Loss of exclusivity (LoE) is accelerating across major therapeutic areas. Between 2025 and 2030, more than $230B in pharma revenue will go off patent, pushing many companies toward an imminent patent cliff and forcing more deliberate, data-driven end-of-lifecycle planning. At the same time, true commercial white space is becoming harder to find, and new launches are increasingly smaller, more segmented, and more competitive.

Leading organizations are responding by treating forecasting as a learning system, not a one-off exercise.

In this webinar, we'll share a real-world case study where we built a drivers-based machine learning forecasting engine to predict LoE erosion for a neurology asset. Rather than relying on a handful of hand-picked analogues, the model learns from hundreds of historical events and external market drivers to generate forecasts that are more accurate, explainable, and scalable.

While the case focuses on LoE, the same ML engine can be applied across the product lifecycle - from forecasting launches to modeling branded competition and generic or biosimilar uptake.

What you’ll take away
- Why traditional analogue-based forecasting often breaks down
- How a drivers-based ML approach improves erosion predictions and transparency
- How better forecasts support earlier, more confident de-investment and resource allocation decisions
- How the same modeling framework can be reused across LOE, launches, and competitive market simulations