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Background: Zolpidem is a non-benzodiazepine hypnotic agent which has been shown to be effective in inducing and maintaining sleep in adults and is one of the most frequently prescribed hypnotics in the world. For drugs that are used to treat sleeping disorders, the time to reach the maximum concentration (T) of the drug in plasma is important to achieving a fast onset of action and this must be maintained when switching from one product to another.
Objectives: The main objective of the present work was to create a PBPK/PD model for zolpidem and establish a clinically relevant "safe space" for dissolution of zolpidem from the commercial immediate release (IR) formulation. A second objective was to analyze literature pharmacokinetic data to verify the negative food effect ascribed to zolpidem and consider its ramifications in terms of the "safe space" for dissolution.
Methods: Using dissolution, pharmacokinetic and pharmacodynamic data, an integrated PBPK/PD model for immediate release zolpidem tablets was constructed in Simcyp®. This model was used to identify the clinically relevant dissolution specifications necessary to ensure efficacy.
Results: According to the simulations, as long as 85% of the drug is released in 45 minutes or less, the impact on the PK and PD profiles of zolpidem would be minimal. According to the FDA, the drug has to dissolve from the test and reference products at a similar rate and to an extent of 85% in not more than 30 minutes to pass bioequivalence via the BCS-biowaiver test. Thus, the BCS-biowaiver specifications are somewhat more stringent than the "safe space" based on the PBPK/PD model. Published data from fasted and fed state pharmacokinetic studies suggest but do not prove a negative food effect of zolpidem.
Conclusions: A PBPK/PD model indicates that current BCS-biowaiver criteria are more restrictive for immediate release zolpidem tablets than they need to be. In view of the close relationship between PK and PD, it remains advisable to avoid taking zolpidem tablets with or immediately after a meal, as indicated by the Stilnox® labeling.
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http://dx.doi.org/10.1016/j.ejps.2020.105534 | DOI Listing |
ADMET DMPK
July 2024
Department of Industrial & Physical Pharmacy, Purdue University, West Lafayette, Indiana, United States.
Background And Purpose: In our previous studies, drug nanocrystals were directly prepared by solution crystallization, possessing uniform particle size and morphology suitable for intravenous (IV) injection. These nanocrystals accumulated in a small percentage of their injected dose in tumor-bearing mice but showed similar anti-tumor effectiveness and much-reduced side effects compared with current commercial solubilized and encapsulated delivery systems.
Experimental Approach: In this study, we aimed to delineate possible controlling factors for the pharmacokinetics (PK) and biodistribution behaviors of paclitaxel (PTX) nanocrystals tested in mice by applying physiologically based pharmacokinetics (PBPK) modeling, coupled with pharmacodynamics (PD) simulation, to the data.
Sci Rep
August 2024
Department of Pharmacy, The Third People's Hospital of Chengdu, College of Medicine, Southwest Jiaotong University, Chengdu, China.
This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD) of meropenem for critically ill patients. A PBPK model of meropenem in healthy adults was established using PK-Sim software and subsequently extrapolated to critically ill patients based on anatomic and physiological parameters. The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between predicted and observed values of pharmacokinetic parameters C, AUC, and CL to evaluate the accuracy of the PBPK model.
View Article and Find Full Text PDFCurr Opin Clin Nutr Metab Care
November 2024
Department of Biomedical Physiology and Kinesiology.
J Clin Pharmacol
November 2024
Bayer HealthCare SAS, Loos, France, on behalf of: Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer, AG, Germany.
As detailed information on the pharmacokinetics (PK) of labetalol in pregnant people are lacking, the aims of this study were: (1) to build a physiologically based PK (PBPK) model of labetalol in non-pregnant individuals that incorporates different CYP2C19 genotypes (specifically, *1/*1, *1/*2 or *3, *2/*2, and *17/*17); (2) to translate this model to the second and third trimester of pregnancy; and (3) to combine the model with a previously published direct pharmacodynamic (PD) model to predict the blood pressure lowering effect of labetalol in the third trimester. Clinical data for model evaluation was obtained from the scientific literature. In non-pregnant populations, the mean ratios of simulated versus observed peak concentration (C), time to reach C (T), and exposure (area under the plasma concentration-time curve, AUC) were 0.
View Article and Find Full Text PDFCPT Pharmacometrics Syst Pharmacol
September 2024
Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, Florida, USA.
This study employed physiologically-based pharmacokinetic-pharmacodynamics (PBPK/PD) modeling to predict the effect of obesity and gastric bypass surgery on the pharmacokinetics and intragastric pH following omeprazole treatment. The simulated plasma concentrations closely matched the observed data from non-obese, morbidly obese, and post-gastric bypass populations. Obesity significantly reduces CYP3A4 and CYP2C19 activities, as reflected by the metabolic ratio [omeprazole sulphone]/[omeprazole] and [5-hydroxy-omeprazole]/[omeprazole].
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