Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel®

Dansirikul, C.; Choi, Malcolm; Duffull, Stephen B.

doi:10.1016/j.compbiomed.2004.02.008

Cited by 36 publications

(16 citation statements)

References 13 publications

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“…[12]. Owing to the limited information on two‐compartment model parameters, these were also derived [25] from noncompartmental variables reported in a study by Brattstrom et al. [11].…”

Section: Methodsmentioning

confidence: 99%

A Bayesian approach for population pharmacokinetic modelling of sirolimus

Dansirikul

Morris

Tett

et al. 2005

Brit J Clinical Pharma

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AimsTo explore a Bayesian approach for the pharmacokinetic analysis of sirolimus concentration data arising from therapeutic drug monitoring (poorly informative concentration-time point design), and to explore possible covariate relationships for sirolimus pharmacokinetics. MethodsSirolimus concentration-time data were available as part of routine clinical care from 25 kidney transplant recipients. Most samples were taken at or near the trough time point at steady state. The data were analyzed using a fully conditional Bayesian approach with PKBUGS (v 1.1)/WinBUGS (v 1.3). Features of the data included noncompliance and missing concentration measurements below the limit of sensitivity of the assay. Informative priors were used. ResultsA two-compartment model with proportional residual error provided the best fit to the data (consisting of 315 sirolimus concentration-time points). The typical value for the apparent clearance (CL/ F ) was 12.5 l h − 1 at the median age of 44 years. Apparent CL was found to be inversely related to age with a posterior probability of a clinically significant effect of 0.734. ConclusionsA population pharmacokinetic model was developed for sirolimus using a novel approach. Bayesian modelling with informative priors allowed interpretation of a significant covariate relationship, even using poorly informative data.

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“…[12]. Owing to the limited information on two‐compartment model parameters, these were also derived [25] from noncompartmental variables reported in a study by Brattstrom et al. [11].…”

Section: Methodsmentioning

confidence: 99%

A Bayesian approach for population pharmacokinetic modelling of sirolimus

Dansirikul

Morris

Tett

et al. 2005

Brit J Clinical Pharma

Self Cite

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“…The pharmacokinetic parameters were obtained using pharmacokinetic software PKSolver . The area under the concentration–time curve (AUC 0–t ) from zero to the last time point, mean residence time (MRT), elimination half‐life time ( T 1/2 ), peak time ( T max ), volume of distribution (Vd), plasma clearance (CL) and peak concentration ( C max ) of the drug were all obtained.…”

Section: Methodsmentioning

confidence: 99%

Development of an enteric nanoparticle of marine sulfated polysaccharide propylene glycol alginate sodium sulfate for oral administration: formulation design, pharmacokinetics and efficacy

Hao

et al. 2018

Journal of Pharmacy and Pharmacology

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“…PKSolver software [21,22] was employed to analyze the plasma concentration-time data with noncompartmental analysis (NCA). The area under the concentration–time curve (AUC 0–t ) from zero to the last time point, mean residence time (MRT), elimination half-life time ( T 1/2 ), volume of distribution (Vd), plasma clearance (Cl), peak concentration ( C max ) and the time to peak concentration ( T max ) of the drug were all obtained.…”

Section: Methodsmentioning

confidence: 99%

An HPLC Method for Microanalysis and Pharmacokinetics of Marine Sulfated Polysaccharide PSS-Loaded Poly Lactic-co-Glycolic Acid (PLGA) Nanoparticles in Rat Plasma

Li¹,

Li²,

Xue³

et al. 2013

Marine Drugs

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This study was aimed at developing a sensitive and selective HPLC method with postcolumn fluorescence derivatization for the detection of propylene glycol alginate sodium sulfate (PSS) in rat plasma. Plasma samples were prepared by a simple and fast ultrafiltration method. PSS was extracted from rat plasma with d-glucuronic acid as internal standard. Isocratic chromatographic separation was performed on a TSKgel G2500 PWxL column with the mobile phase of 0.1 M sodium sulfate at a flow rate of 0.5 mL/min. Analyte detection was achieved by fluorescence detection (FLD) at 250 nm (excitation) and 435 nm (emission) using guanidine hydrochloride as postcolumn derivatizing reagent in an alkaline medium at 120 °C. The calibration curve was linear over a concentration range of 1–500 μg/mL, and the lower limit of detection (LLOD) was found to be 250 ng/mL. This validated method was applied successfully to the pharmacokinetic study of PSS and PSS-loaded poly lactic-co-glycolic acid (PLGA) nanoparticles (PSS-NP) in rat plasma after a single intravenous (PSS only) and oral administration (PSS and PSS-NP). Significant differences in the main pharmacokinetic parameters of PSS and PSS-NP were observed. The relative bioavailability of PSS-NP was 190.10% compared with PSS which shows that PSS-NP can improve oral bioavailability.

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Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel®

Cited by 36 publications

References 13 publications

A Bayesian approach for population pharmacokinetic modelling of sirolimus

A Bayesian approach for population pharmacokinetic modelling of sirolimus

Development of an enteric nanoparticle of marine sulfated polysaccharide propylene glycol alginate sodium sulfate for oral administration: formulation design, pharmacokinetics and efficacy

An HPLC Method for Microanalysis and Pharmacokinetics of Marine Sulfated Polysaccharide PSS-Loaded Poly Lactic-co-Glycolic Acid (PLGA) Nanoparticles in Rat Plasma

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