Navigating the complexity of forecasting oncology products

Bob Ramsey of Kantar Health addresses the complexity of forecasting oncology products.

The oncology market is facing new trends and therapies that present great opportunities for the future of pharma. Biomarkers and targeted therapies are increasingly prominent, and realizing their full potential impact on pharma revenue requires considering the implications they pose to the forecasting of oncology products. These new therapies, as well as the differences in oncology drug / regimen use compared with other therapeutic areas, add to the difficulties of oncology product forecasting and can make it challenging to determine the most effective forecasting approach.

Even before considering the recent emergence of biomarkers and targeted therapies, the usage of oncology drugs compared with drug use in most other therapeutic areas creates the need to carefully examine product forecasts and approaches. Whereas radiation and surgery are used primarily in the early stages of cancer, oncology drugs are used in late-stage treatment, as well as in the adjuvant setting after radiation and / or surgery to improve outcomes for patients in remission. In most cases, these drugs are used in combination rather than as single agents.

A significant concern in cancer treatment is that once a patient has used a certain drug, he or she may not be able to use it for a subsequent line of therapy, making it difficult to identify the appropriate forecasting approach. If the patient cannot reuse a therapy he or she has already received, a patient-flow model may be needed for forecasting. Patient-flow models can track patients from their diagnosis through various levels of treatment. The model also takes into account patients’ patterns of recurrence, remission and survival. Tracking therapy usage through the full course of disease ensures that patients who are ineligible for certain therapies due to previous treatment are accounted for accurately. While the benefits of using patient-flow models are clear, the approach is much more complex than the cross-sectional approach, which also can be utilized. The cross-sectional approach, however, does not explicitly consider a patient’s prior treatment. The complexity of patient-flow models requires more time, effort and resources. It is critical to carefully evaluate these factors when considering business needs to identify the best approach.

“The era of “personalized medicine” has arrived in oncology…”

The era of “personalized medicine” has arrived in oncology, and while physicians and patients are ready to embrace it, a host of factors need to be considered by a company seeking to enter this space. Biomarkers can have significant impact on drug development and oncology product revenue. Many biomarkers have become part of the standard of care, including therapies for breast cancer, colorectal cancer, chronic myeloid leukemia (CML), non-small cell lung cancer (NSCLC), melanoma and other tumor types. Biomarkers add complexity to oncology forecasting since populations must be precisely defined. In turn, assumptions are derived for each defined population, guiding informed decision making to uncover commercially viable opportunities.

Figure-1

Figure 1: Patient-flow models help to identify treatment opportunities in the breast cancer market
Source: Kantar Health CancerMPact® Patient Metrics
 

Targeted therapies continue to grow in importance in cancer treatment. These drugs are gaining popularity due to increased efficacy and reduced toxicity. Targeted therapies can be used in combination or as single agents. One example is the combination of small molecule tyrosine kinase inhibitors and monoclonal antibodies. Identifying forecasting assumptions for these therapies can be complicated as the duration of therapy is not as straightforward as it is with cytotoxic agents. Duration of therapy for cytotoxic drugs is well defined by the number of treatment cycles; however, it is more subjective for targeted therapies as it is usually determined by progression of disease. The variable treatment time period can make annual forecasts challenging. Further, for any therapies exceeding the duration of one year, forecasts that are typically done on a yearly basis need to be broken down into one-month periods to accommodate every month over the one-year mark.

“Targeted therapies continue to grow in importance in cancer treatment.”

The growing rate of biomarker and targeted therapy use gives them the potential to strongly impact the future of pharma revenue. In addition to the clear advantage biomarkers present to commercial revenue, they are also key in the approval of new drugs and can enable access to indications and markets that were previously inaccessible. Further commercial advantages can be realized when biomarkers are identified in early development of a new cancer therapy. Biomarkers can reduce development costs, expedite product launch and those that have good predictive value may facilitate premium pricing. Targeted therapies are also expected to have a significant impact on pharma revenue in the coming years. Use of targeted therapies has grown 900% since 2003, contributing $36 billion in revenue in 2011.

While the landscape of the oncology treatment market and the recent dominance of biomarkers and targeted therapies add complexity to navigating product forecasts and commercial opportunities, understanding their importance and learning how to address and incorporate them is essential.

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About the author:

Robert Ramsey, Ph.D., is Vice President and Chief Scientific Officer at Kantar Health. Dr. Ramsey’s career spans over 30 years in academia, product management, marketing, business development, and strategic planning.

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