Urea kinetics and dialysis treatment time predict vancomycin elimination during high-flux hemodialysis

Population Pharmacokinetics of Vancomycin in Patients Receiving Hemodialysis in a Malian and a French Center and Simulation of the Optimal Loading Dose

PURPOSE

Vancomycin dosing remains challenging in patients receiving intermittent hemodialysis, especially in developing countries, where access to therapeutic drug monitoring and model-based dose adjustment services is limited. The objectives of this study were to describe vancomycin population PK in patients receiving hemodialysis in a Malian and French center and examine the optimal loading dose of vancomycin in this setting.

METHODS

Population pharmacokinetic analysis was conducted using Pmetrics in 31 Malian and 27 French hemodialysis patients, having a total of 309 vancomycin plasma concentrations. Structural and covariate analyses were based on goodness-of-fit criteria. The final model was used to perform simulations of the vancomycin loading dose, targeting a daily area under the concentration-time curve (AUC) of 400-600 mg.h/L or trough concentration of 15-20 mg/L at 48 hours.

RESULTS

After 48 hours of therapy, 68% of Malian and 63% of French patients exhibited a daily AUC of <400. The final model was a 2-compartment model, with hemodialysis influencing vancomycin elimination and age influencing the vancomycin volume distribution. Younger Malian patients exhibited a lower distribution volume than French patients. Dosing simulation suggested that loading doses of 1500, 2000, and 2500 mg would be required to minimize underexposure in patients aged 30, 50, and 70 years, respectively.

CONCLUSIONS

In this study, a low AUC was frequently observed in hemodialysis patients in Mali and France after a standard vancomycin loading dose. A larger dose is necessary to achieve the currently recommended AUC target. However, the proposed dosing algorithm requires further clinical evaluation.

Model-Informed Precision Dosing of Vancomycin in Adult Patients Undergoing Hemodialysis

Model-informed precision dosing (MIPD) maximizes the probability of successful dosing in patients undergoing hemodialysis. In these patients, area under the concentration-time curve (AUC)-guided dosing is recommended for vancomycin. However, this model is yet to be developed. The purpose of this study was to address this issue. The overall mass transfer-area coefficient (KoA) was used for the estimation of vancomycin hemodialysis clearance. A population pharmacokinetic (popPK) model was developed, resulting in a fixed-effect parameter for nonhemodialysis clearance of 0.316 liters/h. This popPK model was externally evaluated, with a resulting mean absolute error of 13.4% and mean prediction error of -0.17%. KoA-predicted hemodialysis clearance was prospectively evaluated for vancomycin (n = 10) and meropenem (n = 10), with a correlation equation being obtained (slope of 1.099, intercept of 1.642; r = 0.927, P < 0.001). An experimental evaluation using an in vitro hemodialysis circuit validated the developed model of KoA-predicted hemodialysis clearance using vancomycin, meropenem, vitamin B6, and inulin in 12 hemodialysis settings. This popPK model indicated a maximum a priori dosing for vancomycin-a loading dose of 30 mg/kg, which achieves the target AUC for 24 h after first dose with a probability of 93.0%, ensured by a predialysis concentration of >15 μg/mL. Maintenance doses of 12 mg/kg after every hemodialysis session could achieve the required exposure, with a probability of 80.6%. In conclusion, this study demonstrated that KoA-predicted hemodialysis clearance may lead to an upgrade from conventional dosing to MIPD for vancomycin in patients undergoing hemodialysis.

Vancomycin Sequestration in ST Filters: An In Vitro Study

BACKGROUND

Sequestration of vancomycin in ST^® filters used in continuous renal therapy is a pending question. Direct vancomycin-ST^® interaction was assessed using the in vitro NeckEpur^® technology.

METHOD

ST150^® filter and Prismaflex dialyzer, Baxter-Gambro, were used. Two modes were assessed in duplicate: (i) continuous diafiltration (CDF): 4 L/h, (ii) continuous dialysis (CD): 2.5 L/h post-filtration.

