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Population Pharmacokinetic Modeling of Adavosertib (AZD1775) in Patients with Solid Tumors.
Johnson, Martin; Kaschek, Daniel; Ghiorghiu, Dana; Lanke, Shankar; Miah, Kowser; Schmidt, Henning; Mugundu, Ganesh M.
Afiliación
  • Johnson M; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Science, R&D, AstraZeneca, Cambridge, UK.
  • Kaschek D; IntiQuan GmbH, Basel, Switzerland.
  • Ghiorghiu D; Global Medicines Development, Late-Stage Development, Oncology R&D, AstraZeneca, Cambridge, UK.
  • Lanke S; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Boston, MA, USA.
  • Miah K; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Boston, MA, USA.
  • Schmidt H; IntiQuan GmbH, Basel, Switzerland.
  • Mugundu GM; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Boston, MA, USA.
J Clin Pharmacol ; 2024 Jul 19.
Article en En | MEDLINE | ID: mdl-39031510
ABSTRACT
Adavosertib (AZD1775) is a potent small-molecule inhibitor of Wee1 kinase. This analysis utilized pharmacokinetic data from 8 Phase I/II studies of adavosertib to characterize the population pharmacokinetics of adavosertib in patients (n = 538) with solid tumors and evaluate the impact of covariates on exposure. A nonlinear mixed-effects modeling approach was employed to estimate population and individual parameters from the clinical trial data. The model for time dependency of apparent clearance (CL) was developed in a stepwise manner and the final model validated by visual predictive checks (VPCs). Using an adavosertib dose of 300 mg once daily on a 5 days on/2 days off dosing schedule given 2 weeks out of a 3-week cycle, simulation analyses evaluated the impact of covariates on the following exposure metrics at steady state maximum concentration during a 21-day cycle, area under the curve (AUC) during a 21-day cycle, AUC during the second week of a treatment cycle, and AUC on day 12 of a treatment cycle. The final model was a linear 2-compartment model with lag time into the dosing compartment and first-order absorption into the central compartment, time-varying CL, and random effects on all model parameters. VPCs and steady-state observations confirmed that the final model satisfied all the requirements for reliable simulation of randomly sampled Phase I and II populations with different covariate characteristics. Simulation-based analyses revealed that body weight, renal impairment status, and race were key factors determining the variability of drug-exposure metrics.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Clin Pharmacol Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Clin Pharmacol Año: 2024 Tipo del documento: Article