scholarly journals Evaluation of the Robustness of Therapeutic Drug Monitoring Coupled with Bayesian Forecasting of Busulfan with Regard to Inaccurate Documentation

2021 ◽  
Author(s):  
Adrin Dadkhah ◽  
Dzenefa Alihodzic ◽  
Astrid Broeker ◽  
Nicolaus Kröger ◽  
Claudia Langebrake ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Infusion Time ◽  
Limited Sampling ◽  
Accuracy And Precision ◽  
Dosing Regimens ◽  
Auc Estimation ◽  
The Individual

Abstract Background Inaccurate documentation of sampling and infusion times is a potential source of error in personalizing busulfan doses using therapeutic drug monitoring (TDM). Planned times rather than the actual times for sampling and infusion time are often documented. Therefore, this study aimed to evaluate the robustness of a limited sampling TDM of busulfan with regard to inaccurate documentation. Methods A pharmacometric analysis was conducted in NONMEM® 7.4.3 and “R” by performing stochastic simulation and estimation with four, two and one sample(s) per patient on the basis of a one-compartment- (1CMT) and two-compartment (2CMT) population pharmacokinetic model. The dosing regimens consisted of i.v. busulfan (0.8 mg/kg) every 6 h (Q6H) or 3.2 mg/kg every 24 h (Q24H) with a 2 h- and 3 h infusion time, respectively. The relative prediction error (rPE) and relative root-mean-square error (rRmse) were calculated in order to determine the accuracy and precision of the individual AUC estimation. Results A noticeable impact on the estimated AUC based on a 1CMT-model was only observed if uncertain documentation reached ± 30 min (1.60% for Q24H and 2.19% for Q6H). Calculated rPEs and rRmse for Q6H indicate a slightly lower level of accuracy and precision when compared to Q24H. Spread of rPE’s and rRmse for the 2CMT-model were wider and higher compared to estimations based on a 1CMT-model. Conclusions The estimated AUC was not affected substantially by inaccurate documentation of sampling and infusion time. The calculated rPEs and rRmses of estimated AUC indicate robustness and reliability for TDM of busulfan, even in presence of erroneous records.

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.

Population Pharmacokinetics of Meropenem in Preterm Infants

2021 ◽  
Vol 76 (5) ◽  
pp. 497-505
Author(s):  
Irina B. Bondareva ◽  
Sergey K. Zyryanov ◽  
Aleksandra M. Kazanova
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Pharmacokinetic Parameter ◽  
Gestational Age ◽  
Drug Monitoring ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Variability ◽  
Older Children ◽  
Term Neonates

Background. Meropenem, a broad spectrum carbapenem antibiotic, is often used for newborns despite of limited data available on neonatal pharmacokinetics. Due to pharmacokinetic and pharmacodynamic differences as well as to significant changes in the human body related to growth and maturation of organs and systems, direct scaling and dosing extrapolation from adults or older children with adjustment on patients weight can result in increased risk of toxicity or treatment failures. Aims to evaluate the pharmacokinetics of meropenem in premature neonates based on therapeutic drug monitoring data in real clinical settings. Materials. Of 53 pre-term neonates included in the pharmacokinetic/pharmacodynamic analysis, in 39 (73.6%) patients, gestational age ranged from 23 to 30 weeks. Population and individual pharmacokinetic parameter values were estimated by the NPAG program from the Pmetrics package based on peak-trough therapeutic drug monitoring. Samples were assayed by high-performance liquid chromatography. One-compartment pharmacokinetic model with zero-order input and first-order elimination was used to fit concentration data and to predict pharmacokinetic parameter (%T MIC of free drug) for virtual patients with simulated fast, moderate and slow meropenem elimination received different dosage by minimum inhibitory concentration (MIC) level. Univariate and multivariate regression analysis was used to evaluate the influence of patients covariates (gestational age, postnatal age, postconceptual age, body weight, creatinine clearance calculated by Schwartz formula, etc) on estimated meropenem pharmacokinetic parameters. Results. The identified population pharmacokinetic parameters of meropenem in pre-term newborns (elimination half-lives T1/2 = 1.93 0.341 h; clearance CL = 0.26 0.085 L/h/ kg; volume of distribution V = 0.71 0.22 L/h) were in good agreement with those published in the literature for adults, neonates and older children. Pharmacokinetic/pharmacodynamic modeling demonstrated that a meropenem dosage regimen of 90 mg/kg/day administered using prolonged 3-hour infusion every 8 hours should be considered as potentially effective therapy if nosocomial infections with resistant organisms (MIC 8 mg/L) are treated. Conclusions. Neonates and especially pre-term neonates have a great pharmacokinetic variability. Meropenem dosing in premature newborns derived from population pharmacokinetic/pharmacodynamic model can partly overcome the variability, but not all pharmacokinetic variability can be explained by covariates in a model. Further personalizing based on Bayesian forecasting approach and a patients therapeutic drug monitoring data can help to achieve desired pharmacodynamic target.