RESULTS

The mean initial vancomycin concentration in the central compartment (CC) was 51.4 +/- 5.0 mg/L. The mean percentage eliminated from the CC over 6 h was 91 +/- 4%. The mean clearances from the CC by CDF and CD were 2.8 and 1.9 L/h, respectively. The mean clearances assessed using cumulative effluents were 4.4 and 2.2 L/h, respectively. The mean percentages of the initial dose eliminated in the effluents from the CC by CDF and CD were 114 and 108% with no detectable sequestration of vancomycin in both modes of elimination.

DISCUSSION

Significant sequestration adds a clearance to that provided by CDF and CD. The study provides multiple evidence from the CC, the filter, and the effluents of the lack of an increase in total clearance in comparison with the flow rates without significant sequestration in the ST^® filter comparing cumulative effluents to the initial dose in the CC.

CONCLUSIONS

There is no evidence ST^® filters directly sequestrate vancomycin.

Comparison of area under the curve for vancomycin from one- and two-compartment models using sparse data

BACKGROUND AND OBJECTIVE

Vancomycin pharmacokinetics have been described by both one- and two-compartment models. One-compartment models are widely used to predict the area under the curve (AUC), a useful parameter for determining the efficacy and safety of vancomycin, based on sparse data collected during therapeutic drug monitoring. It is uncertain whether AUCs from one-compartment models with sparsely sampled data can sufficiently represent the true AUC. This study aimed to compare AUC estimates from one- and two-compartment models using sparse data. The reliability of AUCs from models constructed with trough-only data was also assessed.

METHODS

A previously published robust model was used to simulate vancomycin concentration points at 15 min intervals in 100 patients. From these simulated data, the reference AUC (AUC_ref) was calculated and two depleted dataset versions (trough-only and peak-trough datasets) were also created. One- and two-compartment models were built from the depleted datasets with the use of NONMEM. Vancomycin 24-hour AUC was calculated from concentration-time profiles of each model by a linear trapezoidal formula at three different time periods: 0-24 hours (AUC_0-24), 24-48 hours (AUC_24-48) and 0-48 hours (AUC_avg). The deviation of each of the AUCs from the AUC_ref was examined to assess the AUC predictability of models from sparse data. The difference in AUCs between one- and two-compartment models was analysed from statistical and clinical perspectives.

RESULTS

When assessing the deviation of each AUC from the AUC_ref, the one-compartment model from both peak-trough and trough-only data could adequately represent the true AUC with no statistically significant differences. Two-compartment model from peak-trough data also provided similar AUC estimates with the AUCref. However, AUCs from the two-compartment model with trough-only data did not adequately represent the true AUC, with significant differences of 25.16% for AUC_0-24, 15.92% for AUC_24-48 and 19.45% for AUC_avg.

CONCLUSION

Regardless of statistically significant differences between AUCs from one- and two-compartment models, the level of difference was acceptable from the clinical perspective, being <17% in models from peak-trough data. Therefore, both one- and two-compartment models with sparse data having at least a pair of peak-trough data per patient could be reliable for predicting AUC. Furthermore, AUCs of the one-compartment model from trough-only data did not show a significant difference from the AUC_ref. Hence, one-compartment models developed from trough-only data could be useful for predicting AUC when models with rich data are not available for the intended population. However, it is suggested that the use of the two-compartment model built from trough-only data should be avoided.

A population pharmacokinetic model of vancomycin for dose individualization based on serum cystatin C as a marker of renal function

OBJECTIVES

This study aimed to establish a vancomycin population pharmacokinetics (PPK) model based on serum cystatin C and to optimize dosing for achieving targeted steady-state trough concentrations (C_ss ) of 10-15 and 15-20 mg/l.

METHODS

Patients aged ≥18 years were prospectively enrolled. A vancomycin PPK model was built with glomerular filtration rate (GFR) as a renal covariate estimated by cystatin C. A new group of patients were used for external evaluation. PPK analysis and Monte Carlo simulations were performed using nonlinear mixed effect modelling programme.

