Population Pharmacokinetics of Vancomycin in Postoperative Neurosurgical Patients and the Application in Dosing Recommendation

Population pharmacokinetics of rivaroxaban in Chinese deep vein thrombosis patients and the exposure simulation for dosing recommendation

This study aimed to develop a population pharmacokinetic (PPK) model for rivaroxaban and establish a model-based dosing guideline tailored to Chinese patients with deep vein thrombosis (DVT). A nonlinear mixed-effects modeling approach was employed using Phoenix NLME 7.0 software to construct the PPK model for rivaroxaban. The PK of rivaroxaban was adequately characterized through a one-compartment model. Monte Carlo simulations were employed to formulate dosing guidelines applicable to different patient subgroups. Data from 60 Chinese DVT patients yielded 217 rivaroxaban plasma concentrations for analysis. The apparent clearance (CL/F) of rivaroxaban was found to be significantly influenced by the estimated glomerular filtration rate (eGFR), identified as a major covariate. Based on Monte Carlo simulations, for the acute DVT treatment, a regimen of 15 mg, 10 mg, or 5 mg twice daily was associated with the highest total probability target attainment (PTAtotal) in patients with normal, mildly impaired, or moderately impaired renal function, respectively. For the continued DVT treatment, a regimen of 20 mg, 15 mg, or 5 mg once daily exhibited the maximum PTAtotal in patients with normal, mildly impaired, or moderately impaired renal function, respectively. The recommendation label dose achieved the PK target in those with normal renal function. However, for patients with mild or moderate renal impairment, dose adjustments below the label recommendation might be necessary. The PPK model associated CL/F with the covariate eGFR. Utilizing the PPK model, a dosage regimen table was constructed to offer tailored dosing recommendations for Chinese DVT patients.

External Validation of Obese/Critically Ill Vancomycin Population Pharmacokinetic Models in Critically Ill Patients Who Are Obese

Obesity combined with critical illness might increase the risk of acquiring infections and hence mortality. In this patient population the pharmacokinetics of antimicrobials vary significantly, making antimicrobial dosing challenging. The objective of this study was to assess the predictive performance of published population pharmacokinetic models of vancomycin in patients who are critically ill or obese for a cohort of critically ill patients who are obese. This was a multi-center retrospective study conducted at 2 hospitals. Adult patients with a body mass index of ≥30 kg/m2 were included. PubMed was searched for published population pharmacokinetic studies in patients who were critically ill or obese. External validation was performed using Monolix software. A total of 4 models were identified in patients who were obese and 5 models were identified in patients who were critically ill. In total, 138 patients who were critically ill and obese were included, and the most accurate models for these patients were the Goti and Roberts models. In our analysis, models in patients who were critically ill outperformed models in patients who were obese. When looking at the most accurate models, both the Goti and the Roberts models had patient characteristics similar to ours in terms of age and creatinine clearance. This indicates that when selecting the proper model to apply in practice, it is important to account for all relevant variables, besides obesity.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients

OBJECTIVE

Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset.

METHOD

PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance.

RESULT

A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting.

CONCLUSION

The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

Plasma and Cerebrospinal Fluid Population Pharmacokinetics of Vancomycin in Patients with External Ventricular Drain

