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 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 Simplified Equations Using Two Concentrations To Calculate Area under the Curve for Antimicrobials with Concentration-Dependent Pharmacodynamics: Daptomycin as a Motivating Example

2014 ◽  
Vol 58 (6) ◽  
pp. 3162-3167 ◽  
Author(s):  
Manjunath P. Pai ◽  
Alessandro Russo ◽  
Andrea Novelli ◽  
Mario Venditti ◽  
Marco Falcone
Keyword(s):  
Healthy Volunteers ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Antimicrobial Agents ◽  
Population Pharmacokinetic ◽  
Good Precision ◽  
Time Data ◽  
Time Curve ◽  
Dosing Interval ◽  
Concentration Time

ABSTRACTThe effects of several antimicrobial agents are predicted by the ratio of the area under the concentration-time curve (AUC) to the MIC (AUC/MIC). Peak (Cp) and trough (Ct) concentrations are often measured clinically as surrogates of AUC because actual computation of AUC from 1 or 2 samples requires sophisticated mathematical methods. Given that the effects of daptomycin are predicted by AUC/MIC, our objective was to compare simple equation calculated AUC based onCpandCtto model integrated AUC. A standard population pharmacokinetic model was used to simulate 5,000 daptomycin concentration-time profiles after 5 doses of 6 mg/kg of body weight/day (0.5-h infusions). The AUC for the 24-h period was computed by integration and by equations with 110Cp-Ctcombination pairs. TheCptime points were in 15-min increments between 0.5 h and 3 h andCtin 15-min increments within an hour of the end of the dosing interval for each dose. The precision and bias of the calculated AUC relative to the integrated AUC were determined to identifyCp-Ctpairs associated with the lowest bias and highest precision. The equations were further validated using two daptomycin concentration-time data sets from healthy volunteers and critically ill patients. The precision and bias of calculated AUC were based primarily onCp, and use of a daptomycinCp1.5 h to 3 h from the start of infusion was associated with a bias of <10% and anR2of >0.95. Data from the healthy volunteers and critically ill patients also demonstrated declining bias with use ofCp≥1.5 h from the start of infusion with relatively good precision. Simplified equations using a daptomycinCpapproximately 2 h from the start of infusion and aCtwithin an hour of the end of the dosing interval should yield precise and unbiased estimates of daptomycin AUC.

scholarly journals Evaluating the Relationship between Vancomycin Trough Concentration and 24-Hour Area under the Concentration-Time Curve in Neonates

2018 ◽  
Vol 62 (4) ◽  
pp. e01647-17 ◽  
Author(s):  
Sheng-Hsuan Tseng ◽  
Chuan Poh Lim ◽  
Qi Chen ◽  
Cheng Cai Tang ◽  
Sing Teang Kong ◽  
...  
Keyword(s):  
Steady State ◽  
Trough Concentration ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Time Curve ◽  
Content Type ◽  
Concentration Time ◽  
Vancomycin Trough ◽  
Trough Concentrations ◽  
The Relationship

ABSTRACT Bacterial sepsis is a major cause of morbidity and mortality in neonates, especially those involving methicillin-resistant Staphylococcus aureus (MRSA). Guidelines by the Infectious Diseases Society of America recommend the vancomycin 24-h area under the concentration-time curve to MIC ratio (AUC24/MIC) of >400 as the best predictor of successful treatment against MRSA infections when the MIC is ≤1 mg/liter. The relationship between steady-state vancomycin trough concentrations and AUC24 values (mg·h/liter) has not been studied in an Asian neonatal population. We conducted a retrospective chart review in Singapore hospitals and collected patient characteristics and therapeutic drug monitoring data from neonates on vancomycin therapy over a 5-year period. A one-compartment population pharmacokinetic model was built from the collected data, internally validated, and then used to assess the relationship between steady-state trough concentrations and AUC24. A Monte Carlo simulation sensitivity analysis was also conducted. A total of 76 neonates with 429 vancomycin concentrations were included for analysis. Median (interquartile range) was 30 weeks (28 to 36 weeks) for postmenstrual age (PMA) and 1,043 g (811 to 1,919 g) for weight at the initiation of treatment. Vancomycin clearance was predicted by weight, PMA, and serum creatinine. For MRSA isolates with a vancomycin MIC of ≤1, our major finding was that the minimum steady-state trough concentration range predictive of achieving an AUC24/MIC of >400 was 8 to 8.9 mg/liter. Steady-state troughs within 15 to 20 mg/liter are unlikely to be necessary to achieve an AUC24/MIC of >400, whereas troughs within 10 to 14.9 mg/liter may be more appropriate.

scholarly journals Pharmacokinetics of Daptomycin in Critically Ill Pediatric Patients

