Selection of optimal prophylactic aminoglycoside dosage in cancer patients: population pharmacokinetic approaches

Pharmacokinetic-Pharmacodynamic Crisis in the Elderly

Advances in medical technology have led to improved survival after catastrophic illnesses. Many of the survivors require ongoing care including tracheostomy, mechanical ventilation, tube feedings, and indwelling venous catheters. Repeated hospitalizations may be necessary to treat infectious complications resulting from resistant organisms requiring intravenous antibiotic therapy. Because prolonged intravenous access may be difficult or even impossible in these patients, alternative means of therapy are necessary. Linezolid is the first of a new class of antimicrobial agents known as the oxazolidinones with activity against gram-positive bacteria similar to that of vancomycin and yet its oral bioavailability allows for enteral administration. We present our retrospective experience with oral linezolid in a cohort of pediatric intensive care unit patients. Primary infectious disease issues included endocarditis, tracheitis, pneumonia, or central line sepsis resulting from Staphylococcus epidermidis, methicillin-resistant Staphylococcus aureus, and Enterococcus. Treatment was initiated with vancomycin and changed to enteral linezolid (10 mg/kg every 12 hours). The duration of therapy with linezolid varied from 7 days to 6 weeks. All of the patients were discharged home to complete their course of enteral linezolid. No complications related to linezolid therapy were noted, and all of the patients completed their prescribed course of therapy without the need for rehospitalization. Our preliminary experience suggests that oral linezolid offers an effective alternative to intravenous vancomycin for the treatment of infections resulting from gram-positive bacteria and avoids the need for prolonged vascular access.

Population pharmacokinetics of netilmicin in short-term prophylactic treatment

AIMS

To characterize the population pharmacokinetics of netilmicin, an aminoglycoside antibiotic, in adult urology patients and to develop a covariate model for improved dose titration.

METHODS

Data from 62 adult patients (55 male, seven female), undergoing urological surgery and treated with netilmicin for short-term prophylaxis, were evaluated retrospectively. The group had (median, range) ages 68, 31-92 years, weights 72, 43-106 kg and heights 167, 148-182 cm. No patient showed renal impairment before netilmicin treatment (serum creatinine </=1.9 mg dl-1). Netilmicin (100 mg) was administered as a maximum of four successive intravenous infusions of 30 min, at 8-h intervals. A total of five blood samples were collected from each patient. Prior to analysis, the dataset was divided into 'index' (n = 44) and 'validation' (n = 18) groups at random. The time courses of netilmicin concentrations from all subjects were analysed using a mixed effects, population, nonlinear modelling package (WinNonMix). For covariate model development, a stepwise procedure was used with backward elimination followed by forward inclusion based on age, sex, weight, height, creatinine clearance and type of surgery. The final covariate model parameters from the index group were used to simulate concentrations in the validation group and the bias and precision were compared with the observations.

RESULTS

A bi-compartmental open model with a proportional residual error best described the data. The population parameters for central and peripheral volumes of distribution were (typical population value [interindividual CV%]) Vc = 14.5 l [56%] and Vp = 10.2 l [not estimated], and the systemic and intercompartmental clearances were CL = 3.9 l h-1[42%] and CLQ = 10.1 l h-1[not estimated], respectively. The final population covariate relationships were based on sex (SEX) and creatinine clearance (CrCL): (Vc, l) = 18.9 - 5.9 x SEX [29%] and (CL, l h-1) = 0.06 x CrCL [33%]. Compared with the observations in the validation group, this model showed a bias (95% confidence interval) of -0.028 (-0.28, 0.25) and precision of 1.22 (0.78, 1.34).

CONCLUSION

Bi-compartmental pharmacokinetic parameters of netilmicin have been estimated from clinical data in urological surgery patients using a population approach. A given single dose results in large variability in plasma concentrations and thus the population covariate final model can be used for direct estimation of initial dosing in patients.