KEY FINDINGS

Two hundreds of patients with 514 samples were included. The final model was CL (L/h) = (5.07 × (GFR/105.5)^0.524 × (AGE/48.5)^-0.309 × (WT/60)^0.491 ); V (l) = 46.3. Internal and external evaluations demonstrated good stability and predictability. The average probability of target attainment (PTA) of optimal dosing regimens for targeted C_ss achieving 10-15 and 15-20 mg/l were 51.2% and 40.6%, respectively. An average PTA ≥71% for targeted concentration of 10-20 mg/l was obtained.

CONCLUSIONS

A vancomycin PPK model with cystatin C as the renal marker has good stability and predictability. The new proposed dosing regimens were predicted to achieve a good PTA.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting

OBJECTIVES

Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated.

METHOD

Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting.

RESULTS

A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions.

CONCLUSION

There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.

Vancomycin therapeutic drug monitoring and population pharmacokinetic models in special patient subpopulations

Vancomycin is a fundamental antibiotic in the management of severe Gram-positive infections. Inappropriate vancomycin dosing is associated with therapeutic failure, bacterial resistance and toxicity. Therapeutic drug monitoring (TDM) is acknowledged as an important part of the vancomycin therapy management, at least in specific patient subpopulations, but implementation in clinical practice has been difficult because there are no consensus and agglutinator documents. The aims of the present work are to present an overview of the current knowledge on vancomycin TDM and population pharmacokinetic (PPK) models relevant to specific patient subpopulations. Based on three published international guidelines (American, Japanese and Chinese) on vancomycin TDM and a bibliographic review on available PPK models for vancomycin in distinct subpopulations, an analysis of evidence was carried out and the current knowledge on this topic was summarized. The results of this work can be useful to redirect research efforts to address the detected knowledge gaps. Currently, TDM of vancomycin presents a moderate level of evidence and practical recommendations with great robustness in neonates, pediatric and patients with renal impairment. However, it is important to investigate in other subpopulations known to present altered vancomycin pharmacokinetics (eg neurosurgical, oncological and cystic fibrosis patients), where evidence is still unsufficient.

Clearance of meperidine and its metabolite normeperidine in hemodialysis patients with chronic noncancer pain

CONTEXT

Normeperidine accumulates in patients with impaired renal function and may cause central neurotoxicity. However, some uremic patients still undergo meperidine treatment for chronic pain.

OBJECTIVES

To prevent normeperidine side effects and complications, we investigated the clearance rate and extraction ratio of meperidine and normeperidine in hemodialysis patients with chronic pain.

METHODS

Three hemodialysis patients, with diagnoses of chronic pancreatitis, chronic back pain, and intractable intra-abdominal pain, received long-term (more than six months) administration of meperidine for chronic noncancer pain. During regular hemodialysis, 72 blood samples in total were collected from the afferent port, efferent port, and ultradiafiltrate port at eight time points. The plasma concentrations of meperidine and normeperidine were determined by high-performance liquid chromatography.

RESULTS

The prehemodialysis plasma concentrations of meperidine and normeperidine were 2963 ± 315 and 2369 ± 1974 ng/mL, which declined to 591 ± 109 and 853 ± 765 ng/mL, with 80% and 65% reduction, respectively. The plasma clearance and extraction ratios of meperidine were 22.7 ± 9.8 mL/minute and 10.1 ± 5.6% and for normeperidine 26.0 ± 11.4 mL/minute and 10.8 ± 2.5%, respectively.

CONCLUSION

Hemodialysis can efficiently remove meperidine and its active metabolite, normeperidine, in uremic patients receiving long-term meperidine therapy for chronic noncancer pain.

Population pharmacokinetic parameters of vancomycin in critically ill patients

BACKGROUND

Intensive care unit patients are a highly heterogeneous population. Accurate dosing for this population requires characterization of the appropriate pharmacokinetic parameters.

OBJECTIVE

To estimate population pharmacokinetic parameters of vancomycin (VAN) in adult critically ill patients and to establish the predictive performance of the resulting model.

