Population Pharmacokinetic Modeling of Adavosertib (AZD1775) in Patients with Solid Tumors

Author:

Johnson Martin1ORCID,Kaschek Daniel2ORCID,Ghiorghiu Dana3,Lanke Shankar4ORCID,Miah Kowser4,Schmidt Henning2,Mugundu Ganesh M.4ORCID

Affiliation:

1. Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology and Safety Science R&D AstraZeneca Cambridge UK

2. IntiQuan GmbH Basel Switzerland

3. Global Medicines Development Late‐Stage Development Oncology R&D AstraZeneca Cambridge UK

4. Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca Boston MA USA

Abstract

AbstractAdavosertib (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.

Funder

AstraZeneca

Merck

Publisher

Wiley

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