Many biopharma companies start out with a single asset, focusing attention on how to optimize the potential value of that asset as it advances through development and (hopefully) onward to some sort of transaction or commercial launch. However, most companies do not remain single-asset organizations. Clearly, they will need to discover or acquire a pipeline of additional therapies to drive long-term viability and success. In short, they will need to build a portfolio.
When a company has a portfolio of assets, its decision-making processes become much more complex. Now, it must determine how to best allocate resources across a range of potential therapies that may have distinct characteristics, address different markets, be in various stages of development, and exhibit different opportunities and risks.
For many companies, navigating such a complex decision-making environment can be particularly challenging, as the approaches and processes that they may have used to optimize a single asset are ill-suited to supporting strategic decisions across a diverse portfolio. In this series of articles, we will address three key areas that companies often struggle with:
- Portfolio Forecasting – Holistically understanding the potential future value of a portfolio comprised of diverse assets
- Portfolio Prioritization – Effectively prioritizing individual assets within a portfolio and allocating resources accordingly
- Portfolio Reviews and Management – Systematically assessing the portfolio and adjusting decisions based on environmental or asset-specific changes
In this installment, we address the topic of portfolio forecasting. Let’s dive in.
Portfolio Forecasting: Why It’s Important
In the high-stakes world of biopharmaceutical research and development, portfolio forecasting is far more than a financial exercise; it is a core strategic capability. The process of drug development is defined by immense uncertainty, long timelines, and binary outcomes, where a single clinical trial result can create or destroy significant value. In this environment, the ability to quantify the potential of a pipeline is the bedrock of disciplined and scientific decision-making. A robust forecasting approach is critical to providing a credible, data-driven foundation upon which a company can build subsequent strategic activities such as prioritization, resource allocation, and portfolio governance.
The imperative for a sophisticated forecasting capability has never been greater; R&D costs continue to climb, and external pressures are mounting. The Inflation Reduction Act (IRA) in the United States, for example, is fundamentally reshaping the economic lifecycle of pharmaceutical assets. In this complex and dynamic landscape, companies can no longer rely on simplistic, top-down estimations. A rigorous, credible, and transparent forecasting process is essential to navigate uncertainty, build investor confidence, and ensure that capital is directed toward assets with the greatest potential to deliver value to both patients and shareholders.
Standardization vs. Granularity
A central tension in portfolio forecasting is the trade-off between creating a consistent, standardized approach for portfolio-level comparisons and developing granular, customized models that accurately reflect the unique attributes of each asset. Resolving this tension is critical for effective portfolio management.
A standardized approach is indispensable for portfolio-level decision-making. To make rational trade-offs between disparate programs—such as a novel antibody-drug conjugate (ADC) for a solid tumor versus a line extension for a blockbuster in immunology—decision-makers require a common currency for trade-offs. Conducting opportunity assessments using consistent methodologies and harmonized assumptions enables true “apples-to-apples” comparisons. Without a consistent framework, the portfolio review process can devolve into a series of isolated, incomparable project discussions. Siloed assessments, where different teams use different valuation methods, can make it impossible to get a holistic view, hindering strategic alignment and effective resource allocation.
On the other end of the spectrum, a rigid, one-size-fits-all forecasting model can be just as misleading. The nature of assets changes dramatically across the development lifecycle, requiring some differentiated approaches. Early-stage assets, for example, are characterized by high scientific and competitive uncertainty. They cannot be effectively evaluated using the same financially driven models that are appropriate for late-stage assets nearing launch. Applying a framework tailored for late stage assets that heavily weights near-term financial metrics can undervalue innovative (but highly uncertain) early stage programs. This can stifle innovation and starve the pipeline of its future growth engines.
Enter the “Tiered Rigor” Framework
One optimal solution is a “tiered rigor” approach, where an asset’s strategic priority dictates the level of forecasting depth and customization. This results in more intelligent and efficient application of analytical resources.
The process begins with a preliminary, high-level categorization of all pipeline programs, often into High, Medium, and Low priority tiers. This initial bucketing is not the final prioritization, but an early assessment based on factors like stage of development, scientific rationale, alignment with corporate strategy, or confidence in the mechanism of action (MoA). This strategic tiering guides the forecasting effort:
- High-Priority Programs: These programs are deemed central to the company’s future and should receive a rigorous and customized forecasting effort. That may involve building a detailed, bottom-up patient-flow model, conducting extensive primary market research with physicians and payers, and modeling complex interdependent assumptions and scenarios.
