Pharmaceutical lifecycle
Pharmaceutical forecasting evolves significantly throughout the product lifecycle, adapting to the changing needs and priorities of each stage, from market potential to sales stability. Companies will employ a variety of forecasting types, each tailored to the specific needs of the development phase, market dynamics, and strategic considerations.
Market Assessment and Potential Analysis
In the initial phases – preclinical through early trials – consensus forecasts and epidemiology (EPI)-based forecasts are crucial tools for gauging market potential. Syndicated external reports, such as those from brokers or evaluators like Evaluate Pharma, provide a broad understanding of the market landscape, competitive dynamics, and potential revenue. These forecasts are valuable in assessing the initial feasibility of a product, estimating the overall market size, and understanding the competitive environment.
While these external forecasts are effective in giving a broad market view, they become limited as the product advances. As the market dynamics become more complex, the fixed nature of these external reports makes it difficult to incorporate new information or specific considerations that may affect the product’s performance.
Adapting Forecasts to Specific Needs
As a product progresses through Phase III and approaches launch, internal forecasts become increasingly important. Whether developed in-house or outsourced to specialists like J+D (an evaluate company), these forecasting models are designed to incorporate specific market characteristics or product nuances that are critical for accurate volume and sales predictions.
EPI-based models are commonly used at this stage, as they allow companies to understand the drivers of the market, such as diagnosis rates, treatment rates, and patient segmentation. This type of modelling is particularly useful when new treatments have the potential to expand the market significantly. For example, in a therapeutic area like Alzheimer’s, where innovative treatments could alter the market dynamics, internal models that account for these potential changes are crucial.
In addition to market-specific characteristics, product-specific factors also play a key role in late-stage forecasting. Products like CAR-T therapies, where the treatment journey involves multiple stages before a patient actually receives the therapy, require detailed modelling that considers potential drop-off points. Patients may receive a prescription, but due to the complexity of the treatment process, they might not complete all the steps needed to receive the therapy. Accurately modelling these dynamics is essential to projecting sales volumes realistically.
Using Forecasts for Scenario Analysis and Validation
Internal forecasts are not just about accounting for unique factors; they are also crucial for running scenario analyses. These flexible models allow companies to explore various “what if” scenarios that are key to strategic planning:
This adaptability is essential for managing uncertainties as the product moves toward market introduction. Companies can evaluate potential outcomes, assess risks, and refine their strategies based on various market conditions.
Gaining Market Share: Understanding Patient Segmentation
As a product nears launch, it becomes critical to understand how the patient segmentation can impact market penetration. EPI-based forecasting allows companies to break down patient populations into categories such as new patients, switched patients, and stable patients. This segmentation is key to determining how much of the market the product can realistically capture in the early stages after launch.
For example, consider a company aiming to achieve 20% market share. If only 10% of the total patient population consists of new or switched patients, the company cannot expect to capture 20% of the market immediately. The remaining patient population may consist of stable patients who are already on an alternative therapy and may not switch to the new product right away. Therefore, achieving higher market share might take time unless there is a compelling reason for stable patients to switch.
By using an opportunity-based forecasting model, companies can predict the rate of uptake more accurately by assessing how many patients are likely to switch, how many new patients will emerge each year, and how the treatment landscape might change. This approach helps identify realistic market share trajectories and the time frame in which a product might reach its target market share.
Shifting to Sales-based Forecasting
Once a product has launched and the market becomes more stable, companies may shift from the more complex EPI-based approach to a sales-based forecasting model. This approach is typically used for low-priority products or in cases where the market is well-established and predictable. Sales-based models, which rely on historical sales data trended out to the future, offer an efficient way to forecast and can potentially be automated to some degree using machine learning.
However, even in this phase, EPI-based models may still be required in certain regions or for specific strategies, especially if changes in market drivers or patient behaviour are anticipated.
Considerations Beyond The Product Launch
Loss of Exclusivity and its Impact on Forecasting
As a product approaches Loss of Exclusivity (LoE), forecasting strategies may need to shift again. When a drug loses exclusivity and faces competition from generics, an EPI-based forecast may still be valuable. This is particularly true if the company is developing LoE strategies that rely on the same EPI assumptions or market drivers used during earlier stages of forecasting. For example, if part of the strategy is focussed on capturing niche patient populations or leveraging epidemiological insights, EPI-based models will remain essential in guiding these decisions.
Strategic Forecasting for In-licensing
In addition to its importance during the LoE stage, Business Development and Licensing (BD&L) decisions often require detailed forecasting insights. When evaluating whether to in-license a product, pharma companies typically begin by using syndicated consensus forecasts for an initial understanding of market potential. However, these early insights are only the starting point.
To fully assess the potential value of an in-licensing opportunity, companies will develop EPI-based forecasts. This method allows them to run various scenarios and evaluate how different market drivers (e.g., treatment rates, patient population changes, or competitor actions) might impact the product’s forecast. By simulating different potential outcomes, the company can gauge the potential range of forecasts and make a more informed decision on whether to in-license a product.
This combination of syndicated forecasts and EPI-based scenario analysis ensures that companies are well-prepared to make high-stakes BD&L decisions, enabling them to manage uncertainty and potential opportunities more effectively.
Combining Forecasts for Comprehensive Insights
Throughout the pharmaceutical lifecycle, both external forecasts and internal models are used in tandem. Consensus forecasts continue to serve as useful validation tools, providing an external benchmark to compare against internal estimates. When significant discrepancies arise between internal forecasts and broker reports, it could indicate a need to revisit assumptions or identify market dynamics that might have been overlooked.
Ultimately, the shift in forecasting – from relying on broad, syndicated data in early stages to using tailored, dynamic models closer to launch – reflects the changing nature of pharmaceutical product development. Each type of forecast plays a distinct role, ensuring that companies have the insights needed to navigate each stage of the product lifecycle effectively.
Author
Andrew Ward, Consulting Director at J+D Forecasting. An expert in the pharmaceutical analytics space since 2002 and leading a consultancy team in delivering forecasting solutions including software application development at J+D since 2014.
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