| Autor: |
Luke K. Fostvedt, Dana J. Nickens, Weiwei Tan, Kourosh Parivar |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
CPT: Pharmacometrics & Systems Pharmacology, Vol 11, Iss 9, Pp 1256-1267 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2163-8306 |
| DOI: |
10.1002/psp4.12841 |
| Popis: |
Abstract Dacomitinib is a second‐generation, irreversible EGFR tyrosine kinase inhibitor for first‐line treatment of patients with metastatic non‐small cell lung cancer and EGFR‐activating mutations. A high rate of dose reductions in the pivotal trial led to an observed inverse exposure‐response (ER) relationship with the primary end points. Three ER models were developed to determine if the starting dose from the pivotal trial, 45 mg once daily (q.d.) dose, is appropriate: a longitudinal logistic regression model for adverse event‐related dose changes, a Claret tumor growth inhibition (TGI) model, and a Cox model for progression‐free survival (PFS) based on the TGI model predictions. This analysis included 266 patients taking dacomitinib with a starting dose of 45 mg (N = 250) or 30 mg (N = 16) q.d. The ER relationships with the time‐varying exposure metrics, most recent maximum plasma concentration (Cmax) and average concentration (Cavg) from the first dose, were established for the dose reduction and TGI models, respectively. The TGI model characterized the tumor inhibition over time with constant growth rate (kL = 0.0012 years−1) and highly variable kill rate (kD = 1.002 years−1/[μg/L]θcavg, coefficient of variation [CV] = 89%) and drug resistance (λ = 14.47 years−1, CV = 96%) leading to prolonged tumor shrinkage. The ER relationship was characterized using an exposure parameter with a power parameterization (θcavg = 0.454, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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