- Medium-Priority Programs: These programs can be evaluated using standardized, yet robust, The forecast could be driven by a more streamlined patient-based model primarily informed by analog analysis of comparable products, rather than bespoke primary research.
- Low-Priority Programs: For assets that are of lower strategic importance or are very far from launch, a high-level model may suffice. This provides a reasonable estimate of potential value without consuming excessive resources.
The “tiered rigor” framework establishes a consistent methodology while allowing the depth and granularity of the inputs to vary by priority level. It focuses the most intensive analytical work where it is most needed and provides the best of both worlds: the consistency necessary for portfolio-level trade-offs among programs at the same tier and the tailored detail required for accurate program-level valuation.
Navigating Interdependent Assumptions
The true complexity of forecasting lies in modeling the web of interdependent assumptions that define an asset’s trajectory. Moving beyond static inputs to dynamic, interconnected models is what separates a basic valuation from a powerful strategic tool.
Consider a targeted therapy in oncology that requires biomarker testing to identify eligible patients. The drug’s uptake is directly dependent on the rate of testing. This raises critical strategic questions: If the drug is first launched in a later-line setting (e.g., 2L+), should the company invest heavily in market-shaping activities to drive up testing rates? Or should it conserve resources and wait for a potential front-line (1L) approval, when the patient population is larger and the return on that investment is greater?
Another example of this complexity involves the new “Strategic LOE.” The traditional concept of loss of exclusivity (LOE) is tied to patent expiration, leading to a sharp drop in revenue upon generic or biosimilar entry. However, the Inflation Reduction Act has introduced a new, impactful event horizon that functions as a “Strategic LOE.” IRA mandates price negotiations after 9 years from launch for small molecules and 13 years for biologics. This event decouples the economic life of an asset from its patent life, fundamentally altering the calculus of value creation. This creates a profound strategic dilemma, particularly for assets with multiple potential indications. The moment an asset receives its first approval, the 9- or 13-year clock starts ticking.
This means that forecasting can no longer treat LOE as a simple, fixed date. It must evolve into a tool for strategic optimization. Teams must model the risk-adjusted NPV of different indication sequencing pathways, forcing a direct trade-off between speed-to-market in one indication versus long-term value capture across the entire asset. These transform forecasting from a passive valuation exercise into an active input for a clinical development strategy.
Given the many complexities, should teams spend months building intricate, all-encompassing models for every asset? Or is a simpler, more agile approach better? Is the “tiered rigor” framework the answer? Should the most complex, interdependent models be reserved for the highest-priority assets where these dynamics are pivotal to the investment thesis, whereas for the rest of the portfolio, a consistent, less complex model is more efficient?
The key is to embrace an iterative process. The portfolio forecast is not static. After each major prioritization cycle, where some programs may be de-prioritized, the entire portfolio forecast should be refreshed. The removal of one program can alter the assumptions for others, e.g., freeing up resources to accelerate a different project or changing the IRA/LOE assumptions for later-launching assets if an earlier program is terminated. This iterative loop between forecasting and prioritization ensures that the portfolio potential remains current and reflective of the latest strategic decisions, balancing deep analysis with operational agility.
Beyond the core challenges discussed above, companies must also skillfully navigate other critical tensions. There is the organizational tension between objectivity and advocacy, which can put a centralized portfolio or insight function’s need for unbiased inputs against a project team’s passion for its assets. Decision-makers also face the conflict between model complexity and clarity, as sophisticated models run the risk of becoming “black boxes”. Lastly, financial methodologies can create a strategic tension between valuing near-term certainty and long-term optionality, often penalizing the high-risk innovation required for future growth transformations.
What’s Next?
In part 2 of this series, we will discuss portfolio prioritization, which is crucial for effective strategic planning and resource allocation. To stay up-to-date on our latest publications, please follow us on LinkedIn or check our Insights page frequently.
Blue Matter helps clients navigate these forecasting complexities by developing bespoke forecasting models that are both robust and pragmatic. We combine deep scientific and commercial expertise to build quantitative, patient-flow-based forecast models. Our approach focuses on implementing the right level of rigor for the right assets, ensuring that forecasts are not just a number but a credible and dynamic foundation for strategic decision-making in a rapidly evolving landscape. If you have questions or business needs related to this topic, then please contact us via our website.
