The Evaluation of Multiple Linear Regression–Based Limited Sampling Strategies for Mycophenolic Acid in Children with Nephrotic Syndrome

We evaluated mycophenolic acid (MPA) limited sampling strategies (LSSs) established using multiple linear regression (MLR) in children with nephrotic syndrome treated with mycophenolate mofetil (MMF). MLR-LSS is an easy-to-determine approach of therapeutic drug monitoring (TDM). We assessed the practicability of different LSSs for the estimation of MPA exposure as well as the optimal time points for MPA TDM. The literature search returned 29 studies dated 1998–2020. We applied 53 LSSs (n = 48 for MPA, n = 5 for free MPA [fMPA]) to predict the area under the time-concentration curve (AUCpred) in 24 children with nephrotic syndrome, for whom we previously determined MPA and fMPA concentrations, and compare the results with the determined AUC (AUCtotal). Nine equations met the requirements for bias and precision ±15%. The MPA AUC in children with nephrotic syndrome was predicted the best by four time-point LSSs developed for renal transplant recipients. Out of five LSSs evaluated for fMPA, none fulfilled the ±15% criteria for bias and precision probably due to very high percentage of bound MPA (99.64%). MPA LSS for children with nephrotic syndrome should include blood samples collected 1 h, 2 h and near the second MPA maximum concentration. MPA concentrations determined with the high performance liquid chromatography after multiplying by 1.175 may be used in LSSs based on MPA concentrations determined with the immunoassay technique. MPA LSS may facilitate TDM in the case of MMF, however, more studies on fMPA LSS are required for children with nephrotic syndrome.

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Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01092-18 ◽

2018 ◽

Vol 62 (12) ◽

Cited By ~ 6

Author(s):

Simone H. J. van den Elsen ◽

Marieke G. G. Sturkenboom ◽

Natasha van't Boveneind-Vrubleuskaya ◽

Alena Skrahina ◽

Tjip S. van der Werf ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Compartment Model ◽

Performance Criteria ◽

Therapeutic Drug ◽

Population Pharmacokinetic ◽

Sampling Strategies ◽

Tuberculosis Patients ◽

Limited Sampling ◽

Poppk Model

ABSTRACT Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC0–24) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach. The popPK model and Bayesian LSS were developed using data from 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum time span, 8 h; minimum interval, 1 h; 1 to 3 samples) were tested. Performance criteria were root-mean-square error (RMSE) of <15%, mean prediction error (MPE) of <5%, and r2 value of >0.95. A one-compartment model with lag time best described the data while only slightly underestimating the AUC0–24 (mean, −7.9%; standard error [SE], 1.7%). The Bayesian LSS using 0- and 5-h postdose samples (RMSE, 8.8%; MPE, 0.42%; r2 = 0.957) adequately estimated the AUC0–24, with a mean underestimation of −4.4% (SE, 2.7%). The multiple linear regression LSS using 0- and 4-h postdose samples (RMSE, 7.0%; MPE, 5.5%; r2 = 0.977) was internally validated, with a mean underestimation of −0.46% (SE, 2.0%). In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin. (This study has been registered at ClinicalTrials.gov under identifier NCT01918397.)

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Limited Sampling Strategies Using Linear Regression and the Bayesian Approach for Therapeutic Drug Monitoring of Moxifloxacin in Tuberculosis Patients

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00384-19 ◽

2019 ◽

Vol 63 (7) ◽

Cited By ~ 3

Author(s):

Simone H. J. van den Elsen ◽

Marieke G. G. Sturkenboom ◽

Onno W. Akkerman ◽

Katerina Manika ◽

Ioannis P. Kioumis ◽

...

Keyword(s):

Therapeutic Drug Monitoring ◽

Linear Regression ◽

Multiple Linear Regression ◽

Bayesian Approach ◽

Drug Monitoring ◽

Therapeutic Drug ◽

Population Pharmacokinetic ◽

Sampling Strategies ◽

Limited Sampling ◽

The Bayesian Approach

ABSTRACT Therapeutic drug monitoring (TDM) of moxifloxacin is recommended to improve the response to tuberculosis treatment and reduce acquired drug resistance. Limited sampling strategies (LSSs) are able to reduce the burden of TDM by using a small number of appropriately timed samples to estimate the parameter of interest, the area under the concentration-time curve. This study aimed to develop LSSs for moxifloxacin alone (MFX) and together with rifampin (MFX+RIF) in tuberculosis (TB) patients. Population pharmacokinetic (popPK) models were developed for MFX (n = 77) and MFX+RIF (n = 24). In addition, LSSs using Bayesian approach and multiple linear regression were developed. Jackknife analysis was used for internal validation of the popPK models and multiple linear regression LSSs. Clinically feasible LSSs (one to three samples, 6-h timespan postdose, and 1-h interval) were tested. Moxifloxacin exposure was slightly underestimated in the one-compartment models of MFX (mean –5.1%, standard error [SE] 0.8%) and MFX+RIF (mean –10%, SE 2.5%). The Bayesian LSSs for MFX and MFX+RIF (both 0 and 6 h) slightly underestimated drug exposure (MFX mean –4.8%, SE 1.3%; MFX+RIF mean –5.5%, SE 3.1%). The multiple linear regression LSS for MFX (0 and 4 h) and MFX+RIF (1 and 6 h), showed mean overestimations of 0.2% (SE 1.3%) and 0.9% (SE 2.1%), respectively. LSSs were successfully developed using the Bayesian approach (MFX and MFX+RIF; 0 and 6 h) and multiple linear regression (MFX, 0 and 4 h; MFX+RIF, 1 and 6 h). These LSSs can be implemented in clinical practice to facilitate TDM of moxifloxacin in TB patients.

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A Systematic Review of Multiple Linear Regression-Based Limited Sampling Strategies for Mycophenolic Acid Area Under the Concentration–Time Curve Estimation

European Journal of Drug Metabolism and Pharmacokinetics ◽

10.1007/s13318-021-00713-0 ◽

2021 ◽

Author(s):

Joanna Sobiak ◽

Matylda Resztak

Keyword(s):

Systematic Review ◽

Linear Regression ◽

Multiple Linear Regression ◽

Mycophenolic Acid ◽

Sampling Strategies ◽

Time Curve ◽

Curve Estimation ◽

Limited Sampling ◽

Concentration Time

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Limited-Sampling Strategies for Anidulafungin in Critically Ill Patients

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.03375-14 ◽

2014 ◽

Vol 59 (2) ◽

pp. 1177-1181 ◽

Cited By ~ 5

Author(s):

Marjolijn J. P. van Wanrooy ◽

Johannes H. Proost ◽

Michael G. G. Rodgers ◽

Jan G. Zijlstra ◽

Donald R. A. Uges ◽

...

Keyword(s):

Linear Regression ◽

Critically Ill ◽

Critically Ill Patients ◽

Therapeutic Drug ◽

Population Pharmacokinetic ◽

Pharmacokinetic Data ◽

Sampling Strategies ◽

Time Curve ◽

Limited Sampling ◽

Concentration Time

ABSTRACTEfficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW\Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC0–24) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R2= 0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R2= 0.89; bias, −0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.)

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