Abstract: FR-PO0034
Forecasting the Launch of High-Cost Therapies in Rare Kidney Diseases
Session Information
- Artificial Intelligence and Digital Health at the Bedside
November 07, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Artificial Intelligence, Digital Health, and Data Science
- 300 Artificial Intelligence, Digital Health, and Data Science
Author
- Sarkar, Soumavo, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, United States
Background
Accurate launch forecasts are crucial for pharma companies to assess product potential and scale effectively. Traditional long-range forecasts often miss key launch details like new patient starts, treatment prerequisites, access ramp, and patient support programs—especially critical in rare diseases like IgA Nephropathy. Novartis faced this challenge, as legacy forecasts were overly optimistic. A more granular, realistic forecast was needed to guide commercial teams and set proper expectations.
Methods
We built a multi-product NBRx forecast model covering various MoAs, including three Novartis therapies. Due to low-confidence prevalence data, we validated epidemiology using AI-based patient finder models. Market share was estimated through demand studies and tested across multiple TPP permutations with HCPs. Besides, the REMS program requirements of the products required in-depth analysis of industry analogues to better understand the adoption uptake post immunization impact, access risks, and portfolio investment trade-offs.
Results
The model enabled: 1) Granular launch KPIs to reset leadership expectations and guide resource allocation; 2) Improved accuracy of enrollments, patient starts, dispenses (paid vs. free), and net sales vs. traditional forecasts; 3) Identification of key forecast risks for proactive commercial planning.
Conclusion
Launching high-cost therapies in rare kidney diseases poses risks. Pharma companies must anticipate these and build realistic launch forecasts. Access restrictions and heavy use of patient assistance programs can impact manufacturing, distribution, and engagement strategies.