Martin Johnson PhD, Daniel Kaschek PhD, Dana Ghiorghiu MD, PhD, Shankar Lanke PhD, Kowser Miah PhD, Henning Schmidt PhD, Ganesh M. Mugundu PhD
{"title":"Adavosertib (AZD1775)在实体瘤患者中的群体药代动力学模型。","authors":"Martin Johnson PhD, Daniel Kaschek PhD, Dana Ghiorghiu MD, PhD, Shankar Lanke PhD, Kowser Miah PhD, Henning Schmidt PhD, Ganesh M. Mugundu PhD","doi":"10.1002/jcph.2492","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"64 11","pages":"1419-1431"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Population Pharmacokinetic Modeling of Adavosertib (AZD1775) in Patients with Solid Tumors\",\"authors\":\"Martin Johnson PhD, Daniel Kaschek PhD, Dana Ghiorghiu MD, PhD, Shankar Lanke PhD, Kowser Miah PhD, Henning Schmidt PhD, Ganesh M. Mugundu PhD\",\"doi\":\"10.1002/jcph.2492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":22751,\"journal\":{\"name\":\"The Journal of Clinical Pharmacology\",\"volume\":\"64 11\",\"pages\":\"1419-1431\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Clinical Pharmacology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jcph.2492\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Clinical Pharmacology","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcph.2492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Population Pharmacokinetic Modeling of Adavosertib (AZD1775) in Patients with Solid Tumors
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.