Vancomycin is a commonly used antibacterial agent in patients with primary central nervous system (CNS) infection. This study aims to examine predictors of vancomycin penetration into cerebrospinal fluid (CSF) in patients with external ventricular drainage and the feasibility of CSF sampling from the distal drainage port for therapeutic drug monitoring. Fourteen adult patients (9 with primary CNS infection) were treated with vancomycin intravenously. The vancomycin concentrations in blood and CSF (from proximal [CSF_P] and distal [CSF_D] drainage ports) were evaluated by population pharmacokinetics. Model-based simulations were conducted to compare various infusion modes. A three-compartment model with first-order elimination best described the vancomycin data. Estimated parameters included clearance (CL, 4.53 L/h), central compartment volume (Vc, 24.0 L), apparent CSF compartment volume (VCSF, 0.445 L), and clearance between central and CSF compartments (QCSF, 0.00322 L/h and 0.00135 L/h for patients with and without primary CNS infection, respectively). Creatinine clearance was a significant covariate on vancomycin CL. CSF protein was the primary covariate to explain the variability of QCSF. There was no detectable difference between the data for sampling from the proximal and the distal port. Intermittent infusion and continuous infusion with a loading dose reached the CSF target concentration faster than continuous infusion only. All infusion schedules reached similar CSF trough concentrations. Beyond adjusting doses according to renal function, starting treatment with a loading dose in patients with primary CSF infection is recommended. Occasionally, very high and possibly toxic doses would be required to achieve adequate CSF concentrations, which calls for more investigation of direct intraventricular administration of vancomycin. (This study has been registered at ClinicalTrials.gov under registration no. NCT04426383).

Plasma and Cerebrospinal Fluid Population Pharmacokinetics of Meropenem in Neurocritical Care Patients: a Prospective Two-Center Study

Morbidity and mortality related to ventriculitis in neurocritical care patients remain high. Antibiotic dose optimization may improve therapeutic outcomes. In this study, a population pharmacokinetic model of meropenem in infected critically ill patients was developed. We applied the final model to determine optimal meropenem dosing regimens required to achieve targeted cerebrospinal fluid exposures. Neurocritical care patients receiving meropenem and with a diagnosis of ventriculitis or extracranial infection were recruited from two centers to this study. Serial plasma and cerebrospinal fluid samples were collected and assayed. Population pharmacokinetic modeling and Monte Carlo dosing simulations were performed using Pmetrics. We sought to determine optimized dosing regimens that achieved meropenem cerebrospinal fluid concentrations above pathogen MICs for 40% of the dosing interval, or a higher target ratio of meropenem cerebrospinal fluid trough concentrations to pathogen MIC of ≥1. In total, 53 plasma and 34 cerebrospinal fluid samples were obtained from eight patients. Meropenem pharmacokinetics were appropriately described using a three-compartment model with linear plasma clearance scaled for creatinine clearance and cerebrospinal fluid penetration scaled for patient age. Considerable interindividual pharmacokinetic variability was apparent, particularly in the cerebrospinal fluid. Percent coefficients of variation for meropenem clearance from plasma and cerebrospinal fluid were 41.7% and 89.6%, respectively; for meropenem, the volume of distribution in plasma and cerebrospinal fluid values were 63.4% and 58.3%, respectively. High doses (up to 8 to 10 g/day) improved attainment of meropenem cerebrospinal fluid target exposures, particularly for less susceptible organisms (MICs, ≥0.25 mg/L). Standard meropenem doses of 2 g every 8 h may not achieve effective concentrations in cerebrospinal fluid in all critically ill patients. Higher doses, or alternative dosing methods (e.g., loading dose followed by continuous infusion) may be required to optimize cerebrospinal fluid exposures. Doses of up to 8 to 10 g/day either as intermittent boluses or continuous infusion would be suitable for patients with augmented renal clearance; lower doses may be considered for patients with impaired renal function as empirical suggestions. Ongoing dosing should be tailored to the individual patient circumstances. Notably, the study population was small and dosing recommendations may not be generalizable to all critically ill patients.

The CSF Vancomycin Concentration in Patients With Post-operative Intracranial Infection Can Be Predicted by the WBCs to Total Cells Ratio and the Serum Trough Concentration

Background

The pharmacokinetics of vancomycin in cerebrospinal fluid (CSF) is an important basis for evaluating the bactericidal effect. The accuracy of using serum vancomycin concentrations only to estimate the CSF concentrations remains controversial, may lead to underdosing.

Objectives

The aims of this study were to evaluate the vancomycin exposure in CSF, investigate the factors affecting the vancomycin blood–brain barrier (BBB) penetration, and to establish the prediction model of vancomycin concentration in CSF.

