Dataset Information

Limited sampling strategy for determining metformin area under the plasma concentration-time curve.

ABSTRACT: AIM:The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin. METHODS:Metformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS-derived AUC(0,24 h) was compared with the parameter 'best estimate' obtained by non-compartmental analysis using all plasma concentration data points. Correlation between the LSS-derived and the best estimated AUC(0,24 h) (r(2) ), bias and precision of the LSS estimates were quantified. The LSS models were validated in independent cohorts. RESULTS:A two-point (3 h and 10 h) regression equation with no intercept estimated accurately the individual AUC(0,24 h) in the development cohort: r(2) = 0.927, bias (mean, 95% CI) -0.5, -2.7-1.8% and precision 6.3, 4.9-7.7%. The accuracy of the two point LSS model was verified in study cohorts of individuals receiving single 500 or 1000 mg (r(2) = -0.933-0.934) or seven 1000 mg daily doses (r(2) = 0.918), as well as using data from 16 published studies covering a wide range of metformin doses, demographics, clinical and experimental conditions (r(2) = 0.976). The LSS model reproduced previously reported results for effects of polymorphisms in OCT2 and MATE1 genes on AUC(0,24 h) and renal clearance of metformin. CONCLUSIONS:The two point LSS algorithm may be used to assess the systemic exposure to metformin under diverse conditions, with reduced costs of sampling and analysis, and saving time for both subjects and investigators.

SUBMITTER: Santoro AB

PROVIDER: S-EPMC5137825 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Limited sampling strategy for determining metformin area under the plasma concentration-time curve.

Santoro Ana Beatriz AB Stage Tore Bjerregaard TB Struchiner Claudio José CJ Christensen Mette Marie Hougaard MM Brosen Kim K Suarez-Kurtz Guilherme G

British journal of clinical pharmacology 20160724 4

<h4>Aim</h4>The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin.<h4>Methods</h4>Metformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS-derived AUC(0,24 h) was compared with the parameter 'best estimate' obtained by non-compartmental analysis using all plasma concentration data point ...[more]

PMID: 27324407

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Project description:BACKGROUND:An extended-release, once-daily, oral formulation of tacrolimus is currently used after kidney transplantation as a substitute for the conventional twice-daily formulation. The purpose of this study was to provide a limited sampling strategy with minimum and optimum sampling points to predict the tacrolimus area under the concentration-time curve (AUC) after administration of once-daily tacrolimus in de novo adult kidney transplant patients. METHODS:A total of 36 adult Japanese kidney transplant patients receiving once-daily tacrolimus were included: 31 were allocated to a study group to develop limited sampling strategy (LSS) model equations based on multiple stepwise linear regression analysis, and 5 were allocated to a validation group to estimate the precision of the LSS equations developed by the study group. Twelve-hour AUC (AUC0-12) was calculated by the trapezoidal rule, and the relationship between individual concentration points and AUC0-12 were determined by multiple linear regression analysis. The coefficient of determination (R2) was used to assess the goodness-of-fit of the regression models. Three error indices (mean error, mean absolute error, and root mean squared prediction error) were calculated to evaluate predictive bias, accuracy, and precision, respectively. Quality of the statistical models was compared with Akaike's information criterion (AIC). RESULTS:A four-point model using C0, C2, C4 and C6 gave the best fit to predict AUC0-12 (R2 = 0.978). In the three- and two-point models, the best fits were at time points C2, C4, and C6 (R2 = 0.973), and C2 and C6 (R2 = 0.962), respectively. All three models reliably estimated tacrolimus AUC0-12, consistent with evaluations by the three error indices and Akaike's information criterion. Practically, the two-point model with C2 and C6 was considered to be the best combination, providing a highly accurate prediction and the lowest blood sampling frequency. CONCLUSIONS:The two-point model with C2 and C6 may be valuable in reducing the burden on patients, as well as medical costs, for once-daily tacrolimus monitoring.

