[Inter-individual and intra-individual pharmacokinetic variability during teicoplanin therapy in geriatric patients]

Clinical practice guidelines for therapeutic drug monitoring of teicoplanin: a consensus review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring

Background

Owing to its low risk of adverse effects, teicoplanin has been extensively used in patients with infections caused by MRSA. To promote the better management of patients receiving teicoplanin, we have updated the guidelines for therapeutic drug monitoring (TDM).

Methods

The guidelines were developed by a committee following the methodology handbook published by the Japanese Medical Information Distribution Service. Nine clinical questions were selected. The committee conducted a systematic review and meta-analysis to establish evidence-based recommendations for the target trough concentration (C_min). An initial electronic database search returned 515 articles, and 97 articles qualified for a full review. Four and five studies were included for the efficacy evaluation of cut-off C_min values of 15 and 20 mg/L, respectively.

Results

Compared with C_min < 15 mg/L, a target C_min value of 15–30 mg/L resulted in increased clinical efficacy in patients with non-complicated MRSA infections (OR = 2.68; 95% CI = 1.14–6.32) without an increase in adverse effects. Although there was insufficient evidence, target C_min values of 20–40 mg/L were suggested in patients with complicated or serious MRSA infections. A 3 day loading regimen followed by maintenance treatment according to renal function was recommended to achieve the target trough concentrations. Because of the prolonged half-life of teicoplanin, measurement of the C_min value on Day 4 before reaching steady state was recommended.

Conclusions

The new guideline recommendations indicate the target C_min value for TDM and the dosage regimen to achieve this concentration and suggest practices for specific subpopulations.

To Estimate or to Forecast? Lessons From a Comparative Analysis of Four Bayesian Fitting Methods Based on Nonparametric Models

ABSTRACT

Using pharmacokinetic (PK) models and Bayesian methods in dosing software facilitates the analysis of individual PK data and precision dosing. Several Bayesian methods are available for computing Bayesian posterior distributions using nonparametric population models. The objective of this study was to compare the performance of the maximum a posteriori (MAP) model, multiple model (MM), interacting MM (IMM), and novel hybrid MM(HMM) in estimating past concentrations and predicting future concentrations during therapy. Amikacin and vancomycin PK data were analyzed in older hospitalized patients using 2 strategies. First, the entire data set of each patient was fitted using each of the 4 methods implemented in BestDose software. Then, the 4 methods were used in each therapeutic drug monitoring occasion to estimate the past concentrations available at this time and to predict the subsequent concentrations to be observed on the next occasion. The bias and precision of the model predictions were compared among the methods. A total of 406 amikacin concentrations from 96 patients and 718 vancomycin concentrations from 133 patients were available for analysis. Overall, significant differences were observed in the predictive performance of the 4 Bayesian methods. The IMM method showed the best fit to past concentration data of amikacin and vancomycin, whereas the MM method was the least precise. However, MM best predicted the future concentrations of amikacin. The MAP and HMM methods showed a similar predictive performance and seemed to be more appropriate for the prediction of future vancomycin concentrations than the other models were. The richness of the prior distribution may explain the discrepancies between the results of the 2 drugs. Although further research with other drugs and models is necessary to confirm our findings, these results challenge the widely accepted assumption in PK modeling that a better data fit indicates better forecasting of future observations.

Osteoarticular infections: a specific program for older patients?

BACKGROUND

With the increasing number of elderly patients, arthroplasties, fractures and diabetic foot infections, the worldwide number of osteoarticular infections (OAI) among the elderly is concomitantly expected to rise.

AIMS

We explore existing scientific knowledge about OAI in the frail elderly population.

METHODS

We performed a literature search linking OAIs to geriatric patients and comparing elderly patients (> 65 years) with average adults (range 18-65 years).

RESULTS

In this literature, financial aspects, comparison of diverse therapies on quality of life, reimbursement policies, or specific guidelines or nursing recommendations are missing. Age itself was not an independent factor related to particular pathogens, prevention of OAI, nursing care, and outcomes of OAI. However, geriatric patients were significantly more exposed to adverse events of therapy. They had more co-morbidities and more conservative surgery for OAI.

CONCLUSION

Available literature regarding OAI management among elderly patients is sparse. In recent evaluations, age itself does not seem an independent factor related to particular epidemiology, pathogens, prevention, nursing care, rehabilitation and therapeutic outcomes of OAI. Future clinical research will concern more conservative surgical indications, but certainly reduce inappropriate antibiotic use.

Nonparametric Methods in Population Pharmacokinetics

Population pharmacokinetic (PK) modeling is a widely used approach to analyze PK data obtained from groups of individuals, in both industry and academic research. The approach can also be used to analyze pharmacodynamic (PD) data and pooled PK/PD data. There are 2 main families of population PK methods: parametric and nonparametric. The objectives of this article are to present an overview of nonparametric methods used in population pharmacokinetic modeling and to explain their specific characteristics to inform scientists and clinicians about their potential value for data analysis, simulation, dosage design, and therapeutic drug monitoring (TDM). Nonparametric methods have several interesting characteristics for population PK analysis, including computation of exact likelihoods, the ability to accommodate parameter probability distributions of any shape (eg, non-Gaussian), and to detect subpopulations and outliers. Nonparametric population methods are also highly relevant for model-based TDM and design of individualized drug dosage regimens. Several algorithms have been developed to estimate model parameter values within an individual and compute that individual's dosage to achieve target drug exposure with maximum precision and accuracy. Nonparametric modeling methods for both population and individual PK analysis are available under user-friendly packages.

Is Trough Concentration of Vancomycin Predictive of the Area Under the Curve? A Clinical Study in Elderly Patients

BACKGROUND

Current guidelines suggest that vancomycin trough concentrations (Cmin) between 15 and 20 mg/L should be achieved to optimize vancomycin exposure and effect. The objective of this study was to analyze the correlation between vancomycin Cmin and the area under the concentration-time curve (AUC) and assess the ability to predict an AUC target of 400 mg·h/L based on Cmin.

METHODS

A retrospective analysis of vancomycin therapeutic drug monitoring data collected in 95 elderly patients treated with intermittent intravenous vancomycin was performed. For each patient, individual pharmacokinetic parameters of vancomycin and AUC24 were estimated from concentration measurements using a Bayesian approach. The relationship between vancomycin Cmin and AUC was studied using global and local correlation analysis as well as logistic regression with Receiver Operating Characteristic curve analysis.

RESULTS

The overall correlation between AUC24 and Cmin was significant but moderate (R = 0.51). When vancomycin Cmin was greater than 15 mg/L, the corresponding AUC24 was >400 mg·h/L in 95% of cases. However, AUC24 values >400 mg·h/L were obtained with Cmin < 15 mg/L in more than 30% of the cases. The logistic regression analysis identified a Cmin value of 10.8 mg/L as the optimal predictor of AUC24 > 400 mg·h/L.

CONCLUSIONS

The results of this study indicate that the recommended target range of 15-20 mg/L for vancomycin Cmin seems acceptable for controlling vancomycin exposure, although a value of approximately 11 mg/L appears to be optimal and may be safer.