2018 ◽  
Vol 62 (6) ◽  
Author(s):  
Charalampos Antachopoulos ◽  
Stavroula Ilia ◽  
Paschalis Kadiltzoglou ◽  
Eirini Baira ◽  
Aristides Dokoumetzidis ◽  
...  
Keyword(s):  
Critically Ill ◽  
Drug Exposure ◽  
Pediatric Patients ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Burn Patients ◽  
Time Curve ◽  
Once Daily ◽  
Concentration Time ◽  
Optimal Drug

ABSTRACT The pharmacokinetics of daptomycin (10 mg/kg once daily) was studied in 4 critically ill pediatric patients aged 8 to 14 yrs. The area under the concentration-time curve from time zero to infinity (AUC 0–∞ ) of plasma concentrations on day 1 ranged between 123.8 to 663.9 μg · h/ml, with lower values observed in septic and burn patients; clearance ranged from 15.1 to 80.7 ml/h/kg. Higher-than-recommended doses of daptomycin may be needed in septic children to ensure optimal drug exposure. Interpatient variability may suggest a role for therapeutic drug monitoring.

Limited Sampling Strategies to Estimate the Area under the Concentration-time Curve

2012 ◽  
Vol 51 (05) ◽  
pp. 383-394 ◽  
Author(s):  
M. Fukumoto ◽  
L. Bax ◽  
A. Kohno ◽  
Y. Morishita ◽  
H. Tsuruta
Keyword(s):  
Rate Constant ◽  
Elimination Rate ◽  
Estimation Method ◽  
Pharmacokinetic Parameters ◽  
Good Precision ◽  
Sampling Strategies ◽  
Time Curve ◽  
New Methods ◽  
Limited Sampling ◽  
Concentration Time

SummaryBackground: Over 100 limited sampling strategies (LSSs) have been proposed to reduce the number of blood samples necessary to estimate the area under the concentration-time curve (AUC). The conditions under which these strategies succeed or fail remain to be clarified.Objectives: We investigated the accuracy of existing LSSs both theoretically and numerically by Monte Carlo simulation. We also proposed two new methods for more accurate AUC estimations.Methods: We evaluated the following existing methods theoretically: i) nonlinear curve fitting algorithm (NLF), ii) the trapezium rule with exponential curve approximation (TZE), and iii) multiple linear regression (MLR). Taking busulfan (BU) as a test drug, we generated a set of theoretical concentration-time curves based on the identified distribution of pharmacokinetic parameters of BU and re-evaluated the existing LSSs using these virtual validation profiles. Based on the evaluation results, we improved the TZE so that unrealistic parameter values were not used. We also proposed a new estimation method in which the most likely curve was selected from a set of pre-generated theoretical concentration-time curves.Results: Our evaluation, based on clinical profiles and a virtual validation set, revealed: i) NLF sometimes overestimated the absorption rate constant Ka, ii) TZE overestimated AUC over 280% when Ka is small, and iii) MLR underestimated AUC over 30% when the elimination rate constant Ke is small. These results were consistent with our mathematical evaluations for these methods. In contrast, our two new methods had little bias and good precision.Conclusions: Our investigation revealed that existing LSSs induce different but specific biases in the estimation of AUC. Our two new LSSs, a modified TZE and one using model concentration-time curves, provided accurate and precise estimations of AUC.

scholarly journals New Colistin Population Pharmacokinetic Data in Critically Ill Patients Suggesting an Alternative Loading Dose Rationale

2014 ◽  
Vol 58 (12) ◽  
pp. 7324-7330 ◽  
Author(s):  
N. Grégoire ◽  
O. Mimoz ◽  
B. Mégarbane ◽  
E. Comets ◽  
D. Chatelier ◽  
...  
Keyword(s):  
Steady State ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Plasma Concentrations ◽  
Multidrug Resistant ◽  
Pharmacokinetic Analysis ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Data ◽  
Loading Dose ◽  
Liquid Chromatography Tandem Mass

ABSTRACTColistin is an old antibiotic that has recently gained a considerable renewal of interest as the last-line defense therapy against multidrug-resistant Gram-negative bacteria. It is administered as colistin methanesulfonate (CMS), an inactive prodrug, and it was shown that due to slow CMS conversion, colistin plasma concentrations increase very slowly after treatment initiation, which constitutes the rationale for a loading dose in critically ill patients. However, faster CMS conversion was observed in healthy volunteers but using a different CMS brand, which may also have a major impact on colistin pharmacokinetics. Seventy-three critically ill patients not undergoing dialysis received multiple doses of CMS. The CMS concentrations were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and a pharmacokinetic analysis was conducted using a population approach. We confirmed that CMS renal clearance and colistin concentrations at steady state are mostly governed by creatinine clearance, but we predict a typical maximum concentration of drug in serum (Cmax) of colistin close to 2 mg/liter, occurring 3 h after an initial dose of 2 million international units (MIU) of CMS. Accordingly, the estimated colistin half-life (t1/2) was relatively short (3.1 h), with rapid attainment of steady state. Our results are only partially consistent with other recently published results. We confirm that the CMS maintenance dose should be adjusted according to renal function in critically ill patients. However, much higher than expected colistin concentrations were observed after the initial CMS dose, with rapid steady-state achievement. These discrepancies challenge the pharmacokinetic rationale for a loading dose, which may still be appropriate for rapid bacterial eradication and an improved clinical cure rate.