Project description:BackgroundThe optimal dose of mycophenolate mofetil (MMF) in renal transplant patients has been recommended to be decided on the basis of area under the concentration-time curve (AUC0-12) of mycophenolic acid (MPA). Although meta-analysis has revealed that postoperative day (POD) is an influencing factor in MPA pharmacokinetics, there are no reports regarding a limited sampling strategy (LSS) for MPA AUC in consideration of POD. The aim of this study was to construct of an LSS considering POD that appropriately expresses the MPA AUC following renal transplantation and evaluation of the usefulness.MethodsSerum concentration-time profiles (measured AUC0-12) comprising nine sampling points over 12 h were analyzed in 36 living-donor renal transplant recipients after MMF administration with concomitant once-daily prolonged-release tacrolimus. Two LSSs were developed by stepwise multiple regression analysis (Method A: not classified by PODs; Method B: classified by PODs into POD?<?31 and POD???31). Each LSS comprised four blood-sampling points within 6 h after MMF administration. Precision and reliability were verified by using root-mean-square error (RMSE), correlation coefficient (R2), and coefficient of determination (q2) by using leave-one-out cross-validation. The absolute values of the difference between measured and estimated AUCs (delta AUC) were compared for both estimating equations.ResultsOne-hundred samples obtained from 36 recipients for AUC0-12 comprised POD?<?31 (n?=?39) and POD???31 (n?=?61). Estimation of AUC0-12 by Method B resulted in better accuracy and reliability (Method A: RMSE?=?5.5, R2?=?0.85, q2?=?0.83; Method B: POD?<?31: RMSE?=?5.5, R2?=?0.86, q2?=?0.83; POD???31: RMSE?=?3.9, R2?=?0.92, q2?=?0.89) and significantly lower median delta AUC compared with that by Method A (delta AUC: 2.6 (0.0-11.6) v.s. 3.9 (0.1-18.1), p?=?0.032).ConclusionThese results suggest that LSS, classified as POD?<?31 or POD?>?31, would provide more accurate and reliable estimation of MPA AUC0-12 in Japanese living-donor renal transplant patients.

Project description:BackgroundIncremental value (IncV) evaluates the performance change between an existing risk model and a new model. Different IncV metrics do not always agree with each other. For example, compared with a prescribed-dose model, an ovarian-dose model for predicting acute ovarian failure has a slightly lower area under the receiver operating characteristic curve (AUC) but increases the area under the precision-recall curve (AP) by 48%. This phenomenon of disagreement is not uncommon, and can create confusion when assessing whether the added information improves the model prediction accuracy.MethodsIn this article, we examine the analytical connections and differences between the AUC IncV (ΔAUC) and AP IncV (ΔAP). We also compare the true values of these two IncV metrics in a numerical study. Additionally, as both are semi-proper scoring rules, we compare them with a strictly proper scoring rule: the IncV of the scaled Brier score (ΔsBrS) in the numerical study.ResultsWe demonstrate that ΔAUC and ΔAP are both weighted averages of the changes (from the existing model to the new one) in separating the risk score distributions between events and non-events. However, ΔAP assigns heavier weights to the changes in higher-risk regions, whereas ΔAUC weights the changes equally. Due to this difference, the two IncV metrics can disagree, and the numerical study shows that their disagreement becomes more pronounced as the event rate decreases. In the numerical study, we also find that ΔAP has a wide range, from negative to positive, but the range of ΔAUC is much smaller. In addition, ΔAP and ΔsBrS are highly consistent, but ΔAUC is negatively correlated with ΔsBrS and ΔAP when the event rate is low.ConclusionsΔAUC treats the wins and losses of a new risk model equally across different risk regions. When neither the existing or new model is the true model, this equality could attenuate a superior performance of the new model for a sub-region. In contrast, ΔAP accentuates the change in the prediction accuracy for higher-risk regions.

Dataset Information

Limited sampling strategy for determining metformin area under the plasma concentration-time curve.

Publications

Limited sampling strategy for determining metformin area under the plasma concentration-time curve.

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