PATIENTS AND METHOD

Fifty critically ill patients with suspected or documented infection with VAN-sensitive micro-organisms were included. Thirty patients and 234 serum concentration-time sets obtained during clinical routine monitoring were used to estimate the pharmacokinetic parameters (group A). An open bicompartimental model with intermittent intravenous administration was used to adjust the data. Data were evaluated using a nonlinear mixed effects model (nonmem software). Forty plasma concentration-time data sets from 20 patients were used for validation using the Bayesian method (group B).

RESULTS

There was a linear relationship between creatinine clearance (Cl(cr)) and VAN clearance (Cl(VAN)). The inclusion of the non-renal clearance (Cl(nr)) (intercept of Cl(VAN) vs. Cl(cr) relationship) improved the model significantly (Cl(nr) 17 mL/min). The volume of distribution seems to be larger than previously reported: volume of the central compartment (V(c)) was 0.41 L/kg and volume of the peripheral compartment was (V(p)), 1.32 L/kg. The mean error (bias) and mean absolute error (precision) for predicting subsequent peak concentrations were -2.16 and 9.28 mg/L and for trough concentrations, -0.22 and 3.87 mg/L respectively.

CONCLUSION

The use of population-specific pharmacokinetic parameters and Bayesian forecasting improves dosage-regimen design.

Levofloxacin pharmacokinetics in ESRD and removal by the cellulose acetate high performance-210 hemodialyzer

BACKGROUND

No published data are available describing the pharmacokinetics of intravenous levofloxacin in patients with end-stage renal disease (ESRD). Objectives of this study are to determine the pharmacokinetics and dialytic clearance of levofloxacin and develop dosing strategies in these patients.

METHODS

Eight noninfected subjects receiving long-term thrice-weekly hemodialysis, with no measurable residual renal function, were administered intravenous levofloxacin, 250 mg, over 1 hour after a scheduled hemodialysis session. Blood samples were collected serially during the interdialytic period, during the next intradialytic period, and immediately after the next hemodialysis session. Serum concentrations of levofloxacin were determined by high-performance liquid chromatography. Differential equations describing a 2-compartment open-infusion pharmacokinetic model were fit to each individual subject's serum concentration-time data by iterative nonlinear weighted least-squares regression analysis using Adapt II (Biomedical Simulations Resource, University of Southern California, Los Angeles, CA). Ratios of maximum serum concentration (C(max)) to minimum inhibitory concentration (MIC) were calculated for common respiratory pathogens by using MIC for 90% of isolates (MIC90) data from published studies.

RESULTS

All subjects completed the study, and no adverse events were reported. Median systemic clearance, volume of distribution at steady state, elimination half-life, and C(max) were 37.0 mL/min (range, 12.8 to 42.7 mL/min), 103.3 L (range, 39.8 to 139.3 L), 34.4 hours (range, 28.4 to 39.3 hours), and 5.2 microg/mL (range, 4.1 to 11.3 microg/mL), respectively. Median dialytic clearance and levofloxacin reduction ratios were 84.4 mL/min (range, 61.8 to 107.6 mL/min) and 0.244 (range, 0.181 to 0.412), respectively. Median C(max)-MIC90 ratios were 10 or greater for Haemophilus influenzae, Moraxella catarrhalis, Enterobacter cloacae, and Klebsiella pneumoniae, approximately 5 for Streptococcus pneumoniae, and less than 1 for Pseudomonas aeruginosa.

CONCLUSION

The administration of levofloxacin to patients with ESRD as 500 mg initially, followed by 250 mg every 48 hours, will provide adequate C(max)-MIC ratios after the first and subsequent doses for most patients with respiratory tract infections caused by organisms with levofloxacin MICs of 1 microg/mL or less.