Relationship between pharmacokinetic parameters of gentamicin and patient characteristics and/or clinical data in patients with solid organ tumours

Gentamicin monitoring and the selection of the initial dosage are generally based on the relationship between pharmacokinetic parameters of gentamicin (GPP) and patient characteristics and/or clinical data (PC). However, the number of studies about this relationship in cancer patients is limited. Therefore, the main objective of the present study was to evaluate the relationship between GPP and PC in cancer patients and to identify different subgroups within this group of patients with unique relationship models between GPP and PC. A total of 198 cancer patients were included in the study. Firstly, GPP were estimated by the Sawchuk and Zaske regression method. Then, a linear regression analysis was performed to investigate the relationship between GPP and PC, and lastly subgroups with unique models were identified by comparing their regression models. The results revealed that the variable which was the best predictor of the distribution volume of gentamicine was the dosing weight (DW = IBW + (ABW-IBW), ABW being the actual body weight and IBW the ideal body weight). Creatinine clearance (CLCR) measured by a 24-hour urine collection (CLCRu) was the best predictor of gentamicin clearance (CL). When this value is not available, the CLCR estimated by the formula of Crockcroft and Gault (C-G), can be used. When the C-G formula was used, unique models to predict CL from CLCR were identified for patients who were obese, patients who had received high-dose chemotherapy and, for subjects who had never developed aplasia following chemotherapy. Whichever the model used, the results showed that some variability in pharmacokinetic parameters of gentamicin was not explained by the models, especially in some groups of patients.

Population pharmacokinetics of gentamicin in patients with cancer

AIMS

The purpose of this study was to describe the population pharmacokinetics of gentamicin in patients with cancer, to identify possible relationships between clinical covariates and population pharmacokinetic parameter estimates and to examine the relevance of existing dosage nomograms in light of the population model developed in these patients.

METHODS

Data were collected prospectively from 210 patients with cancer and were analysed with package NONMEM. Data were split into two sets: a population data set and an evaluation set. Creatinine clearance was estimated using measured creatinine concentrations and using 'low' creatinines set to a minimum of 60 micromol l(-1), 70 micromol l(-1) or 88.4 micromol l(-1)

RESULTS

A two compartment model was fitted to the concentration-time curve. Two best models were obtained, one that related clearance to estimated creatinine clearance (minimum creatinine value 60 micromol l(-1)) and the other that related clearance to age, creatinine concentration and body surface area. Volume of the central compartment was influenced by body surface area and albumin concentration. For both models 90% of measured concentrations lay within the 95% confidence interval of the simulated concentrations and the mean prediction errors were -7.2% and -6.6%, respectively. A final analysis performed in all patients identified the following relationship CL (1 h(-1))=0.88 x (1 + 0.043 x creatinine clearance) and central volume of distribution V1 (1)=8.59 x body surface area x (albumin/34)(-0.39). The mean population estimate of intercompartmental clearance (Q) was 1.301 h(-1) and peripheral volume of distribution (V2) was 9.801. Coefficient of variation was 18.5% on clearance and 28.2% on Q. Residual error expressed as a standard deviation was 0.36 mg l(-1) at 1.0 mg l(-1) and 1.32 mg l(-1) at 8.0 mg l(-1). The mean population estimate of clearance was 4.21 h(-1) and volume of distribution (Vss) was 24.61 (0.381 kg(-1)). The mean population estimates of half-lives were 1.8 h and 8.0 h.

CONCLUSIONS

In the context of published nomograms this analysis indicated that both the traditional approach and the new, 'once daily' approach should achieve satisfactory concentrations in cancer patients although serum concentration monitoring is required to confirm optimal dosing in individual patients.

Population pharmacokinetic parameters of gentamicin in patients with solid tumors: estimation by one- and two-stage methods

Gentamicin monitoring has been improved with the introduction of Bayesian methods but the usefulness depends on the quality of the population parameters (PP) used. The objective of this study was to determine PP of gentamicin in patients with solid tumors. A total of 198 adult patients with cancer were included in the analysis. Population parameters were estimated by both a two-stage and a one-stage method (NPEM, Non Parametric Expectation Maximization). Individual parameters (IP) were estimated by the Sawchuk-Zaske method and by nonlinear regression. The estimated distribution volume and clearance of gentamicin (mean +/- SD) were 18.37 +/- 5.021 (0.30-0.32 l/kg of dosing weight) and 3.34 +/- 1.6 l/h, respectively. No significant differences between IP or PP obtained by the different methods were found. The results indicated a wide variability of gentamicin pharmacokinetics making monitoring necessary and that patients with solid tumors may have larger gentamicin volume and slower clearance than normal patients. These observations imply that different population pharmacokinetic parameters should be used for this group of patients.