Presentation (CTI)

OBJECTIVES: Under the Inflation Reduction Act (IRA) of 2022, the Centers for Medicare & Medicaid Services (CMS) negotiated the maximum fair prices (MFPs) that Medicare beneficiaries and plan sponsors will pay for the first 10 high-expenditure drugs, effective from January 2026. As negotiations for an additional 15 drugs are now underway, we sought to develop a model for estimating MFPs using the first-round drugs as a benchmark.
METHODS: We conducted a series of regression analyses using data from the first round of negotiated drugs. Key variables included sources available at the time of analysis, including 2023 Wholesale Acquisition Cost (WAC), Medicare 2022 Part D spending, and 2023 Federal Supply Schedule (FSS)/Big Four prices.
RESULTS: The analysis set (n=10) showed an average WAC price reduction of 63%, with a range of 38% to 79%. The regression model incorporating the key variables explained 99% of the variance in the MFP outcome (R² = 0.99), specifically the lowest price of FSS/Big Four was the strongest predictor of MFP (p<0.01). On average, there was a 36% reduction between the lowest FSS/Big Four price and the MFP for the first-round drugs (-8% to 72%). 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: Despite the limited sample size, the analysis revealed a strong positive correlation between the MFP and the lowest FSS/Big Four price. This relationship may serve as a useful benchmark for estimating future discounts under upcoming Medicare price negotiations. Continued monitoring will be critical to interpreting the evolving pricing environment, especially as second-round MFPs are published, and the broader ripple effects of most-favoured-nation pricing begin to emerge.