Inter- and intra-subject variability of metoprolol kinetics after intravenous administration

Richard, J.; Jm, Cardot; Godbillon, J.

doi:10.1007/bf03188836

Cited by 11 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Comparison between observed and simulated concentration vs time profiles after IV administration of metoprolol at doses (in mg) of (a) 20, 33 (b) 20, 35 (c) 15, 36 (d) 5, 34 (e) 10, 34 (f) 15, 34 (g) 20, 34 (h) 88.5, 37 and (i) 5 32 correspondingly. Red colored dots describe the reported data values, the mean solidified line indicates the simulated data values, the dashed line represents the maximum and minimum values, and the dotted line depicts the 5th and 95th percentiles.…”

Section: Resultsmentioning

confidence: 99%

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

et al. 2023

View full text Add to dashboard Cite

The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (R obs/pre), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean R obs/pre ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients.

show abstract

Section: Resultsmentioning

confidence: 99%

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The number of subjects in arm A was selected to ensure appropriate power for statistical analysis of the metoprolol data, as this substance has the largest intraindividual variability among the CYP substrates in this study [12,13]. Allowing for a CVof 25% for AUC∞, a sample size of 18 gives a power of more than 90% to conclude that there is at most a weak inhibition at the 0.05 significance level, given boundaries for weak inhibition of 0.5 and 2.0 [7,11], and assuming that the magnitude of inhibition corresponds to a true AUC ratio of 1.5.…”

Section: Sample Sizementioning

confidence: 99%

Non randomized study on the potential of nitisinone to inhibit cytochrome P450 2C9, 2D6, 2E1 and the organic anion transporters OAT1 and OAT3 in healthy volunteers

Huledal

Olsson

Önnestam

et al. 2018

Eur J Clin Pharmacol

View full text Add to dashboard Cite

Purpose Nitisinone inhibits the cytochrome P450 (CYP) subfamilies CYP2C9, CYP2D6, and CYP2E1 and the organic anion transporter (OAT) isoforms OAT1 and OAT3 in vitro. Since the effect of nitisinone on these enzymes and transporters in humans is still unknown, the purpose of this study was to evaluate the effect of nitisinone on these CYP subfamilies and OAT isoforms. Methods This was an open-label, nonrandomized, two-arm, phase 1 study (EudraCT: 2016-004297-17) in healthy volunteers. The substrates (tolbutamide, metoprolol, and chlorzoxazone for the respective CYPs and furosemide for the OATs) were administered as single doses, before and after 15 days of once daily dosing of 80 mg nitisinone, to determine the AUC ∞ ratios ([substrate+nitisinone]/[substrate]). Nitisinone pharmacokinetics, safety, and tolerability were also assessed, and blood and urine were collected to determine substrate and nitisinone concentrations by LC-MS/MS. Results Thirty-six subjects were enrolled with 18 subjects included in each arm. The least square mean ratio (90% confidence interval) for AUC ∞ was 2.31 (2.11-2.53) for tolbutamide, 0.95 (0.88-1.03) for metoprolol, 0.73 (0.67-0.80) for chlorzoxazone, and 1.72 (1.63-1.81) for furosemide. Clinically relevant nitisinone steady-state concentrations were reached after 12 days: mean C av,ss of 94.08 μM. All treatments were well tolerated, and no safety concerns were identified. Conclusions Nitisinone did not affect CYP2D6 activity, was a weak inducer of CYP2E1, and was a weak inhibitor of OAT1 and OAT3. Nitisinone was a moderate inhibitor of CYP2C9, and treatment may therefore result in increased plasma concentrations of comedications metabolized primarily via this enzyme. Clinical trial registry identification EudraCT 2016-004297-17.

show abstract

“…The second stage is a summary analysis that quanti®es the population mean pharmacokinetic response, IOV and ISV. Numerous applications of such an approach have been reported (Upton et al, 1982;Richard et al, 1994). However, methods that analyse all available data simultaneously are generally preferred over such two-stage approaches as the latter require rich data, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The work presented herein was primarily motivated by a multioccasion data set that came from a pharmacokinetic study (Richard et al, 1994) of the drug metoprolol, a 1 -selective adrenoreceptor antagonist used for the treatment of cardiac disease. The data are measurements of metoprolol concentration in the blood plasma of 18 healthy human subjects who had received a single 5-min intravenous infusion of approximately 43 "mol metoprolol on each of two occasions.…”

Section: Introductionmentioning

confidence: 99%

Markov Chain Monte Carlo Techniques for Studying Interoccasion and Intersubject Variability: Application to Pharmacokinetic Data

Lunn

Aarons

1997

Journal of the Royal Statistical Society Series C: Applied Statistics

View full text Add to dashboard Cite

Values of pharmacokinetic parameters may seem to vary randomly between dosing occasions. An accurate explanation of the pharmacokinetic behaviour of a particular drug within a population therefore requires two major sources of variability to be accounted for, namely interoccasion variability and intersubject variability. A hierarchical model that recognizes these two sources of variation has been developed. Standard Bayesian techniques were applied to this statistical model, and a mathematical algorithm based on a Gibbs sampling strategy was derived. The accuracy of this algorithm's determination of the interoccasion and intersubject variation in pharmacokinetic parameters was evaluated from various population analyses of several sets of simulated data. A comparison of results from these analyses with those obtained from parallel maximum likelihood analyses (NONMEM) showed that, for simple problems, the outputs from the two algorithms agreed well, whereas for more complex situations the NONMEM approach may be less accurate. Statistical analyses of a multioccasion data set of pharmacokinetic measurements on the drug metoprolol (the measurements being of concentrations of drug in blood plasma from human subjects) revealed substantial interoccasion variability for all structural model parameters. For some parameters, interoccasion variability appears to be the primary source of pharmacokinetic variation.

show abstract

Inter- and intra-subject variability of metoprolol kinetics after intravenous administration

Cited by 11 publications

References 10 publications

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

Non randomized study on the potential of nitisinone to inhibit cytochrome P450 2C9, 2D6, 2E1 and the organic anion transporters OAT1 and OAT3 in healthy volunteers

Markov Chain Monte Carlo Techniques for Studying Interoccasion and Intersubject Variability: Application to Pharmacokinetic Data

Contact Info

Product

Resources

About