scholarly journals P99 Clinical validation of published vancomycin population PK models in critically ill neonates

2019 ◽  
Vol 104 (6) ◽  
pp. e58.2-e59
Author(s):  
A van der Veen ◽  
RJ Keizer ◽  
W de Boode ◽  
A Somers ◽  
R Brüggemann ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
A Priori ◽  
Predictive Performance ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Suitable Model ◽  
List Type ◽  
A Posteriori ◽  
Target Attainment

BackgroundVancomycin is commonly used for treatment of severe Gram+ neonatal infections. Currently, even with the use of optimized dosing regimens and therapeutic drug monitoring (TDM), target attainment rates are abominable, leaving patients at risk for therapeutic failure and toxicity. Model-informed precision dosing (MIPD) offers a large potential to improve therapy in the individual patient.The aim of this study was to identify a suitable model for bedside MIPD by assessing the predictive performance of published population pharmacokinetic (popPK) models.MethodsA literature search was conducted to identify parametric popPK models. PK vancomycin data were retrospectively collected from NICU patients at the Radboud University Hospital, Nijmegen, The Netherlands. The model predictive performance was assessed by comparison of predictions to observations, calculation of bias (Mean Percentage Errors, MPE) and imprecision (Normalized Root Mean Squared Errors, NRMSE). Evaluations included both a priori (model covariate input) and a posteriori (model covariate and TDM concentration input) scenarios.Results265 TDM measurements from 65 neonates (median postmenstrual age:32 weeks [range:25–45 weeks]; median weight:1281g [range:597–5360g]; median serum creatinine:0,48 mg/dL [range:0,15–1,28 mg/dL]) were used for model evaluation. Six popPK models were evaluated1–6. A posteriori predictions of all models were consistently more accurate and precise compared to the a priori (starting dose) predictions. PopPK models of Frymoyer et al. and Capparelli et al. consistently performed best through all evaluations in both the a priori and a posteriori scenario (MPE ranging from -18 to 6,4% in a priori scenario and -6,5 to -3,8% in a posteriori scenario; NRMSE ranging from 34 to 40% in a priori scenario and 23 to 24% in a posteriori scenario).ConclusionLarge differences in predictive performance of popPK models were observed. Repeated therapeutic drug monitoring remains necessary to increase target attainment rate. Best performing models for bedside MIPD were identified in our patient population.ReferencesZhao W, Lopez E, Biran V, et al. ( 2013). Vancomycin continuous infusion in neonates: Dosing optimisation and therapeutic drug monitoring. Arch Dis Child;98(6):449–453.Capparelli EV, Lane JR, Romanowski GL, et al. ( 2001). The influences of renal function and maturation on vancomycin elimination in newborns and infants. J Clin Pharmacol, 41:927–934.De Cock RFW, Allegaert K, Brussee JM, et al. ( 2014). Simultaneous pharmacokinetic modeling of gentamicin, tobramycin and vancomycin clearance from neonates to adults: towards a semi-physiological function for maturation in glomerular filtration. Pharm Res;31(10):2642–2654.Frymoyer A, Hersh AL, El-Komy MH, et al. ( 2014). Association between vancomycin trough concentration and area under the concentration-time curve in neonates. Antimicrob Agents Chemother, 58(11):6454–6461.Anderson BJ, Allegaert K, Van Den Anker JN, Cossey V, Holford NHG. ( 2006). Vancomycin pharmacokinetics in preterm neonates and the prediction of adult clearance. Br J Clin Pharmacol;63(1):75–84.Germovsek E, Osborne L, Gunaratnam F, Lounis SA, Busquets FB, Sinha AK. ( 2019). Development and external evaluation of a population pharmacokinetic model for continuous and intermittent administration of vancomycin in neonates and infants using prospectively collected data. J Antimicrob Chemother, 1–9.Disclosure(s)R. Keizer is an employee and stockholder of InsightRX.

