Biotech Portfolio Scenario Planning: Prioritize Programs, Extend Runway and Increase Value

From Gut Feel to a Framework: A Better Way to Allocate Resources Across Biotech Programs

For an early-stage biotech, having multiple promising R&D programs is a good problem to have until it becomes a cash problem. With a finite runway, every decision on how to allocate resources across biotech programs feels monumental. The pressure to choose correctly, often with incomplete data, can lead to analysis paralysis or funding decisions based more on internal politics than strategic alignment. The core challenge is not a lack of scientific vision but the absence of a simple framework to translate that vision into a sustainable operational plan. This is about moving beyond gut-feel to make disciplined, data-informed choices that extend your runway and maximize your chances of hitting the next critical, fundable milestone. It’s a foundational element of effective biotech portfolio management.

The default approach to R&D program prioritization often involves backing the project with the most compelling narrative or the most senior scientific champion. While passion is essential, it is not a strategy. Portfolio scenario planning provides a structure to evaluate trade-offs objectively, helping teams overcome common biases like the sunk cost fallacy. Methodologies like constrained-optimization heuristics can formalise resource choices; you can see research on optimisation and resource allocation for practical approaches. This process is not about finding a magic formula to predict the future. It is about creating a common financial language to discuss different potential futures and understand the consequences of your decisions today.

The model is a tool to inform judgment, not replace it. What founders find actually works is shifting the conversation from “Which project is the best?” to “Which portfolio of projects gives us the best shot at our next milestone within our financial constraints?” This structured approach becomes particularly urgent under financial pressure. In fact, a key trigger for implementing this process is managing a runway of less than 18 months. For context, the typical runway planning horizon for post-seed or Series A companies is 12-18 months, making this a frequent and critical exercise in financial planning for drug development.

Building Your Model: The Essential Data for Early-Stage Biotech Budgeting

One of the biggest hurdles in decision making in biotech investments is the perceived effort required to gather data. The reality for most early-stage startups is more pragmatic: the goal is directionally correct data, not perfect data. You do not need to spend weeks on complex financial modeling. You can start today with the information already available in your existing accounting software, like QuickBooks or Xero, and your payroll system. This approach directly addresses the pain of pulling together data fast enough to make timely go or no-go decisions.

Distinguishing Direct Costs from Overhead

The first and most critical distinction is to separate direct, program-specific costs from general overhead. Direct costs are the variable expenses you can directly attribute to a single R&D program. Overhead costs, also known as general and administrative (G&A) expenses, are the fixed costs of running the business, such as rent, utilities, and executive salaries. For this analysis, you should set overhead aside. Do not get stuck trying to allocate portions of the CEO’s salary or office rent to each program. You can use activity-based costing for more detailed overhead allocation later, but it is not necessary for this initial prioritization exercise.

Gathering Your Core Data Inputs

You only need to focus on a few key data points to build a powerful and practical model.

1. Direct Headcount Costs: For each R&D program, list the scientists, engineers, and technicians working directly on it. From your payroll system, pull their fully-loaded cost, which includes gross salary, employer taxes, health insurance, and any other benefits. This calculation gives you the direct people cost per program, which is often the largest single expense.
2. External Spend: Identify major external costs tied directly to each program. This includes contracts with contract research organizations (CROs), specialized reagents, preclinical studies, or specific raw materials. You can find this by reviewing vendor expenses in your bookkeeping system. Using tags or classes in QuickBooks or Xero to track project-specific spending from the outset makes this step much simpler. If you have grant funding, ensure you integrate grant milestones and their associated budgets into the relevant program plans.
3. Timelines and Milestones: For each program, outline the key scientific milestones over the next 12-18 months. These should not be simple activity completions; they should be significant, value-creating events that de-risk the program and make it more attractive to future investors or partners.

By focusing only on these direct, variable costs, you can quickly assemble a “good enough” picture of where your money is going and which expenses you can realistically control.

Quantifying the Unknown: A Practical Approach to R&D Program Prioritization

Translating uncertain scientific milestones into comparable financial metrics is often the hardest part of R&D program prioritization. How can you compare a high-risk, high-reward new drug candidate to a lower-risk, incremental improvement for a discovery platform? This is a common challenge that requires careful consideration of platform versus program budgeting, especially when weighting platform investments against specific therapeutic program spend. A simplified risk-adjusted Net Present Value (rNPV) framework provides a consistent logic for this comparison. The objective is not to predict the future with perfect accuracy but to apply the same evaluation criteria across every program.

A Simplified rNPV Framework

We use a straightforward formula to create a common yardstick for all projects, regardless of their scientific nature. This helps address the pain of translating scientific uncertainty into comparable numbers.

Simplified rNPV formula: (Potential Financial Upside * Probability of Success) - Cost to Achieve Success.

Let's break down each component:

• Potential Financial Upside: This is an estimate of the program's value upon hitting its next major milestone. For an early-stage company, this might be the anticipated step-up in valuation at the next financing round, the value of a potential licensing deal, or the estimated market value of the asset at that stage.
• Probability of Success (PoS): This is the most subjective, yet powerful, input. It represents the team's collective judgment on the likelihood of achieving the next milestone. To avoid endless debate, it helps to use standardized scoring bands based on both technical and market risk. A practical approach is bucketing PoS into three tiers: High (70%+), Medium (40-60%), and Low (10-30%).
• Cost to Achieve Success: This is the sum of the direct headcount and external spend you calculated in the previous step required to reach the next milestone. For more context on the underlying principles, see a concise primer on risk-adjusted NPV for valuation.

