Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic–Pharmacodynamic Analysis

Remimazolam, widely used for procedural sedation and general anesthesia, is a new ultra short-acting benzodiazepine for intravenous sedation and anesthesia. We aim to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam and its metabolite CNS 7054 in healthy Chinese volunteers using population analysis and suggest an optimal dosing regimen for sedation therapy. Data were collected from a single-center, placebo-controlled, randomized, and dose-escalation clinical pharmacology study. Forty-six healthy volunteers received a single infusion dose of remimazolam, while nine healthy subjects received a continuous infusion of remimazolam. A population PK/PD model was established and RxODE and Shiny in R were used to design the remimazolam dosing regimens. A three-compartment model best described the PK of remimazolam and a two-compartment model with one transit compartment was adopted for CNS 7054. The relationship between exposure and the bispectral index was best described using an effect compartment model with an inhibitory sigmoid model. Additionally, a web-based dashboard was developed to provide individualized dosing regimens, complemented by a graphical illustration of the PK/PD profiles of the proposed dosing regimen. The established population PK/PD model characterized the dose-exposure-response relationship of remimazolam well, which could be applied to optimize individual dosing regimens.

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Publication Details

Journal
Pharmaceutics
Published
2024-08-26
DOI
https://doi.org/10.3390/pharmaceutics16091122
Citations
5
Primary Topic
Epilepsy research and treatment
Type
article
Field-Weighted Citation Impact
1.93
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article

Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic–Pharmacodynamic Analysis

Xiaochun Zheng, Zheng Jiao, Cansheng Gong, Yueting Chen et al.
5 citations
Pharmaceutics
Epilepsy research and treatment
1.93
article

Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic–Pharmacodynamic Analysis

Xiaochun Zheng, Zheng Jiao, Cansheng Gong, Yueting Chen, Feng Liu
article en
5 citations

Abstract

Remimazolam, widely used for procedural sedation and general anesthesia, is a new ultra short-acting benzodiazepine for intravenous sedation and anesthesia. We aim to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam and its metabolite CNS 7054 in healthy Chinese volunteers using population analysis and suggest an optimal dosing regimen for sedation therapy. Data were collected from a single-center, placebo-controlled, randomized, and dose-escalation clinical pharmacology study. Forty-six healthy volunteers received a single infusion dose of remimazolam, while nine healthy subjects received a continuous infusion of remimazolam. A population PK/PD model was established and RxODE and Shiny in R were used to design the remimazolam dosing regimens. A three-compartment model best described the PK of remimazolam and a two-compartment model with one transit compartment was adopted for CNS 7054. The relationship between exposure and the bispectral index was best described using an effect compartment model with an inhibitory sigmoid model. Additionally, a web-based dashboard was developed to provide individualized dosing regimens, complemented by a graphical illustration of the PK/PD profiles of the proposed dosing regimen. The established population PK/PD model characterized the dose-exposure-response relationship of remimazolam well, which could be applied to optimize individual dosing regimens.

PharmaceuticsVol. 16(9)
Fujian Medical University (CN), Shanghai Jiao Tong University (CN), Shanghai Chest Hospital (CN), Humanwell Healthcare Group (China) (CN), Fujian Provincial Hospital (CN), Fuzhou University (CN)
Good health and well-being
Openalex Percentile: Top 13%
Epilepsy research and treatment
1.93
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Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic–Pharmacodynamic Analysis — Xiaochun Zheng, Zheng Jiao, et al. · Pharmaceutics (2024) | TGRS Research Map | TGRS