Increased cortisol metabolites and reduced activity of 11beta-hydroxysteroid dehydrogenase in patients on hemodialysis

BACKGROUND

Patients with renal failure have symptoms assumed to be attributable to the accumulation of toxic endo- or xenobiotics. Most of these molecules, especially those with a molecular weight>300 D, have not been identified. In addition to excretion, the kidney is involved in some defined metabolic processes. In the cortical collecting duct, the enzyme 11beta-hydroxysteroid dehydrogenase type 2 (11beta-HSD2) interconverts cortisol (F) and cortisone (E), and the metabolites of these glucocorticoids, tetrahydrocortisol (THF), 5alpha-tetrahydrocortisol (5alpha-THF) and tetrahydrocortisone (THE), are excreted in urine. We hypothesized that first, these metabolites accumulate and second, their concentration pattern changes in patients on hemodialysis.

METHODS

THF, 5alpha-THF, THE, F and E were measured in plasma of 63 patients on dialysis and in 34 healthy controls by gas-chromatography-mass spectrometry (GC/MS). In 11 patients, the metabolite clearance was determined during high flux hemodialysis by using a population pharmacokinetic approach.

RESULTS

Mean plasma concentrations of THF, 5alpha-THF and THE were more than five times higher and those of E lower in patients than in controls. The ratios of (THF + 5alpha-THF)/THE and F/E were increased in patients, indicating a reduced activity of 11beta-HSD2. Intradialytic clearances were between 120 and 300 mL/min and not sufficient to normalize the steroid concentrations.

CONCLUSION

Patients on hemodialysis exhibit pronounced increases in THF, 5alpha-THF and THE concentrations in plasma with insufficient removal during dialysis. Due to a reduced 11beta-HSD2 activity, an abnormal pattern of the concentrations of these cortisol and cortisone metabolites is observed. Since many signs and symptoms in uremic patients resemble those observed in subjects with glucocorticoid excess, the clinical relevance of the high concentrations of these glucocorticoid metabolites deserves further investigation.

Clinical review: use of vancomycin in haemodialysis patients

Following intravenous administration, vancomycin is poorly metabolized and is mainly excreted unchanged in urine. Total body clearance is thus dependent on the kidney, and is correlated with glomerular filtration rate and creatinine clearance. Accumulation of vancomycin in patients with renal insufficiency may therefore occur, and this may lead to toxic side effects if dosage is not modified according to the degree of renal failure. Furthermore, vancomycin easily diffuses through dialysis membranes. The aim of the present review is to establish guidelines for handling this drug in such patients. We indicate how and when plasma concentrations of vancomycin should be determined in dialysis patients.

Cefazolin dialytic clearance by high-efficiency and high-flux hemodialyzers

Cefazolin dialytic clearance has not been determined in patients undergoing hemodialysis with high-efficiency or high-flux dialyzers. The objective of this study is to determine the pharmacokinetics and dialytic clearance of cefazolin and develop dosing strategies in these patients. Twenty-five uninfected subjects undergoing chronic thrice-weekly hemodialysis were administered a single dose of intravenous cefazolin (15 mg/kg) after their standard hemodialysis session. Fifteen subjects underwent hemodialysis with high-efficiency hemodialyzers, and 10 subjects underwent hemodialysis with high-flux hemodialyzers. Blood and urine samples were collected serially over the interdialytic period, during the next intradialytic period, and immediately after the next hemodialysis session. Serum and urine concentrations of cefazolin were determined by high-performance liquid chromatography. Differential equations describing a two-compartment model were fit to the cefazolin serum concentration-time data over the study period, and pharmacokinetic parameters were determined. Mean dialytic clearance values for cefazolin were significantly greater in the high-flux group compared with the high-efficiency group (30.9 +/- 6.52 versus 18.0 +/- 6.26 mL/min, respectively; P: < 0.05). Cefazolin reduction ratios were significantly greater (0.62 +/- 0.08 versus 0.50 +/- 0.07; P: < 0.005) in the high-flux group compared with the high-efficiency group and correlated well with equilibrated urea reduction. The pharmacokinetic model developed from patient data was used to simulate cefazolin serum concentration data for high-efficiency and high-flux dialyzers. Cefazolin doses of 15 or 20 mg/kg after each hemodialysis session maintained adequate serum concentrations throughout a 2- or 3-day interdialytic period regardless of hemodialyzer type.