An Example in Practice

Consider a biotech startup evaluating two very different programs:

• Project A is a novel drug candidate. The potential upside is enormous, estimated at $50M in valuation uplift. However, the technical risk is very high, and the cost to reach the next key data readout is $5M. The team agrees the PoS is low, perhaps 15%.
• Project B is a new software module for the company's existing discovery platform. The upside is more modest at $10M, but it's a known technical challenge with a clear market need. The cost is $1M, and the team assesses the PoS as medium, at 60%.

Let’s apply the formula:

• Example Project A (New Drug Candidate) calculation: ($50M * 0.15) - $5M = $2.5M Risk-Adjusted Value.
• Example Project B (New Software Module) calculation: ($10M * 0.60) - $1M = $5.0M Risk-Adjusted Value.

Surprisingly, the lower-risk software project shows double the risk-adjusted value at this moment. This result does not automatically mean you should cancel the drug program. Instead, it provides an objective starting point for a strategic discussion. It forces the team to confront the resource allocation models they are using and ask critical questions. Is the portfolio balanced? Is the high-risk project consuming too many resources relative to its current risk-adjusted value? Could a small investment in Project B generate near-term value that extends the runway for the riskier, but potentially more transformative, Project A?

Scenario Analysis for Biotech Startups: Modeling Your Financial Future

With risk-adjusted values calculated, the next step is to see how different funding decisions impact your most critical metric: cash runway. This is where you connect program prioritization directly to financial planning for drug development. This analysis addresses the core pain point of forecasting how reallocating people and spend will affect cash flow. You do not need complex software for this; a simple spreadsheet in Google Sheets or Excel is perfectly sufficient. You can find practical templates for building multi-program models in Excel to get started.

Setting Up Your Runway Model

Create a model with months along the top as columns. The rows should be structured to show each program’s direct costs, broken down into Headcount and External Spend. Below the programs, add a section for your fixed overhead costs (G&A). A summary at the bottom should calculate your Total Monthly Burn Rate, Net Cash Flow, and the resulting Cash Balance over time. This structure allows you to clearly see how changes in one program ripple through to your company's overall financial health.

Running Key Scenarios

Now, you can use the model to run different scenarios and visualize the trade-offs.

• Scenario 1: The Baseline. Input all current and proposed programs with their full costs. Your model will immediately calculate your default cash runway. Often, this is shorter than founders expect and serves as a critical wake-up call.
• Scenario 2: The 'Cut' Scenario. Based on your rNPV analysis, deactivate the costs for a lower-ranking program. The model instantly recalculates the burn rate and shows you precisely how many months of runway you have gained.
• Scenario 3: The 'Reallocation' Scenario. Instead of a simple cut, model pausing one project and moving a scientist to another. You can adjust the headcount costs between programs and see the nuanced impact. This allows for decision-making beyond a binary go or no-go choice.

A scenario we repeatedly see is a founder realizing that funding all projects results in a 10-month runway, which is too short to reach a meaningful data readout before needing to fundraise from a position of weakness. By modeling a pause on a secondary, lower-value program, they see their runway extend to 15 months. That extra five months provides the critical time needed to generate compelling data from their lead asset, dramatically improving their negotiating position for the next financing round.

Putting It All Together: From Analysis to Action

Building a robust framework for early-stage biotech budgeting does not require a dedicated finance team or enterprise software. It requires a commitment from leadership to a pragmatic, data-informed process that fosters open and objective conversation.

1. Start Simple, Start Now. Use your existing accounting and payroll systems to pull direct costs for each program. Focus on being directionally correct rather than perfectly precise. You can build a first-pass model in an afternoon that delivers immediate strategic value.
2. Use Frameworks to Guide, Not Dictate. The simplified rNPV calculation is a tool to facilitate a strategic conversation, not an algorithm that makes decisions for you. Its purpose is to help remove emotion, bias, and internal politics from the debate, grounding the discussion in a shared set of assumptions.
3. Connect to Runway. The ultimate value of this exercise is understanding the real-world impact of your choices on your cash. Your scenario model is the bridge between your scientific strategy and your financial reality, ensuring your operational plan is not just ambitious but also sustainable.

This process is dynamic, not static. You should revisit your assumptions and update your model quarterly, or whenever significant new data emerges from your research. The goal is to make conscious, informed trade-offs, ensuring every dollar and every hour is allocated to maximize your company's overall chance of success. For additional templates and resources, visit our program-portfolio FP&A hub at the topic level: program-portfolio FP&A.

Frequently Asked Questions

Q: How often should we update our portfolio scenario model?
A: A good cadence is to review the model quarterly as part of your regular financial planning. You should also conduct an ad-hoc review any time a major event occurs, such as unexpected clinical data, a change in the competitive landscape, or a significant shift in your fundraising timeline.

Q: Our projects are too early to estimate 'financial upside'. What should we use instead?
A: If a specific dollar value is too speculative, use a proxy for value. This could be a simple 1-10 scoring system for "Strategic Importance" or "Impact on Next Fundraising." The key is not the absolute number but applying a consistent, relative scoring methodology across all programs to enable comparison.

Q: How do we get buy-in from the scientific team for a finance-driven process?
A: Frame it as a tool for strategic clarity, not a top-down budget cut. Involve scientific leads in defining the milestones and assessing the Probability of Success. When the team co-owns the inputs, they are more likely to trust the outputs and see the process as a way to protect and prioritize their most promising work.

Q: What is the biggest mistake companies make when implementing R&D program prioritization?
A: The most common mistake is treating it as a one-time, perfect analysis. Teams get bogged down seeking perfect data instead of using good-enough estimates to start the conversation. The goal is to build a dynamic tool that evolves with your science, not a static report that sits on a shelf.