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Blog
The $200 Million Question in Clinical Development
How to use data to tear down siloes and forecast the best clinical development plan for your next big drug launch.
Jun 14, 2022
Imagine you have a promising new oncology drug in late phase development. And early clinical trial data results point to patients experiencing on average six months progression-free survival (PFS). However, it will take another year or more to gather data and verify overall survival (OS) rates.
Now you have to decide: Do you launch the drug immediately using PFS to gain regulatory approval and negotiate prices, or should you wait a year to gather overall survival data? It’s a common question among sponsors vying to gain an edge in the increasingly competitive oncology space.
While PFS data takes less time to capture, it is not differentiating against competitors. This makes it more difficult for sponsors to secure premium pricing from payers. However, waiting to capture more compelling overall survival data comes with significant added research & development costs and in the case of certain types of breast cancer, requires waiting an additional year to recognize commercial revenue.
The loudest voice wins
In many big pharma companies, these kinds of decisions are made based on siloed opinions coming from cross-functional stakeholders who each bring their own agenda to the table.
The medical team will likely want to gather additional data so they can demonstrably out-perform competitor products or the current standard of care, whereas the clinical operations team may be more concerned about whether they have the resources to capture that data, what the additional research will cost, and the potential impact of trial extensions and other trial dependencies.
These teams will have to compete with commercial stakeholders whose primary focus is on driving revenue projections and figuring out how the product launch will affect their global timelines and sales strategies.
Often forgotten are the clinical statisticians, who are focused on how the decision impacts trial specifics like patient sample sizes, number and location of recruitment sites, and how to maximize the statistical power of each trial.
Each of these teams brings a valid perspective to the decision-making process, but each on their own is biased.
In an ideal setting, these teams would work collaboratively, sharing their expertise to make the best overall decision for both the business and patients. But in reality, these meetings can devolve into disagreements, where the stakeholder group with the loudest voice or most senior staff ultimately gets their way. This can cause companies to make seemingly smart decisions that ultimately cost them millions of dollars in lost revenue.
Single source of truth
Companies can eliminate this risk by using a forecasting platform, like IQVIA’s Pipeline Architect, which makes it easy to calculate the outcomes of different scenarios. Pipeline Architect combines public clinical trials data with proprietary development cost benchmarks to accurately forecast outcomes. This allows stakeholders from across the company to conduct scenario planning and identify the most optimal development plan for their program.
This creates a single source of truth for all decision makers, and transforms the conversation from a battle of perspectives to an objective consideration of options. In the case of the new oncology drug, the data might show that waiting a year to get OS data will increase the product price by 20%, outweighing the year-long wait to start recognizing revenue. Conversely, there could be a risk of competitors coming to market that could negatively affect that price, making it more valuable to launch first to market using PFS data.
These aren’t just broad projections. Machine learning algorithms within these tools quantify how issues around launch timing, product efficacy, patient population, disease type, competitor products, local pricing trends, and other clinical and market factors will impact long-term business outcomes.
When disparate stakeholder groups leverage data-driven forecasting platforms, it allows for the identification of the most optimal development plan to maximize business and patient outcomes.