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

2018 ◽  
Vol 62 (12) ◽  
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.)

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

2019 ◽  
Vol 63 (7) ◽  
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.

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

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3723
Author(s):  
Joanna Sobiak ◽  
Matylda Resztak ◽  
Maria Chrzanowska ◽  
Jacek Zachwieja ◽  
Danuta Ostalska-Nowicka
Keyword(s):  
Nephrotic Syndrome ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Mycophenolic Acid ◽  
High Performance ◽  
Optimal Time ◽  
Therapeutic Drug ◽  
Renal Transplant Recipients ◽  
Sampling Strategies ◽  
Limited Sampling

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.

scholarly journals Limited-Sampling Strategies for Anidulafungin in Critically Ill Patients

2014 ◽  
Vol 59 (2) ◽  
pp. 1177-1181 ◽  
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.)

scholarly journals 1319. Pharmacokinetics of Ceftolozane/Tazobactam in Patients with Burns

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S670-S671
Author(s):  
Ronald G Hall ◽  
Jotam Pasipanodya ◽  
William C Putnam ◽  
John Griswold ◽  
Sharmila Dissanaike ◽  
...  
Keyword(s):  
Human Plasma ◽  
Kidney Injury ◽  
Compartment Model ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Full Thickness ◽  
Severe Burns

Abstract Background Antimicrobial dosing in moderate/severe burns patients is complicated due to the potential unpredictable hyperdynamic pathophysiologic states including 1) hypoproteinemia, 2) acute kidney injury and 3) onset of septicemia. Therefore, distribution assumptions about the population pharmacokinetic (PopPK) profiles of either endogenous or xenobiotic pharmacophores in this patient population can lead to biased parameter estimates. In order to prevent potential bias an agnostic nonparametric adaptive grid approach to describe ceftolozane/tazobactam (C/T) PopPK profiles in patients with partial- and full-thickness burns was employed. Methods A human clinical PK study in burn patients was conducted using the standard approved dose of C/T (2 grams/1 gram). A single intravenous dose was administered over 60 minutes. Whole blood was obtained pre-dose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12, 16, and 24 hours following the start of infusion. LC-MS/MS bioanalytical methods were developed, validated and employed to determine C/T concentrations in human plasma. PopPK were modeled using Pmetrics package for R. One-, two- and three-compartment models were examined and compared. The influence of several parameters, including %body surface area burns, creatinine clearance (CrCL), weight, albumin and age were tested. Results The bioanalytical method for determination of C/T in human plasma met all recommended criteria of the LC-MS/MS. Five males and one female (ages 24 to 66 years), contributed 148 plasma PK samples. The female had 35% partial-thickness burns. The males had full-thickness burns ranging from 27 to 66%. The median CrCL was 104 mL/min (range 73-148 mL/min). Two-compartment model with absorption (Ka) from compartment 1 to 2 and elimination from compartment 2 (Ke), with nonlinear interactions between C/T elimination and CrCL best described the data. Figure A show that bias was minimal. Importantly, both drugs exhibited marked variability for both volume and elimination (Table), since volume was bimodally distributed (Figure B). A) Observation-versus-Prediction; B) Estimated Ke, V and Ka population parameter densities Summary of pharmacokinetic parameters Conclusion C/T exhibited high variability surpassing that observed with severe infections, suggesting that dose adjustment and/or may be therapeutic drug monitoring may be needed to balance target attainment from dose-related toxicities. Disclosures Ronald G. Hall, II, PharmD, MSCS, Medical Titan Group (Grant/Research Support)Merck (Research Grant or Support)

scholarly journals Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis

2015 ◽  
Vol 59 (8) ◽  
pp. 4907-4913 ◽  
Author(s):  
Marieke G. G. Sturkenboom ◽  
Leonie W. Mulder ◽  
Arthur de Jager ◽  
Richard van Altena ◽  
Rob E. Aarnoutse ◽  
...  
Keyword(s):  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Mean Squared Error ◽  
Geometric Mean ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Wide Range

ABSTRACTRifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC0–24) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC0–24values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n= 1,000). The geometric mean AUC0–24value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with anr2value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of −0.4%. This study showed that the rifampin AUC0–24in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.

