To Reduce Commercial Risk for Orphan Drugs, Transform Forecasting

, ,

To successfully commercialize orphan drugs, companies need to set aside general medicine strategies and adopt a more granular approach to identifying and capitalizing on commercial opportunities.

Commercializing rare and ultra-orphan drugs presents unique challenges for biopharmaceutical companies. The core problem orphan drug commercial teams face is a lack of data. Rare and ultra-orphan products don’t produce the data quantities general medicine therapies do. In some cases, we’re talking about drugs with only a few thousand patients across the country. Therefore, the commercial strategies companies deploy for general medicine drugs (which treat large numbers of patients) are largely ineffective for orphan drugs. To successfully commercialize orphan drugs, companies need to set aside general medicine strategies and adopt a more granular approach to identifying and capitalizing on commercial opportunities. Those that don’t will face significant business risk.

This move toward granularity should trickle down to all commercial activities, but it should start with forecasting. After all, the first step in a biopharma company’s go-to-market approach is often the forecast. Commercial operations — from incentive compensation plan design to targeting — can look very different depending on the results a company aims to achieve. Too often, orphan drug forecasts produce wildly divergent scenarios that result in uncertainty across a biopharma organization. This uncertainty can make decision-making (from commercial operations to manufacturing) difficult and increase the likelihood that a company will miss out on upside opportunities by not putting in place enough resources or expose itself to excessive risk.

When it comes to incentive compensation, for instance, a forecast that doesn’t offer details on probable outcomes gives the commercial team little guidance when they set sales goals. A company therefore runs the risk of overestimating sales results and deploying a plan with unreachable targets or dramatically underestimating sales results and blowing out the budget. Though even the most detailed forecasts will present ranges of outcomes, the best ones will include probability bands that help a commercial team make informed decisions regarding the structure of the incentive compensation plan.

Further, a biopharma company will struggle to model the commercial impact of market changes and different targeting and marketing mix strategies without a detailed and sophisticated approach to forecasting. A company assessing the expected impact of COVID-19 on a planned launch back at the beginning of the pandemic would have had to account for — and project the impact of — new access restrictions, changes in rep call capacity and an increase in digital marketing efforts. To accurately model these discrete yet interconnected items, the team would have needed an approach to forecasting rooted in advanced data analytics. Without this analytics-based approach to forecasting, the team would have essentially been flying blind when they revised their commercial strategies for the new reality.

Orphan drug manufacturers need to rethink their forecasting efforts. Start by making an all-in commitment to data analytics and advanced technology. Those that embrace this analytics-driven forecasting process, powered by dynamic and predictive forecast models and advanced technology platforms, will produce more accurate and realistic forecasts that successfully guide end-to-end commercial efforts.

From top-down to bottom-up forecasting

The traditional approach to forecasting usually takes place at the level of national patient segments. While this top-down approach can be sufficient to project commercial results for general medicine products that have large numbers of potential prescribing physicians and patients, applying it to rare and ultra-orphan settings only exacerbates the inherent uncertainty facing these commercial teams.

Further, traditional forecasting does not account for the varied paths taken by rare disease patients. These paths can lead to considerably different commercial outcomes. A forecast model in the orphan drug space must account for the twists and turns of the patient journey as well as the probabilistic nature of therapy adoption and the various interactions — involving patients, caregivers, physicians, KOLs, etc. — that contribute to treatment decisions.

While the lack of data available to orphan drug commercial teams can lead some practitioners to give up entirely on the idea of creating accurate forecasts, the small market size can actually be an advantage for commercial teams if they throw away the top-down approach to forecasting and embrace a bottom-up approach (think forecasts built at the subnational level — in some cases, even the patient level).

In the end, orphan drug commercial leaders must tighten the range of possible outcomes presented by their forecasts. Doing so will create more confidence in projected results and enable companies to make smarter investments across their commercial operations. This effort will also help the commercial team secure alignment across stakeholder groups and manage expectations in the C-suite and among investors. Most importantly, a higher level of granularity and accuracy in forecasting will help an orphan drug manufacturer reduce commercial risk.

Create dynamic and predictive models to optimize commercial efforts

To enable bottom-up forecasting, companies must work to link data with the rare disease treatment journey and develop more dynamic and predictive forecast models. Developing these models requires sophistication in data analytics. For example, teams often need to creatively combine multiple sources of data and information (e.g., claims data, medical records, etc.) to understand the patient universe for their products. To develop these models, companies also need significant computational horsepower. When properly executed, these models can help biopharma commercial teams clearly see the implications of various market events and then act on that information.

Take site activation for a CAR T therapy. A CAR T commercial team must determine the optimal locations and activation timing for new treatment sites. Many factors go into this decision-making process. For example:

  • How many potential patients are in the geography under consideration?
  • Do competitors have sites in the area?
  • What’s the incremental commercial “lift” the company can achieve from opening a new site in the geography?

Answering these site-specific questions with a top-down, national forecast model is impossible. But by modeling at a subnational level, a team can assess various market contingencies with confidence and improve its decision-making process.

In addition to helping a team make site activation decisions, a dynamic and predictive forecast model can also help it understand the implications of different outcomes. For example, if a new site’s planned onboarding is delayed, what is the impact on the company’s ability to hit the forecast? A forecast model should provide a map of the key drivers of commercial success, which, in turn, enables thorough scenario planning and, in the end, effective commercial action.

Importantly, while it’s clear that a move to bottom-up forecasting requires a shift in mindset, it also requires a shift in technology capabilities.

Embrace the platform advantage

To enable the sophisticated modeling required to forecast performance of rare and ultra-orphan drugs, biopharma companies need powerful cloud-based technology platforms.

These technology platforms will look different depending on a company’s needs. But, at a high level, they should be powerful and fast. The capabilities of Excel spreadsheets are quickly exhausted by the calculations needed for accurate orphan drug forecasting. These platforms must be able to handle the many complex calculations required to model commercial results at subnational levels, as well as Monte Carlo simulations.

Additionally, the technology should offer transparency into the forecast for the appropriate stakeholders. These stakeholders should be able to view and, in some cases, adjust assumptions to see the impact on the forecast of different commercial outcomes without creating version-control issues. This transparency helps facilitate alignment across the organization on the forecast and its assumptions — as well as the commercial efforts that follow forecasting.

Real-time data linkage is another important capability of these platforms. As sales data arrives, users should be able to see how the company’s performance tracks against the forecast. These automated updates allow a commercial team to keep a close eye on performance, identify any problems and take corrective action promptly.

Transforming forecasting for orphan drugs

The orphan drug space is one of the most exciting and important in the biopharma industry. Companies are transforming patients’ lives by uncovering novel and effective treatments to confounding illnesses. But these highly innovative companies must address the significant risk they face during the commercial process. Bad goal setting can wreak financial havoc. Ineffective targeting approaches or a suboptimal marketing mix can hinder therapy awareness and uptake.

Fortunately for orphan drug commercial leaders, analytics and technology can help them go beyond traditional forecasting approaches and deploy more granular forecasts that effectively capture the on-the-ground reality of this unique and challenging market.

To succeed here, orphan drug commercial leaders must first understand the forecast’s weighty implications for their companies’ commercial efforts. Then, embrace the analytics capabilities and technology tools needed to enable a more dynamic and predictive approach to forecasting. Transforming forecasting in this way will help these leaders reduce business risk and enhance their overall demand generation efforts.

Beghou Consulting’s Dennis Fournogerakis is an associate partner; Esin Izat is a manager and leader of the firm’s marketing research practice; and Dan Wetherill is a partner.