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 P73 Paediatric population pharmacokinetic modeling for gentamicin – is the current dose recommendation justified?

2019 ◽  
Vol 104 (6) ◽  
pp. e47.2-e47
Author(s):  
P Paioni ◽  
C Berger ◽  
SD Krämer
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Drug Exposure ◽  
Body Height ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
List Type ◽  
Dosing Regimen ◽  
Population Pharmacokinetic Modeling ◽  
Pediatr Infect

BackgroundMonitoring of gentamicin serum trough level (Cmin) is standard practice in children to prevent toxicity by accumulation1. Cmin < 2 mg/L are recommended. Peak serum concentration (Cmax) is not routinely measured although Cmax between 10 and 12 mg/L have been recommended balancing efficacy and toxicity2,3. We aimed to develop a population pharmacokinetic (PK) model for gentamicin in children to optimise current dosing regimens.MethodsAll patients receiving once daily intravenous gentamicin (5 mg/kg in children < 7 days and 7.5 mg/kg in children >7 days of age) at the University Children’s Hospital Zurich between 10/2017 and 01/2019 were eligible for this study. Children with cystic fibrosis and renal replacement procedures were excluded. Routine Cmin were measured in all patients before administration of the second or third dose. Additional gentamicin serum levels were measured 30 min (C30) and 4 h after the second dose in patients giving written informed consent. Data were analysed by non-linear mixed-effects modeling.Results165 patients (median age 34 days; IQR 15–56 days) were included in the study. A total number of 103 C30 and 166 Cmin measurements were available, respectively. C30 (mean 19.7 mg/L, SD ±6.1 mg/L) was >12 mg/L in 94/103 (91%) and Cmin >2 mg/mL in 3/166 (1.8%) measurements. The PK model successfully predicted most C30 >12 mg/L but performed poorly at the through levels.ConclusionsOur current gentamicin dosing regimen rarely leads to accumulation but most Cmax are above optimal range. The latter was successfully modelled. Although no evidence for a Cmax upper limit exists, toxicity has been associated with high drug exposure3. This calls for an adjustment of our dosing regimen using our PK model based on body height or weight in order to lower exposure. Further studies investigating the relationship between Cmax levels and clinical outcome and additional data for PK model testing are needed for validation.ReferencesRitz N, Bielicki J, Pfister M, van den Anker J. Therapeutic Drug Monitoring for Anti-infective Agents in Pediatrics: The Way Forward. Pediatr Infect Dis J. 2016;35(5):570–572.Chattopadhyay B. Newborns and gentamicin-how much and how often? J Antimicrob Chemother. 2002;49(1):13–16.Touw DJ, Westerman EM, Sprij AJ. Therapeutic drug monitoring of aminoglycosides in neonates. Clin Pharmacokinet. 2009;48(2):71–88.Disclosure(s)Nothing to disclose

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.

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.

scholarly journals Therapeutic Drug Monitoring of Vancomycin in Adult Patients with Methicillin-Resistant Staphylococcus aureus Bacteremia or Pneumonia

2021 ◽  
Vol 74 (4) ◽  
Author(s):  
Ryan Marko ◽  
Julia Hajjar ◽  
Vanessa Nzeribe ◽  
Michelle Pittman ◽  
Vincent Deslandes ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Logistic Regression ◽  
Therapeutic Drug Monitoring ◽  
Treatment Failure ◽  
Drug Monitoring ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Methicillin Resistant ◽  
Discordance Rate