scholarly journals Therapeutic drug monitoring in special populations

1998 ◽  
Vol 44 (2) ◽  
pp. 415-419 ◽  
Author(s):  
Philip D Walson
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Diagnostic Testing ◽  
Drug Effects ◽  
Special Populations ◽  
Therapeutic Drug ◽  
Drug Reactions ◽  
Drug Concentrations ◽  
Dosing Regimens ◽  
Multiple Drugs

Abstract Therapeutic drug monitoring (TDM) is commonly used to maintain “therapeutic” drug concentrations. Even in compliant patients, with “average” drug kinetics, TDM is useful to identify the causes of unwanted or unexpected responses, prevent unnecessary diagnostic testing, improve clinical outcomes, and even save lives. TDM has greatest promise in certain special populations who are: (a) prone to under- or overrespond to usual dosing regimens, (b) least able to tolerate, recognize, or communicate drug effects, or who are (c) intentionally or accidentally misdosed. TDM is especially useful in patients at the extremes of age, in adolescents, and in patients who are either taking multiple drugs or expressing unusual pharmacokinetics as a result of physiological, environmental, or genetic causes. Less-well-appreciated uses of TDM include prevention of dangerousunderdosing of patients, investigation of adverse drug reactions, and identification of serious medication errors, even for a number of drugs that are not traditionally monitored. TDM can be useful for some drugs in any patient and for most drugs in some special populations.

scholarly journals What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

Pharmaceutics ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 638
Author(s):  
Ming G. Chai ◽  
Menino O. Cotta ◽  
Mohd H. Abdul-Aziz ◽  
Jason A. Roberts
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Therapeutic Drug Monitoring ◽  
Patient Outcomes ◽  
Drug Monitoring ◽  
Point Of Care ◽  
Statistical Modelling ◽  
Therapeutic Drug ◽  
Dosing Regimens ◽  
Point Of Care System

Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient’s clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.

Evaluation of Existing Limited Sampling Models for Therapeutic Drug Monitoring of Busulphan in Children and Adults Undergoing SCT.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 1150-1150
Author(s):  
Zuzana Hassan ◽  
Marie Sandström ◽  
Moustapha Hassan
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Conditioning Regimen ◽  
Trapezoidal Rule ◽  
Therapeutic Window ◽  
Therapeutic Drug ◽  
High Dose ◽  
Time Interval ◽  
Suitable Model ◽  
Limited Sampling

Abstract Busulphan (Bu) is used in high dose conditioning regimen prior to stem cell transplantation. Bu has a narrow therapeutic window and over- and under-dosing may have a fatal outcome. Bu pharmacokinetics and pharmacodynamics were extensively studied and wide inter- and intra-individual variation was found. Several limited sampling models (LSM) have been developed for Bu administered orally to simplify therapeutic drug monitoring and consequently dose adjustment. The aim of this study was to evaluate the existing LSM in adults and children undergoing conditioning regimen before SCT. Seventy-four patients (62 adults and 12 children) with malignant and non-malignant diseases were analysed. Plasma was sampled at 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5 and 6 hours after the first dose of Bu. Bu was determined using gas chromatography with electron capture detection. The area under the plasma concentration-time curve (AUC) for time interval 0 to 6 hours was determined using Winnonlin program and trapezoidal rule. Results were compared to the estimated AUCs using LSMs (Vassal 1992, Schuler 1994, Hassan 1996, Chatergoon 1997). The best correlation between the AUCs determined using trapezoidal rule and 3-points model by Schuler was found (R²=0.95 for all patients, R²=0.97 for children and R²=0.94 for adults). In children, a correlation between AUCs determined with trapezoidal rule and following LSMs was found: 2-points LSM by Schuler (R²=0.94), LSM by Hassan (R²=0.94), LSM by Vassal (R²=0.81) and 3 of 5 LSMs by Chatergoon (R²=0.85, 0.88 and 0.87, resp.). AUCs in children determined using Winnonlin showed good correlation with both Schuler’s models, model by Hassan and one of 4-points models by Chatergoon. However, the correlation between the AUCs determined using trapezoidal rule and Winnonlin was good in children (R²=0.98), but not in adults (R²=0.65). Thus, several limited sampling models are suitable for AUC estimation in children, while there is only one suitable model for adults. This conclusion is made with reservation that even the trapezoidal rule may underestimate the real AUC dependent on sampling density.

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