Population pharmacokinetic modeling and dosing simulations of tobramycin in pediatric patients with cystic fibrosis

2021 ◽  
Author(s):  
Antonin Praet ◽  
Laurent Bourguignon ◽  
Florence Vetele ◽  
Valentine Breant ◽  
Charlotte Genestet ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Dose Adjustment ◽  
Total Body Weight ◽  
Compartment Model ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Time Curve ◽  
Population Pharmacokinetic Modeling ◽  
Two Compartment Model ◽  
Total Body

Initial dosing and dose adjustment of intravenous tobramycin in cystic fibrosis children is challenging. The objectives of this study were to develop nonparametric population pharmacokinetic (PK) models of tobramycin in children with CF to be used for dosage design and model-guided therapeutic drug monitoring. We performed a retrospective analysis of tobramycin PK data in our CF children center. The Pmetrics package was used for nonparametric population PK analysis and dosing simulations. Both the maximal concentration over the MIC (Cmax/MIC) and daily area under the concentration-time curve to the MIC (AUC 24 /MIC) ratios were considered as efficacy target. Trough concentration (Cmin) was considered as the safety target. A total of 2884 tobramycin concentrations collected in 195 patients over 9 years were analyzed. A two-compartment model including total body weight, body surface area and creatinine clearance as covariates best described the data. A simpler model was also derived for implementation into the BestDose software to perform Bayesian dose adjustment. Both models were externally validated. PK/PD simulations with the final model suggest that an initial dose of tobramycin of 15 to 17.5 mg/kg/day was necessary to achieve Cmax/MIC ≥ 10 values for MIC values up to 2 mg/L in most patients. The AUC 24 /MIC target was associated with larger dosage requirements and higher Cmin. A daily dose of 12.5 mg/kg would optimize both efficacy and safety target attainment. We recommend to perform tobramycin TDM, model-based dose adjustment, and MIC determination to individualize intravenous tobramycin therapy in children with CF.

scholarly journals Combining Therapeutic Drug Monitoring and Pharmacokinetic Modelling Deconvolutes Physiological and Environmental Sources of Variability in Clozapine Exposure

Pharmaceutics ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 47
Author(s):  
Kenneth H. Wills ◽  
Stephen J. Behan ◽  
Michael J. Nance ◽  
Jessica L. Dawson ◽  
Thomas M. Polasek ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Smoking Status ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Modelling ◽  
Environmental Covariates ◽  
Poppk Model ◽  
Post Hoc ◽  
The Impact

Background: Clozapine is a key antipsychotic drug for treatment-resistant schizophrenia but exhibits highly variable pharmacokinetics and a propensity for serious adverse effects. Currently, these challenges are addressed using therapeutic drug monitoring (TDM). This study primarily sought to (i) verify the importance of covariates identified in a prior clozapine population pharmacokinetic (popPK) model in the absence of environmental covariates using physiologically based pharmacokinetic (PBPK) modelling, and then to (ii) evaluate the performance of the popPK model as an adjunct or alternative to TDM-guided dosing in an active TDM population. Methods: A popPK model incorporating age, metabolic activity, sex, smoking status and weight was applied to predict clozapine trough concentrations (Cmin) in a PBPK-simulated population and an active TDM population comprising 142 patients dosed to steady state at Flinders Medical Centre in Adelaide, South Australia. Post hoc analyses were performed to deconvolute the impact of physiological and environmental covariates in the TDM population. Results: Analysis of PBPK simulations confirmed age, cytochrome P450 1A2 activity, sex and weight as physiological covariates associated with variability in clozapine Cmin (R2 = 0.7698; p = 0.0002). Prediction of clozapine Cmin using a popPK model based on these covariates accounted for <5% of inter-individual variability in the TDM population. Post hoc analyses confirmed that environmental covariates accounted for a greater proportion of the variability in clozapine Cmin in the TDM population. Conclusions: Variability in clozapine exposure was primarily driven by environmental covariates in an active TDM population. Pharmacokinetic modelling can be used as an adjunct to TDM to deconvolute sources of variability in clozapine exposure.

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