Methods

Eligible patients were included and given a standard dose of vancomycin. At the fifth dose, the blood and CSF samples were collected 0.5 h before the start of infusion of vancomycin, and 1, 2, 3, and 8 h from the start of infusion, and were measured by the enzyme-multiplied immunoassay technique using the Siemens Viva-E Drug Testing System.

Results

The AUC_CSF/serum of patients with intracranial infection was higher than that of patients without (p = 0.001). The CSF concentration was relatively stable between dosing periods (p = 0.095). The area under the concentration–time curve (AUC) ratio of CSF to serum (AUC_CSF/serum) in patients with intracranial infection ranged from 15.1 to 80.1% (33.23 ± 19.31%; median, 26.25%). The CSF vancomycin AUC levels were affected by the serum trough concentration (B: 5.23 ± 2.36, t = 2.22, p = 0.039), and were mainly affected by the CSF white blood cells (WBCs)/total cells (B: 113.96 ± 35.10, t = 3.25, p = 0.004) (Y = −17.86 + 5.23 × serum trough concentration + 113.96 × CSF [WBCs/total cells]; R² = 0.473, F = 8.542, p = 0.002).

Conclusions

After intravenous administration of vancomycin, the CSF concentration curve was fluctuated gently. The CSF vancomycin concentration in patients with postoperative intracranial infection can be predicted by the WBCs to total cells ratio and the serum trough concentration, and help to adjust the administration of vancomycin.

Augmented Renal Clearance in Severe Infections-An Important Consideration in Vancomycin Dosing: A Narrative Review

Vancomycin is a hydrophilic antibiotic widely used in severe infections, including bacteremia and central nervous system (CNS) infections caused by Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), coagulase-negative staphylococci and enterococci. Appropriate antimicrobial dosage regimens can help achieve the target exposure and improve clinical outcomes. However, vancomycin exposure in serum and cerebrospinal fluid (CSF) is challenging to predict due to rapidly changing pathophysiological processes and patient-specific factors. Vancomycin concentrations may be decreased for peripheral infections due to augmented renal clearance (ARC) and increased distribution caused by systemic inflammatory response syndrome (SIRS), increased capillary permeability, and aggressive fluid resuscitation. Additionally, few studies on vancomycin’s pharmacokinetics (PK) in CSF for CNS infections. The relationship between exposure and clinical response is unclear, challenging for adequate antimicrobial therapy. Accurate prediction of vancomycin pharmacokinetics/pharmacodynamics (PK/PD) in patients with high interindividual variation is critical to increase the likelihood of achieving therapeutic targets. In this review, we describe the interaction between ARC and vancomycin PK/PD, patient-specific factors that influence the achievement of target exposure, and recent advances in optimizing vancomycin dosing schedules for severe infective patients with ARC.

Predictive performance of reported vancomycin population pharmacokinetic model in patients with different renal function status, especially those with augmented renal clearance

BACKGROUND

There is a significant correlation between augmented renal clearance (ARC) and lower serum trough concentrations of vancomycin (VCM) during therapy. There is a need to evaluate the predictive performance of the population pharmacokinetic (PPK) model used for individual calculation of dosage regimens in ARC patients.

OBJECTIVE

Our study aimed to estimate the predictive performance differences of the reported VCM PPK software JPKD-vancomycin and SmartDose in patients with varying renal function status, especially those with ARC.

METHODS

Patients receiving VCM treatment from May 2014 to December 2019 were enrolled, and divided into the ARC group, the normal renal function (NRF) group, and the impaired renal function (IRF) group. VCM dosage, trough concentration, area under the curve (AUC) and pharmacokinetic parameters were compared among the three groups. The predictive performance of PPK software was expressed using absolute prediction error (APE), sensitivity, specificity, and regression coefficient (r²) of linear regression analysis between the measured VCM trough concentration and the predicted trough concentration.

