Presentation (CTI)

OBJECTIVES: The Inflation Reduction Act (IRA) of 2022 introduced Medicare drug price negotiations of Maximum Fair Prices (MFPs) to reduce patient costs and federal spending. Previously, we developed a model based on the first-round negotiations to predict outcomes of the second negotiation round. With round two results published in November 2025, we aimed to refine the model using results from both rounds, evaluate the stability of key pricing drivers, and predict round three MFPs.
METHODS: We conducted a series of regression analyses using data from the first and second rounds of negotiated drugs. Key variables included sources available at the time of analysis, including 2023/4 Wholesale Acquisition Cost (WAC), Medicare 2022/3 Part D spending, and 2023/4 Federal Supply Schedule (FSS)/Big Four prices.
RESULTS: Combined results from the two negotiation rounds (n=25) showed an average WAC price reduction of 61%, with a range of 34% to 86%. The regression model incorporating the key variables explained 97% of the variance in the MFP outcome (R² = 0.97), and both WAC price and lowest FSS/Big Four prices were strong predictors of MFP (p<0.01). On average, there was a 40% reduction between the lowest FSS/Big Four prices and the MFP for the first-round drugs (-23% to 80%). The inherent variability of list pricing, and approaches taken to negotiating price outcomes mean that a probabilistic approach (Monte Carlo simulation) is the most appropriate way to predict MFP prices.
CONCLUSIONS: Combining outcomes from both CMS negotiation rounds confirms that two price benchmarks - WAC and lowest FSS/Big 4 price - remain strong predictors of MFP, despite administration shifts. The greater dataset improves predictive reliability, particularly for lower-cost drugs, and the refined model offers a practical tool to anticipate round three and future MFPs.