Budget impact analysis of niraparib and olaparib for maintenance treatment of platinum-sensitive, recurrent ovarian cancer in the US.

AIMS: This study aimed to evaluate the budget impact of niraparib and olaparib in patients with platinum-sensitive, recurrent ovarian cancer from a US third party payer perspective.

MATERIALS AND METHODS: A budget impact model was constructed to assess the additional per member per month (PMPM) costs associated with the introduction of niraparib and olaparib, two poly ADP-ribose polymerase ribose polymerase (PARP) inhibitors recently approved to be used in platinum-sensitive, recurrent ovarian cancer patients with and without a gBRCA mutation. The model assessed both pharmacy costs and medical costs. Pharmacy costs included adjusted drug costs, coinsurance, and dispensing fees. Medical costs included costs associated with disease monitoring and management of adverse events from the treatment. Epidemiological data from the literature were used to estimate the target population size. The analysis used 1-year time frame, and patients were assumed on treatment until disease progression or death. All costs were computed in 2017 USD. One-way sensitivity analyses were conducted to evaluate the model robustness.

RESULTS: In a hypothetical plan of 1,000,000 members, 206 patients were estimated to be potential candidates for niraparib or olaparib maintenance treatment after applying all epidemiological parameters. At listed 30-day supply WAC prices of $14,750 for niraparib and $13,482 for olaparib, budget impacts of these two drugs were $0.169 PMPM and $0.156 PMPM, respectively, most of which were contributed by pharmacy costs. Sensitivity analyses suggested that assumptions around market share, platinum-sensitive rate after first treatment, and WAC prices affected results the most.

LIMITATIONS: In this model, it was assumed that adopting niraparib and olaparib would not affect utilization of existing medications. Also, the estimated clinical parameters from clinical trials could differ from real-world data.