RESULTS

A total of 388 patients were included: 86 patients in the ARC group, 241 patients in the NRF group, and 61 patients in the IRF group. The daily dose of the adjusted regimen in the ARC group was higher than in the NRF group, but the trough concentration was significantly lower than in the NRF group (2.8±0.6 g vs 1.9±0.6 g, p<0.001; 10.5±5.1 mg/L vs 12.9±6.8 mg/L, p=0.030). The percentage of trough concentrations lower than 10 mg/L was 84.9% in the ARC group. Compared with the APE of the initial dosage regimen, the APE of the adjusted regimen calculated by JPKD was lower in the ARC group (p=0.041) and the NRF group (p＜0.001). Specificity of JPKD and SmartDose in the ARC group was higher than in the NRF group (p<0.001; p<0.001). According to the linear regression analysis, the coefficients of determination (r²) were all >0.6 for the initial regimen and adjusted regimen of VCM in the ARC and NRF groups, and the r² of the adjusted regimen of JPKD was >0.8 in the ARC and NRF groups. In the IRF group, 31.1% of patients had a change in serum creatinine (Scr) level of >50%. The r² increased from 0.527 to 0.7347 in SmartDose and from 0.55 to 0.7802 in JPKD when using Scr at the sampling time. The ARC group showed a significant decrease in AUC (p<0.001) and an increase in clearance rate (p<0.001) when compared to the NRF group.

CONCLUSION

ARC was significantly associated with subtherapeutic serum VCM concentration. The pharmacokinetic parameters of VCM were diverse in patients with different renal function status. The PPK model JPKD and SmartDose had a good predictive performance for predicting VCM trough concentrations of the ARC and NRF patients, especially using JPKD for prediction of the adjusted regimen. The change of Scr is a main factor affecting the accuracy of software prediction.

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.

Population pharmacokinetics of vancomycin in patients with external ventricular drain-associated ventriculitis

BACKGROUND

To determine the distribution of vancomycin into the cerebrospinal fluid (CSF) in patients with external ventricular drain (EVD)-associated ventriculitis, the pharmacokinetics of vancomycin were evaluated and covariate relationships explored.

METHODS

For the population pharmacokinetic model patients were recruited in a neurocritical care unit at the University Hospital of Muenster in the period between January 2014 and June 2015. All patients had a clinical evidence of EVD-associated ventriculitis. Population pharmacokinetic analysis of vancomycin was performed using NONMEM.

RESULTS

A total of 184 blood and 133 CSF samples were collected from 29 patients. The final population pharmacokinetic model is a three-compartment model with linear elimination. Creatinine clearance (Cl_Cr ) and CSF-lactate were detected as significant covariates, showing that the total vancomycin plasma clearance (Cl) depends on Cl_Cr and furthermore the clearance (Cl_dif ) between the central and CSF compartment correlates with CSF lactate concentration. Based on the final model, the following values were estimated by NONMEM: Cl = 5.15 L/h, Q (intercompartmental clearance) = 3.31 L/h, Cl_dif  = 0.0031 L/h, V_central  = 42.1 L, V_CSF  = 0.32 L and the value of V_peripheral was fixed to 86.2 L. With the developed pharmacokinetic model, area under the curve (AUC) values as well as CSF trough levels were simulated.

CONCLUSION

Based on our analysis, the dosing of vancomycin should be referred to the degree of inflammation (derived from the CSF lactate concentration) and renal function (derived from Cl_Cr ).

Impact of gender, albumin, and CYP2C19 polymorphisms on valproic acid in Chinese patients: a population pharmacokinetic model

OBJECTIVE

This prospective study aimed to establish the valproic acid (VPA) population pharmacokinetic model in Chinese patients and realise personalised medication on the basis of population pharmacokinetics.

METHODS

The patients' clinical information and VPA plasma concentrations were collected from The General Hospital of Taiyuan Iron & Steel (Group) Corporation (TISCO). Nonlinear mixed-effect modelling was used to build the population pharmacokinetic model. To characterise the pharmacokinetic data, a one-compartment pharmacokinetic model with first-order absorption and elimination was used. The first-order conditional estimation with η-ε interaction was applied throughout the model-developing procedure. The absorption rate constant (Ka) was fixed at 2.38 hour^-1, and the impact of covariates on clearance and apparent volume of distribution were also explored. Medical records of 60 inpatients were reviewed prospectively and the objective function value (OFV) of the base model and final model were 851.813 and 817.622, respectively.