scholarly journals Cerebrospinal Fluid Penetration of Ceftolozane-Tazobactam in Critically Ill Patients with an Indwelling External Ventricular Drain

2020 ◽  
Vol 65 (1) ◽  
pp. e01698-20 ◽  
Author(s):  
Fekade B. Sime ◽  
Melissa Lassig-Smith ◽  
Therese Starr ◽  
Janine Stuart ◽  
Saurabh Pandey ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
External Ventricular Drain ◽  
Compartment Model ◽  
Time Data ◽  
Time Curve ◽  
Content Type ◽  
Concentration Time ◽  
Unbound Concentration

ABSTRACTThe aim of this study was to describe the pharmacokinetics of ceftolozane-tazobactam in plasma and cerebrospinal fluid (CSF) of infected critically ill patients. In a prospective observational study, critically ill patients (≥18 years) with an indwelling external ventricular drain received a single intravenous dose of 3.0 g ceftolozane-tazobactam. Serial plasma and CSF samples were collected for measurement of unbound ceftolozane and tazobactam concentration by liquid chromatography. Unbound concentration-time data were modeled in R using Pmetrics. Dosing simulations were performed using the final model. A three-compartment model adequately described the data from 10 patients. For ceftolozane, the median (interquartile range [IQR]) area under the unbound concentration-time curve from time zero to infinity (fAUC0-inf) in the CSF and plasma were 30 (19 to 128) h·mg/liter and 323 (183 to 414) h·mg/liter, respectively. For tazobactam, these values were 5.6 (2 to 24) h·mg/liter and 52 (36 to 80) h·mg/liter, respectively. Mean ± standard deviation (SD) CSF penetration ratios were 0.2 ± 0.2 and 0.2 ± 0.26 for ceftolozane and tazobactam, respectively. With the regimen of 3.0 g every 8 h, a probability of target attainment (PTA) of ≥0.9 for 40% fT>MIC in the CSF was possible only when MICs were ≤0.25 mg/liter. The CSF cumulative fractional response for Pseudomonas aeruginosa-susceptible MIC distribution was 73%. The tazobactam PTA for the minimal suggested exposure of 20% fT>1 mg/liter was 12%. The current maximal dose of ceftolozane-tazobactam (3.0 g every 8 h) does not provide adequate CSF exposure for treatment of Gram-negative meningitis or ventriculitis unless the MIC for the causative pathogen is very low (≤0.25 mg/liter).

Estimation of Abbreviated Mycophenolic Acid Area Under the Concentration-Time Curve during Stable Post-transplant Period by Limited Sampling Strategy

2020 ◽  
Vol 15 ◽  
Author(s):  
Asieh Karimani ◽  
Hasan Abedi ◽  
Fatemeh Nazemian ◽  
Atena Poortaji ◽  
Amir Hooshang Mohammad pour
Keyword(s):  
Renal Transplantation ◽  
Prediction Error ◽  
Sampling Strategy ◽  
Limited Sampling Strategy ◽  
Stable Phase ◽  
Therapeutic Drug ◽  
Time Curve ◽  
Limited Sampling ◽  
Concentration Time ◽  
Over Time

Background: The area under the concentration-time curve (AUC) of mycophenolic acid (MPA), is a valid prognosticator of the risk of rejection and the gold standard in its therapeutic drug monitoring (TDM), over time posttransplantation. Objective: This study aimed to investigate MPA pharmacokinetic parameters, as well as developing a limited sampling strategy (LSS) to estimate an abbreviated MPA AUC, in the stable phase post-renal transplantation. Methods: In this study 19 patients with normal graft function (glomerular filtration rate >70 ml/min) who fulfilled inclusion & exclusion criteria were involved. Blood samples at various times were taken in the stable phase after transplantation. MPA plasma concentration was measured by reverse-phase high-performance liquid chromatography. MPA AUC0–12h was calculated using the linear trapezoidal rule. Multiple stepwise regression analysis was used to determine the minimal time points of MPA levels that could be used to yield model equations best fitted to MPA AUC 0- 12h. The findings of this study were compared with the results of our previous study, which was done similarly in the early phase post-renal transplantation. Results: The results demonstrated that the MPA-AUC and clearance were not affected over time, but MPA-tmax was significantly lower in the stable phase in comparison with the early phase (P=0.001). The best regression equation for AUC estimation in the stable phase was AUC=9.57*C6+27.238 (r2=0.907). The validation of the method was performed using the jackknife method. The mean prediction error of these models was not different from zero (P > 0.05) and had a high root mean square prediction error (7.91). Conclusion: In conclusion, the pharmacokinetics of MPA could be affected by time after transplantation, make it essential to develop a limited sampling strategy granted an efficacious approach for therapeutic drug monitoring during the stable post-transplant period.

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