Commercial forecasting for cell and gene therapies: 4 obstacles to overcome
Rick Johnston, Ph.D, Senior Software Solutions Principal at IQVIA
David Wolter, Vice President, Consulting
Alexandra Tataru, Product Manager at IQVIA
Mar 12, 2020
Commercial forecasting for cell and gene therapies

Development of cell and gene therapies is among the most exciting innovations occurring in pharma research today. These treatments, which adapt a patient’s own genes and cells to treat inherited or acquired diseases, take personalized medicine to a new level.

As of 2019, the US Food and Drug Administration (FDA) has approved 19 of these treatments for conditions ranging from large B-cell lymphoma, to inherited blindness, to the appearance of wrinkles in adults. Industry analysts predict the market for cell and gene therapies will surpass $11.96 billion by 2025.

However it is still early days, and these treatments face a number of unique obstacles that make accurate commercial forecasting a huge challenge:

  1. Personalized production. Forecasters typically do not have to be concerned with manufacturing and supply chain when generating commercial forecasts, assuming there will be enough established capacity to treat any patient who needs therapy. That’s a good assumption for pharma and biopharma manufacturing, where large scale batch manufacturing is used to churn out thousands of doses that can be stored for months or years before they’re needed. But in cell and gene therapies, each treatment is created with a patient’s own cells as a starting point. This means manufacturers cannot rely on traditional methods of batch manufacturing ahead of time – they must wait for the patients to come to them. Forecasters need to be able to predict how rapidly a patient’s cells can be extracted, adapted and returned for treatment. They also need to be able to predict how many patients can be treated at once and what to do if the demand changes significantly from week to week. These are complex equations that have to factor in transportation, logistics, as well as capacity analysis and scheduling. It means forecasters have to include complex supply chain and manufacturing considerations into forecasts that previously assumed ‘infinite’ manufacturing capacity.
  2. Pay for performance. Payer decision-making further complicates the cell and gene therapy forecasting process. These treatments come with very high price tags – for example, Yescarta, a treatment for large B-cell lymphoma, costs up to $373,000; while Kymriah, which treats B-cell acute lymphoblastic leukemia, costs up to $475,000 per treatment. Such one-time price tags are difficult to sustain with current methods of reimbursement. To cover these drugs, payers have had to rely on new ‘innovative’ reimbursement frameworks, such as linking payments to outcomes, or spreading payments over time. These models allow payers to manage the risk that a patient may not respond to treatment, or to spread out costs over a longer period of months to years in cases of success. But for commercial forecasters, these models require them to consider the risk of treatment failure explicitly in their forecasts, as well as the cash flow implications of deferring revenue.
  3. The potential for cure. Many cell and gene therapies are revolutionary precisely because they promise the potential for a “one-and-done” cure. This is an exciting prospect for those affected by an otherwise chronic disease, but presents a set of unique challenges for commercial forecasters. Patients take a much shorter course of therapy, and their responses to the drug have a high degree of uncertainty, both in terms of outcomes and side effects. For example, the successful treatment rate at two years for lymphoma is around 40 percent.

    This kind of limited engagement and variability in outcomes makes forecasting a lot more complicated. Longevity and loyalty become irrelevant factors in the forecasting process, and every time a patient successfully uses your treatment (or your competitor’s) the potential patient pool shrinks, changing the forecast equation.

  4. First to market is more vital than ever. Being first to market is always good for business, but with cell and gene therapies it’s even more of an advantage. Because many of these treatments are curative, the time between the first and second market entrant has the potential to be significant to the overall commercial potential of an asset. Market leaders will have the chance to treat as many patients as possible – if they can find them, establish access, and manufacture sufficient treatments in time. Treated (and cured) patients will never switch to a competitor product, and this will create fierce competition to build brands and marketing quickly and effectively. Forecasting that window of opportunity and understanding how many patients a manufacturer can realistically support will become a vital component of any market strategy.

The right forecasting solutions can help

IQVIA The Human Data Science Company

Lack of historical data, complicated payment structures, and custom production means forecasters need more sophisticated data analysis tools and strategies to deliver accurate predictions for cell and gene therapies.

Commercial forecasting tools for this treatment category need to be able to handle an order of magnitude more detail than is usually used for forecasting pharmaceutical or biotech products. They should be able to combine inputs from epidemiology analysis, the patient journey, market access and reimbursement, as well as manufacturing and supply chain, to establish accurate revenue and profit forecasts.

These days, increasingly sophisticated forecasting platforms, such as IQVIA’s Forecast Horizon, are leveraging the power of cloud computing and machine learning (ML) tools to enable this level of analysis, supporting the greater complexity while reducing manual input and adjustment. When forecasters have access to a platform with this level of detail, streamlined with automation, they can accelerate the forecasting process, gain accuracy, and adjust results on a constant basis as the patient population, payer environment and competition evolves.

To learn more about our approach to forecasting, click here or contact us at

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