoff −$210,000,000
Partner for Phase 1/2expected $189,500,000
Chance: Ex-vivo Partnership- 35% Major Pharma Partner +$500,000,000payoff $440,000,000
- 35% Biotech Partner +$200,000,000payoff $140,000,000
- 30% No Interest +$15,000,000payoff −$45,000,000
- 30% Partial Correction +$30,000,000payoff −$30,000,000
- 35% Preclinical Failurepayoff −$60,000,000
License to Big Pharma −$5,000,000expected $124,250,000
Chance: License Deal Outcome- 25% Licensee Succeeds + Royalties +$400,000,000payoff $395,000,000
- 30% Licensee Reaches Phase 2 +$80,000,000payoff $75,000,000
- 30% Licensee Fails in Clinic +$15,000,000payoff $10,000,000
- 15% Licensee Abandons +$5,000,000payoff $0
- Shelve Programpayoff $0
Change the numbers. Open the workbook with ModelChoice, change a probability or a cost in the tree, and the best decision updates at once.
Key assumptions
| Assumption | Value | Notes |
|---|---|---|
| AAV Program to IND | $80,000,000 | Vector design, preclinical, CMC |
| Ex-vivo Program to IND | $60,000,000 | Cell processing, gene editing, preclinical |
| Phase 1/2 Self-Fund (AAV) | $120,000,000 | First-in-human, dose escalation, 20-40 patients |
| Phase 1/2 Self-Fund (Ex-vivo) | $100,000,000 | Cell collection, processing, infusion |
| AAV Preclinical Success Rate | 65% | Strong or moderate efficacy in animal models |
| Manufacturing Scale-Up Risk | 15% failure | GMP production of viral vectors at scale |
| Clinical Durable Response | 25-35% | One-time treatment with lasting benefit |
| FDA Accelerated Approval | 45% | Rare disease with unmet need qualifies for RMAT |
| Peak Revenue (Approved) | $1.4-2.0B | At $2M per patient, ~700-1000 treatable patients |
| Patient Population | ~5,000 worldwide | Rare monogenic disease |
The figures are illustrative: the companies and numbers are invented for teaching.
Try it in ModelChoice
- Strategy Table. Compare in-vivo vs ex-vivo vs license strategies
- Sensitivity Analysis. How do preclinical success rate and pricing affect EV?
- Risk Profile. Visualize the high-risk/high-reward nature of gene therapy
Find similar models
- Product
- Type
- Application
- Uses
Further reading
Download the workbook and open it with ModelChoice. The free trial opens every example.
Related example models
- ModelChoice★★★★★Biosimilar Launch Strategy DecisionLaunch a biosimilar at risk, wait for a patent settlement, license it in, or stay out of the market?Three decisions and 13 chance events. Best first decision: At-Risk Launch, expected value $130.0m.
- ModelChoice★★★★★Vaccine Platform Investment DecisionInvest in an mRNA platform with a lead vaccine, the platform alone with a partner, one licensed candidate, or nothing?Three decisions and 14 chance events. Best first decision: Full Platform + Lead Candidate, expected value $538.2m.
- ModelChoice★★★★★Clinical Trial Go/No-Go DecisionAfter Phase I: run Phase II, partner the compound, or shelve it?Four decisions and eight chance events. Best first decision: Proceed to Phase II, expected value $135.1m.
An illustrative example: the figures are invented for teaching. Last updated 9 October 2026.