Anonymous Client A — Mid-sized biopharma ($1.2B revenue, 25+ oncology pipeline assets)
The client had built its oncology portfolio through a combination of internal discovery and in-licensing over 8 years. The portfolio spanned Phase I through Registration across solid tumors and hematological malignancies. However, the portfolio had grown organically without a unifying strategy or investment framework. Two assets had been in development for 6+ years without clear efficacy signals, while three promising early-stage programs were starved of resources. The CFO had flagged the R&D spend as unsustainable and the board demanded a portfolio review within one quarter.
A mid-sized biopharma with 25+ oncology pipeline assets lacked a systematic framework for portfolio prioritization. Investment decisions were made reactively based on individual champion advocacy rather than data-driven analysis. The company was burning $180M annually across the portfolio with no clear go/no-go governance, leading to resource misallocation, delayed termination of high-risk programs, and an increasingly unsustainable R&D burn rate relative to its revenue base.
We deployed our proprietary portfolio optimization methodology combining quantitative scoring with strategic framework analysis. Each asset was evaluated across four dimensions: scientific merit, commercial potential, development risk, and strategic fit. We benchmarked each asset against 500+ comparable oncology programs using our pharma intelligence platform.
Portfolio audit: Cataloged all 25+ assets with current status, spend-to-date, remaining development costs, and probability of technical success (PTS) estimates
Quantitative scoring: Applied our 100-point scoring framework across scientific, commercial, risk, and strategic fit dimensions for each asset
Competitive benchmarking: Mapped each asset against 500+ comparable oncology programs in our database, analyzing development timelines, success rates, and competitive positioning
Risk-adjusted NPV modeling: Built probability-weighted NPV models for each asset incorporating PTS, development costs, peak sales, and competitive erosion
Scenario analysis: Modeled 3 portfolio scenarios — aggressive investment, selective focus, and rationalized portfolio — with capital allocation implications
Governance framework: Designed a portfolio decision governance structure with clear go/no-go criteria, stage gates, and investment thresholds
We delivered a prioritized investment roadmap with clear go/no-go recommendations for all 25+ assets. Two assets were recommended for immediate termination (combined annual savings of $45M), three assets were recommended for out-licensing or partnership, five assets were flagged for accelerated investment, and the remaining assets were categorized as maintain-with-conditions. The portfolio was restructured around 8 priority assets with a clear capital allocation plan over 3 years.
Phase 1 (Weeks 1-4): Portfolio audit and data collection across all 25+ assets, including interviews with 12 project leads and review of all development plans
Phase 2 (Weeks 3-8): Quantitative scoring and competitive benchmarking using our pharma intelligence platform with 500+ comparator programs
Phase 3 (Weeks 7-11): Risk-adjusted NPV modeling and scenario analysis with 3 portfolio options
Phase 4 (Weeks 10-14): Final recommendations, governance framework design, and board-ready presentation with investment roadmap
| Metric | Before | After | Impact |
|---|---|---|---|
| Annual R&D Spend | $180M | $135M | $45M annual savings redirected to priority assets |
| Portfolio Risk-Adjusted NPV | $2.1B | $2.56B | +22% increase through capital reallocation |
| Average Asset PTS | 12% | 18% | Higher-probability assets received concentrated investment |
| Decision Cycle Time | 6-9 months | 4-6 weeks | New governance framework enabled faster go/no-go decisions |
| Board Approval Rate | 40% | 95% | Data-driven recommendations achieved near-unanimous board support |
“Medifirm transformed how we think about our portfolio. For the first time, we had a data-driven framework that made go/no-go decisions objective rather than political. The $45M we saved by killing two underperforming assets funded three promising programs that are now in Phase II.”
VP, Portfolio Strategy
Anonymous Client A
Forecast
Reallocated capital into higher-probability assets, projected to increase portfolio NPV by 22% over 3 years based on risk-adjusted modeling.
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