Background: Vancomycin remains widely used for methicillin-resistant Staphylococcus aureus (MRSA) infections; however, treatment failure rates up to 50% have been reported. At the authors’ institution, monitoring of trough concentration is the standard of care for therapeutic drug monitoring of vancomycin. New guidelines support use of the ratio of 24-hour area under the concentration–time curve to minimum inhibitory concentration (AUC24/MIC) as the pharmacodynamic index most likely to predict outcomes in patients with MRSA-associated infections.Objectives: To determine the discordance rate between trough levels and AUC24/MIC values and how treatment failure and nephrotoxicity outcomes compare between those achieving and not achieving their pharmacodynamic targets. Methods: This retrospective cohort study involved patients with MRSA bacteremia or pneumonia admitted to the study hospital between March 1, 2014, and December 31, 2018, and treated with vancomycin. Data for trough concentrations were collected, and minimum concentrations (Cmin) were extrapolated. The AUC24/MIC values were determined using validated population pharmacokinetic models. The Cmin and AUC24/MIC values were characterized as below, within, or above pharmacodynamic targets (15–20 mg/L and 400–600, respectively). Discordance was defined as any instance where a patient’s paired Cmin and AUC24/MIC values fell in different ranges (i.e., below, within, or above) relative to the target ranges. Predictors of treatment failure and nephrotoxicity were determined using logistic regression. Results: A total of 128 patients were included in the analyses. Of these, 73 (57%) received an initial vancomycin dose less than 15 mg/kg. The discordance rate between Cmin and AUC24/MIC values was 21% (27/128). Rates of treatment failure and nephrotoxicity were 34% (43/128) and 18% (23/128), respectively. No clinical variables were found to predict discordance. Logistic regression identified initiation of vancomycin after a positive culture result (odds ratio [OR] 4.41, 95% confidence interval [CI] 1.36–14.3) and achievement of target AUC24/MIC after 4 days (OR 3.48, 95% CI 1.39–8.70) as modifiable predictors of treatment failure. Conclusions: The relationship between vancomycin monitoring and outcome is likely confounded by inadequate empiric or initial dosing. Before any modification of practice with respect to vancomycin monitoring, empiric vancomycin dosing should be optimized.  RÉSUMÉ Contexte : La vancomycine reste largement utilisée contre les infections dues au Staphylococcus aureus méthicillinorésistant (SAMR); cependant, on rapporte des taux d’échec de traitement allant jusqu’à 50 %. Dans l’institution où travaillent les auteurs, la surveillance de la concentration minimale constitue la norme de soins du suivi thérapeutique pharmacologique de la vancomycine. De nouvelles lignes directrices soutiennent l’utilisation du ratio de 24 h de l’aire sous la courbe de concentration-temps à concentration minimale inhibitrice (AUC24/MIC) en tant qu’indice pharmacodynamique, vraisemblablement pour prédire certains résultats concernant les patients présentant des infections associées au SAMR. Objectifs : Déterminer le taux de discordance entre la concentration minimale et les valeurs de l’AUC24/MIC et la manière dont les échecs de traitement et les résultats de néphrotoxicité se comparent entre les personnes atteignant leurs cibles pharmacodynamiques et celles qui ne l’atteignent pas. Méthodes : Cette étude de cohorte rétrospective impliquait des patients atteints d’une bactériémie au SAMR ou d’une pneumonie au SAMR, admis à l’hôpital où se déroulait l’étude entre le 1er mars 2014 et le 31 décembre 2018 et traités à l’aide de vancomycine. Les données relatives aux concentrations minimales ont été recueillies, et les concentrations minimales (Cmin) extrapolées. Les valeurs de l’AUC24/MIC ont été déterminées à l’aide de modèles de population pharmacocinétiques validés. La caractérisation des valeurs de la Cmin et des valeurs de l’AUC24/MIC se décrit comme suit : « en dessous », « à l’intérieur » ou « au-dessus » des cibles pharmacodynamiques (respectivement 15-20 mg/L et 400-600). La discordance était définie comme une situation où les valeurs associées de la Cmin et de l’AUC24/MIC tombaient dans des plages différentes (c.-à-d., en dessous, à l’intérieur ou au-dessus) par rapport aux plages cibles. Une régression logistique a permis de déterminer les prédicteurs d’échecs de traitement et de néphrotoxicité. Résultats : Au total, 128 patients ont été inclus dans les analyses. De ceux-ci, 73 (57 %) ont reçu une dose initiale de vancomycine de moins de 15 mg/kg. Le taux de discordance entre les valeurs de la Cmin et de l’AUC24/MIC était de 21 % (27/128). Les taux d’échec de traitement et de néphrotoxicité se montaient respectivement à 34 % (43/128) et 18 % (23/128). Aucune variable clinique n’a pu prédire la discordance. La régression logistique a permis de déterminer le début de l’administration de la vancomycine après un résultat de culture positif (rapport de cotes [RC] 4,41, 95 % intervalle de confiance [IC] 1,36–14,3) et l’atteinte de la cible de l’AUC24/MIC après quatre jours (RC 3,48, 95 % IC 1,39-8,70) en tant que prédicteurs modifiables de l’échec du traitement. Conclusions : Il existe probablement une confusion relative à la relation entre la surveillance de la vancomycine et le résultat à cause d’un dosage empirique ou initial inadéquat. Avant de modifier la pratique relative à la surveillance de la vancomycine, le pharmacien doit optimiser son dosage empirique.

scholarly journals Model-Optimized Fluconazole Dose Selection for Critically Ill Patients Improves Early Pharmacodynamic Target Attainment without the Need for Therapeutic Drug Monitoring

2020 ◽  
Vol 65 (3) ◽  
Author(s):  
Indy Sandaradura ◽  
Jessica Wojciechowski ◽  
Deborah J. E. Marriott ◽  
Richard O. Day ◽  
Sophie Stocker ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Critically Ill ◽  
Drug Monitoring ◽  
Critically Ill Patients ◽  
Drug Exposure ◽  
Fungal Infections ◽  
Therapeutic Drug ◽  
Target Attainment ◽  
Dose Selection ◽  
Dose Individualization

ABSTRACT Fluconazole has been associated with higher mortality compared with the echinocandins in patients treated for invasive candida infections. Underexposure from current fluconazole dosing regimens may contribute to these worse outcomes, so alternative dosing strategies require study. The objective of this study was to evaluate fluconazole drug exposure in critically ill patients comparing a novel model-optimized dose selection method with established approaches over a standard 14-day (336-h) treatment course. Target attainment was evaluated in a representative population of 1,000 critically ill adult patients for (i) guideline dosing (800-mg loading and 400-mg maintenance dosing adjusted to renal function), (ii) guideline dosing followed by therapeutic drug monitoring (TDM)-guided dose adjustment, and (iii) model-optimized dose selection based on patient factors (without TDM). Assuming a MIC of 2 mg/liter, free fluconazole 24-h area under the curve (fAUC24) targets of ≥200 mg · h/liter and <800 mg · h/liter were used for assessment of target attainment. Guideline dosing resulted in underexposure in 21% of patients at 48 h and in 23% of patients at 336 h. The TDM-guided strategy did not influence 0- to 48-h target attainment due to inherent procedural delays but resulted in 37% of patients being underexposed at 336 h. Model-optimized dosing resulted in ≥98% of patients meeting efficacy targets throughout the treatment course, while resulting in less overexposure compared with guideline dosing (7% versus 14%) at 336 h. Model-optimized dose selection enables fluconazole dose individualization in critical illness from the outset of therapy and should enable reevaluation of the comparative effectiveness of this drug in patients with severe fungal infections.

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.

Sign in / Sign up

Export Citation Format

Share Document