RESULTS

Gender was identified as the covariate that had a significant impact on the volume of distribution, and albumin and CYP2C19 genotypes influenced clearance.

CONCLUSION

Bootstrap and VPC indicated that a reliable model had been developed that was based on the simulation results, and a simple-to-use dosage regimen table was created to guide clinicians for VPA drug dosing.

Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients

OBJECTIVES

Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance.

METHODS

Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression.

RESULTS

Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p = 0.007) significantly influenced the performance of the Goti model.

CONCLUSIONS

The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

CSF penetration of vancomycin in critical care patients with proven or suspected ventriculitis: a prospective observational study

BACKGROUND

Vancomycin is recommended for ventriculitis. However, penetration into the CNS is relatively poor.

OBJECTIVES

To investigate the population pharmacokinetics of vancomycin in serum and CSF in critical care patients with proven or suspected CNS infections from neurosurgical procedures.

PATIENTS AND METHODS

This was an observational pharmacokinetic study in critical care patients with proven or suspected CNS infections receiving intravenous vancomycin. Multiple blood and intraventricular CSF samples were collected. Population pharmacokinetic analysis and simulation were undertaken with ADAPT5 and Pmetrics.

RESULTS

A total of 187 blood and CSF samples were collected from 21 patients. The median (range) Cmax and Cmin concentrations in serum were 25.67 (10.60-50.78) and 9.60 (4.46-23.56) mg/L, respectively, with a median daily dose of 2500 (500-4000) mg. The corresponding median concentrations in CSF were 0.65 (<0.24-3.83) mg/L and 0.58 (<0.24-3.95) mg/L, respectively. The median AUC0-24 in serum and CSF was 455.09 and 14.10 mg·h/L, respectively. A three-compartment linear population pharmacokinetic model best fitted the observed data. Vancomycin demonstrated poor penetration into CSF, with a median CSF/serum ratio of 3% and high intersubject pharmacokinetic variability of its penetration.

CONCLUSIONS

Therapeutic drug monitoring in both serum and CSF and higher daily doses may be an option to ensure adequate trough levels and to optimize patient therapy. Novel dosing strategies designed to reduce renal toxicity, such as administration by continuous infusion, should be investigated in further clinical studies to avoid antibiotic underexposure in CSF.

Development and comparison of population pharmacokinetic models of vancomycin in neurosurgical patients based on two different renal function markers

WHAT IS KNOWN AND OBJECTIVES

Some previous studies have indicated that serum cystatin C (Cys C) is a better marker than serum creatinine (SCR) for assessing the glomerular filtering rate (GFR). However, in almost all population pharmacokinetic models of vancomycin, the GFR is usually estimated from SCR. Therefore, the aim of this study was to compare the GFR estimated from SCR (sGFR) with the GFR estimated from Cys C (cGFR) and investigate which one can describe the characteristics of vancomycin population pharmacokinetics better in Chinese neurosurgical adult patients.

METHODS

Patients from the Neurosurgery Department aged ≥18 years were enrolled retrospectively. Among these patients, the data from 222 patients were used to establish two population pharmacokinetic models based on sGFR and cGFR, separately. The data from another 95 patients were used for the external validation of these two models. Non-linear mixed-effect modelling (NONMEM) 7.4.3 was used for the population pharmacokinetic analysis.

RESULTS

We developed two one-compartment models with first-order absorption based on Cys C and SCR, separately. In the Cys C model, age, body weight and cGFR were significant covariates on the clearance rate (CL) of vancomycin (typical value, 6.4 L/hour). In the SCR model, age and sGFR were significant covariates on the CL (typical value, 6.46 L/hour). The external validation results showed that the predictive performance of the two models was similar.

