Precision in Pharmacoeconomics: A Comparative Cost-Utility Analysis of Osimertinib in EGFR-Mutant NSCLC Using Traditional and Pharmacometric Models
Therapeutic Innovation and Regulatory Science, cilt.60, sa.4, ss.1084-1093, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 60 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s43441-026-00960-w
- Dergi Adı: Therapeutic Innovation and Regulatory Science
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, Library, Information Science & Technology Abstracts (LISTA), MEDLINE
- Sayfa Sayıları: ss.1084-1093
- Anahtar Kelimeler: Cost-Utility analysis, EGFR-Mutant NSCLC, Health technology assessment, Osimertinib, Pharmacoeconomic modeling, Pharmacometric model
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
Health technology assessments (HTAs) for targeted therapies like osimertinib require models capturing individual pharmacokinetic/pharmacodynamic (PK/PD) variability in epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC). Traditional models often fail to reflect these dynamics adequately. This study compared a pharmacometric-based model (PMX) with four traditional frameworks to evaluate osimertinib’s cost-utility from a healthcare payer perspective. A virtual cohort of 1,000 patients with advanced EGFR-mutant NSCLC received osimertinib (80 mg daily) or comparator first-line EGFR-tyrosine kinase inhibitors (gefitinib/erlotinib). Models projected survival, adverse events (AEs), quality-adjusted life years (QALYs), and costs over 2 years. ICURs used PMX as benchmark. The PMX model yielded highest QALYs (1.48) and lowest ICUR ($82,000/QALY vs. comparator), outperforming traditional models (ICUR deviations − 21% to + 40%). TTE exponential showed most divergence due to constant hazard. Probabilistic analyses confirmed PMX superiority across willingness-to-pay thresholds. Pharmacometric models, enabling individualized dosing and exposure-driven effects, provide more biologically plausible estimates, supporting their integration into HTAs for precision oncology.