Early Cost-Effectiveness and Price Threshold Analyses of Resmetirom: An Investigational Treatment for Management of Nonalcoholic Steatohepatitis

Pharmacoecon Open. 2023 Jan;7(1):93-110. doi: 10.1007/s41669-022-00370-2. Epub 2022 Sep 14.

Abstract

Background: Nonalcoholic steatohepatitis (NASH) is characterized by inflammation and hepatocellular damage caused by accumulation of fat in the liver. Resmetirom (MGL-3196) is an orally administered, small-molecule, liver-targeted, selective thyroid hormone receptor-β agonist. This early analysis explored the potential cost effectiveness of resmetirom for the treatment of NASH from a US commercial payer perspective.

Methods: An early economic model was developed to reflect the clinical pathways typically followed by patients with NASH and liver fibrosis. Use of resmetirom, compared with placebo, was assessed. The Markov model structure was informed by a previous modeling study and a randomized, double-blind, placebo-controlled, phase II trial of resmetirom. Costs and outcomes were assessed over a lifetime time horizon with results presented in terms of cost per quality-adjusted life-year (QALY) gained.

Results: Resmetirom treatment resulted in increased costs of US$66,764 per patient, while increasing QALYs by 1.24. The incremental cost-effectiveness ratio was US$53,929 per QALY gained, indicating resmetirom treatment would potentially be cost effective at a willingness-to-pay (WTP) threshold of US$100,000. Results indicated that resmetirom would reduce the lifetime number of cases of decompensated cirrhosis (- 87), hepatocellular carcinoma (- 59), and liver transplants (- 30) per 1,000 patients compared with placebo. Resmetirom treatment remained cost effective at a US$100,000 WTP threshold up to a daily price point of US$72.00.

Conclusion: Resmetirom is a potentially cost-effective treatment option for patients with NASH and liver fibrosis based on an analysis performed from a US commercial payer perspective. Future economic analyses of the technology should, however, focus on overcoming the limitations of existing modeling methodology.