WHAT IS NEW AND CONCLUSION

In this study, the predictive performance of two models was similar in neurosurgical patients. We did not find a significant improvement in the predictive performance of the model when GFR was estimated from Cys C.

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.

Population Pharmacokinetic Modeling of Azithromycin Eyedrops in Tears Following Single-Dose Topical Administration in Healthy Volunteers

BACKGROUND AND OBJECTIVES

The disposition of azithromycin in the human eye following topical administration has not been fully explored. Population pharmacokinetic (PopPK) modeling can allow useful conclusions to be drawn based on limited tear sampling data. The aim of this study was therefore to develop and evaluate a PopPK model of azithromycin eyedrops in tears, investigate typical model parameters, and identify potential covariates following single-dose ocular instillation.

METHODS

A total of 84 tear samples were obtained from 42 healthy volunteers at seven time points over 24 h following topical administration of azithromycin eyedrops (2.5 mL/25 mg). Azithromycin concentrations in the tears were determined using a validated LC-MS/MS assay. PopPK analysis was performed using nonlinear mixed-effects modeling. Intraocular pressure, tear secretion measurement, age, and gender were evaluated as possible covariates. Bootstrap and visual predictive checks were used simultaneously to evaluate the PopPK model. The dosage regimen was further estimated based on Monte Carlo simulation and the area under the curve/minimal inhibitory concentration.

RESULTS

A linear two-compartment first-order elimination model was found to best describe the pharmacokinetic profile of azithromycin in tears. None of the covariates had a significant influence on the typical model parameters. The final PopPK model was demonstrated to be suitable and effective according to bootstrap and visual predictive checks. Twice-daily instillation of azithromycin eyedrops would appear to provide the required antibacterial activity.

CONCLUSION

A proposed linear two-compartment PopPK model of azithromycin eyedrops was found to be effective at describing the disposition of azithromycin in tears after ocular instillation.

Plasma and cerebrospinal fluid population pharmacokinetic modeling and simulation of meropenem after intravenous and intrathecal administration in postoperative neurosurgical patients

Combined intravenous and local intrathecal administration of meropenem in patients after craniotomy is widely used to treat intracranial infections. However, the optimal dosing regimen of meropenem has not been investigated, posing a risk to treatment efficacy. We aimed to identify significant factors associated with inter-individual variability in cerebrospinal fluid (CSF) pharmacokinetics of meropenem and to evaluate potential intravenous and intrathecal meropenem dosing regimens for the treatment of patients with intracranial infections. After the diagnosis of intracranial infection, 15 patients with an indwelling drain tube received intravenous and intrathecal administration of meropenem. Blood and cerebrospinal fluid (CSF) samples were obtained at the scheduled time to measure meropenem concentration. Plasma and CSF concentration-time data were fit simultaneously using a nonlinear mixed-effects modeling approach. A 3-compartmental model was selected to characterize the in vivo behavior of meropenem. Through population modeling, multiple covariates were tested about their impact on the meropenem pharmacokinetics. Considering CSF outflow via drain tube leading to a drug loss, the drug clearance in CSF (CL_CSF) was added to describe this drug loss. The covariate selection indicated that the drainage volume (mL/d) had a significant positive correlation with CL_CSF. Bootstrap and visual predictive check suggested a robust and reliable pharmacokinetic model was structured. The established final population model was useful to apply with simulation to identify meropenem dosing regimens for the treatment of patients with intracranial infections. With the goal of CSF concentrations exceeding the minimum inhibitory concentration during the therapy, we created a simple to use dosage regimen table to guide clinicians with drug dosing.

Population Pharmacokinetics of Combined Intravenous and Local Intrathecal Administration of Meropenem in Aneurysm Patients with Suspected Intracranial Infections After Craniotomy

BACKGROUND AND OBJECTIVE

For patients with intracranial infection, local intrathecal administration of meropenem may be a useful method to obtain a sufficient drug concentration in the cerebral spinal fluid (CSF). However, a large inter-individual variability may pose treatment efficacy at risk. This study aimed to identify factors affecting drug concentration in the CSF using population pharmacokinetics method.

