BUDGET IMPACT AND COST CALCULATOR MODEL FOR POHERDY® (PERTUZUMAB-DPZB) IN THE TREATMENT OF HER2-POSITIVE BREAST CANCER
Author(s)
Ricardo Bueno, BA, MHA, PhD1, Yohanna Ramires, MSc2, Nanci Utida, MD1, Rafael Gama, MBA, MD3, Brian Seagrave, MPharm, MSc4, Cassaundra Johnson, MSHA RN, CMCN5, Susana Guitar Jiménez, MSc, MBA, MHE6, Delphine Courmier, BSc, MBA, MSc, PhD7;
1Organon Farmaceutica, Sao Paulo, Brazil, 2Organon, Fazenda Rio Grande, Brazil, 3Unimed Blumenau, CURITIBA, Brazil, 4Amaris Consulting, Toronto, ON, Canada, 5Organon, Plymouth Meeting, PA, USA, 6Organon, Global Market Access & Pricing, Plymouth Meeting, PA, USA, 7Organon, Executive Director Global Pricing & Biosimilars Market Access, Florham Park, NJ, USA
1Organon Farmaceutica, Sao Paulo, Brazil, 2Organon, Fazenda Rio Grande, Brazil, 3Unimed Blumenau, CURITIBA, Brazil, 4Amaris Consulting, Toronto, ON, Canada, 5Organon, Plymouth Meeting, PA, USA, 6Organon, Global Market Access & Pricing, Plymouth Meeting, PA, USA, 7Organon, Executive Director Global Pricing & Biosimilars Market Access, Florham Park, NJ, USA
OBJECTIVES: To estimate the budget impact of POHERDY®, a pertuzumab biosimilar, from the perspective of the Brazilian private healthcare payer. This analysis evaluates changes in total healthcare expenditure following POHERDY® adoption, accounting for treatment uptake, dosing requirements, market share dynamics, and relevant cost components. In addition, per-patient total treatment costs are estimated using the CCM, incorporating key population characteristics such as patient weight (drug costs) and IV infusion time (administration costs).
METHODS: The model calculated the total number of eligible patients and total costs associated with two scenarios: the reference scenario reflecting current clinical practice without POHERDY® and the new scenario with POHERDY® available. The following steps were taken in a budget impact analysis: 1. Identifying the pertuzumab-eligible target population eligible, with patient numbers estimated based on forecasted sales data. This population includes patients with early and metastatic HER2-positive breast cancer. 2. Estimating the market shares of each comparator and the number of patients receiving each intervention. 3. Determining treatment-related costs for reference and new scenario. Additional CCM steps included: 1. Dividing the cohort into weight bands, where each band reflects relevant clinical cut-offs. 2. Determining the relative cost per patient of each formulation, accounting for personalized dosing based on patient’s weight.
RESULTS: The adoption of POHERDY® is projected to generate net budget savings for Brazilian private payers over the 3-year horizon, driven primarily by reduced drug acquisition costs relative to the reference product. Savings were sensitive to biosimilar uptake and market share assumptions, as well as real-world administration practices.
CONCLUSIONS: The CCM identified patient weight distribution and IV infusion duration as key drivers. Weight-based IV dosing resulted in lower costs for lighter patients, whereas fixed-dose SC regimens became slightly more favorable in higher-weight patients. Deterministic sensitivity analyses confirmed drug pricing and biosimilar uptake as the most influential parameters, with savings maintained across scenarios.
METHODS: The model calculated the total number of eligible patients and total costs associated with two scenarios: the reference scenario reflecting current clinical practice without POHERDY® and the new scenario with POHERDY® available. The following steps were taken in a budget impact analysis: 1. Identifying the pertuzumab-eligible target population eligible, with patient numbers estimated based on forecasted sales data. This population includes patients with early and metastatic HER2-positive breast cancer. 2. Estimating the market shares of each comparator and the number of patients receiving each intervention. 3. Determining treatment-related costs for reference and new scenario. Additional CCM steps included: 1. Dividing the cohort into weight bands, where each band reflects relevant clinical cut-offs. 2. Determining the relative cost per patient of each formulation, accounting for personalized dosing based on patient’s weight.
RESULTS: The adoption of POHERDY® is projected to generate net budget savings for Brazilian private payers over the 3-year horizon, driven primarily by reduced drug acquisition costs relative to the reference product. Savings were sensitive to biosimilar uptake and market share assumptions, as well as real-world administration practices.
CONCLUSIONS: The CCM identified patient weight distribution and IV infusion duration as key drivers. Weight-based IV dosing resulted in lower costs for lighter patients, whereas fixed-dose SC regimens became slightly more favorable in higher-weight patients. Deterministic sensitivity analyses confirmed drug pricing and biosimilar uptake as the most influential parameters, with savings maintained across scenarios.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
EE57
Topic
Economic Evaluation
Topic Subcategory
Budget Impact Analysis
Disease
SDC: Oncology, STA: Biologics & Biosimilars