METHODS

After craniotomy, aneurysm patients with an indwelling lumbar cistern drainage tube who received a combined intravenous and intrathecal administration of meropenem for the treatment of suspected intracranial infection were enrolled. Venous blood and CSF specimens were collected for determining meropenem concentrations. Nonlinear mixed-effects modeling method was used to fit blood and CSF concentrations simultaneously and to develop the population pharmacokinetic model. The proposed model was applied to simulate dosage regimens.

RESULTS

A three-compartmental model was established to describe meropenem in vivo behavior. Lumbar CSF drainage resulted in a drug loss, and drug clearance in CSF (CL_CSF) was employed to describe this. The covariate analysis found that the drainage volume (mL/day) was strongly associated with CL_CSF, and the effect of creatinine clearance was significant on the clearance of meropenem in blood (CL). Visual predictive check suggested that the proposed pharmacokinetic model agreed well with the observations. Simulation showed that both intravenous and intrathecal doses should be increased with the increases of minimum inhibitory concentration and daily CSF drainage volume.

CONCLUSION

This model incorporates covariates of the creatinine clearance and the drainage volume, and a simple to use dosage regimen table was created to guide clinicians with meropenem dosing.

Factors Influencing Norvancomycin Concentration in Plasma and Cerebrospinal Fluid in Patients After Craniotomy and Dosing Guideline: A Population Approach

PURPOSE

Antibacterial spectrum and activity of norvancomycin are comparable with vancomycin, and it has been widely used in China. Norvancomycin can penetrate into the cerebrospinal fluid (CSF) through the damaged blood-brain barrier in patients after craniotomy. Because higher inter-individual variability was observed, we aimed to identify factors related to drug concentration to guide clinicians with norvancomycin dosing.

METHODS

After craniotomy, patients with an indwelling catheter in the operational area/ventricle were intravenously administered norvancomycin. Venous blood and CSF specimens were collected at a scheduled time for measuring drug concentrations. Blood and CSF data were fitted simultaneously with the use of the nonlinear fixed-effects modeling method to develop the population pharmacokinetic model. Covariate analysis was applied to select candidate factors associated with pharmacokinetic parameters. A model-based simulation was performed to find optimized regimens for different subgroups of patients.

FINDINGS

A 3-compartmental model (central, peripheral, and CSF compartments) with 2 elimination pathways (drug elimination from the kidney and CSF outflow) was developed to characterize the in vivo process of norvancomycin. The covariate analysis identified that weight and drainage amount were strongly associated with the central volume and the drug clearance from CSF, respectively. Goodness-of-fit and model validation suggested that the proposed model was acceptable. A dosage regimen table was created for specific patient populations with different weights and drainage amounts to facilitate clinical application.

IMPLICATIONS

We identified 2 clinical markers associated with plasma and CSF concentrations. The proposed simulation may be useful to clinicians for norvancomycin dosing in this specific population with normal kidney function.

Population pharmacokinetics of peginterferon α2a in patients with chronic hepatitis B

There were significant differences in response and pharmacokinetic characteristics to the peginterferon α2a treatment among Chronic Hepatitis B (CHB) patients. The aim of this study is to identify factors which could significantly impact the peginterferon α2a pharmacokinetic characteristics in CHB patients. There were 208 blood samples collected from 178 patients who were considered as CHB and had been treated with peginterferon α2a followed by blood concentration measurement and other laboratory tests. The covariates such as demographic and clinical characteristics of the patients were retrieved from medical records. Nonlinear mixed-effects modeling method was used to develop the population pharmacokinetic model with NONMEM software. A population pharmacokinetic model for peginterferon α2a has been successfully developed which shows that distribution volume (V) was associated with body mass index (BMI), and drug clearance (CL) had a positive correlation with creatinine clearance (CCR). The final population pharmacokinetic model supports the use of BMI and CCR-adjusted dosing in hepatitis B virus patients.