Population pharmacokinetics of netilmicin in short-term prophylactic treatment

Individualising netilmicin dosing in neonates

PURPOSE

The aim of this study was develop an optimal dosing regimen for netilmicin in neonates.

METHODS

This was a population pharmacokinetic study in 97 neonates aged from 2 to 28 days after the due date who were being treated with netilmicin for suspected sepsis. The model was used to simulate dosing regimens.

RESULTS

The principle factors influencing netilmicin clearance (CL) were postmenstrual age (PMA) and current body weight (CWT), and the principal determinant of volume of distribution (V) was CWT. The final covariate model was CL = 0.192 x (CWT/2)(1.35) x (PMA/40)(1.03), V = 1.5 x (CWT/2)(0.3). The optimal dosing was 5 mg/kg ever 36 h, 5 mg/kg every 24 h, 6 mg/kg every 24 h and 7 mg/kg every 24 h for neonates < or =27, 28-30, 31-33 and > or =34 weeks PMA, respectively.

CONCLUSION

Individualisation of netilmicin dosing in neonates requires adjustment of dose by body weight, and dosing interval by both PMA and CWT.

Applications of population pharmacokinetics in current drug labelling

BACKGROUND AND OBJECTIVE

The application of population pharmacokinetics (PopPK) appears increasingly in drug labelling. The current study was to examine the use of PopPK in dose recommendation in drug-product labels.

METHOD

PopPK information was identified in the data sheets included in the physician desk reference (PDR). Electronic key word searches were conducted in the electronic library of PDR. The use of PopPK in the prescribing information, including the determination of dosing regimen, dosing in special populations and dose-adjustments was summarized and evaluated. The reliability and criteria for integrating the information derived from PopPK studies into the product labelling were discussed.

RESULTS AND DISCUSSION

Among more than 2500 items listed in the PDR, 88 listings were found to have PopPK information in the labelling. The information included general data (Gen) on pharmacokinetics (PK) and the effects of gender (sex), age, race, drug-drug interactions (DDI), smoking (Smk), alcohol consumption (Alc), disease state (Dis), renal impairment (Ren) and metabolic status (Met) on the PK parameters (Table). Whether there was an effect (+) or not (-) is also shown. Appendix 1 lists the products included in each category. Searches conducted at different times suggest an increase in both quantity and quality of PopPK data in drug development. PopPK is widely used in paediatric studies and the sample sizes in these studies are sometimes too small. The application of PopPK to protein drugs is increasing rapidly (Appendix 2). Several precautions should be exercised when PopPK is applied to protein drugs. When considering gender effects, different normalization methods for body weight have been used. The number of subjects included in the PopPK analysis should be given and the influence of the imbalance in any covariate should be investigated. PopPK-DDI results are particularly difficult to evaluate unless details about potentially influential factors such as dosing and sampling information for both drug and interacting drugs are given.

CONCLUSIONS

The use of PopPK to aid optimal dosing is increasing. Several noticeable problems raised usually avoid the acceptability of PopPK studies. More investigations are needed to inform the development of consensuses on these issues. There is an accelerating shift from PopPK to PopPK/PD. The limitations of such modelling should be recognized.

Bicompartmental kinetics of tobramycin analysed with a wide range of covariates

The pharmacokinetics of tobramycin was studied in adult patients (N = 151) admitted either for initial suspicion of Gram-negative infection or for prophylaxis. In addition to age, weight, height and creatinine clearance (CrCL), a range of other covariates were also analysed, including type of pathology, co-medication, fever, sex and ethnicity (Basque or not). All patients received 100mg tobramycin every 8 h and samples were collected at three time points after the first dose and at two time points after the fourth dose and assayed with a fluorescence polarisation immunoassay. The population mixed effects bicompartmental parameters were obtained from 725 concentration measurements using NONMEM, FOCE method, and were: systemic clearance, CL = 6.03 L/h (between-subject coefficient of variation (CV) %, 29.4%); volume of distribution, V = 15.04 L (7.3%); and intercompartmental constants, k(12) = 0.192 h(-1) (56%) and k(21) = 0.55 h(-1) (no CV% determined). Covariate modelling was performed within NONMEM. Two alternative significant covariate models (Models 1 and 2) are proposed, with functions of CrCL and/or sex (Model 2). However, for clinical purposes, differentiation by sex is insignificant. Model 1 is for CL = 3.1 + 0.05.CrCLL/h (17.3%); V = 14.6 L (12%); k(12) = 0.224 h(-1) (63%) and k(21) = 0.468 h(-1). Stochastic simulation was used to predict the expected concentration 95th percentiles after the recommended 7 mg/kg dose and for minimum inhibitory concentration (MIC) = 1 mg/L, as well as alternative once-daily dosing regimens for MIC = 2 mg/L. It is seen that once-daily high-dose tobramycin is an appropriate strategy with respect to pharmacodynamic indices, C(peak)/MIC or AUC/MIC (where C(peak) is the peak plasma concentration and AUC is the area under the concentration-time curve).

Sex and racial differences in pharmacological response: where is the evidence? Pharmacogenetics, pharmacokinetics, and pharmacodynamics

The Food and Drug Administration (FDA) reviewed 300 new drug applications between 1995 and 2000. Of the 163 that included a sex analysis, 11 drugs showed a >40% difference in pharmacokinetics between males and females, which was listed on the product label, yet no dosing recommendations were made based on sex. Female sex has been shown to be a risk factor for clinically relevant adverse drug reactions. Would simply dosing females based on their different pharmacokinetics decrease the incidence of adverse events? The answer is not known. Sex-dependent pharmacodynamic effects have been identified. The role of pharmacokinetics vs. pharmacodynamics is unclear, as is the impact of pharmacogenetics on both. This review highlights a few specific examples in each area in which sex differences in pharmacokinetics and pharmacodynamics are important and provides recommendations for additional needed research.

Pharmacokinetics of teicoplanin in an ICU population of children and infants

PURPOSE

Better dosing is needed for antibiotics, including teicoplanin (TEI), to prevent emergence of resistant bacterial strains. Here, we assess the TEI pharmacokinetics (PK) related to a 10 mg/l minimum inhibitory concentration (MIC) target in ICU children (4 to 120 months; n = 20) with gram+ infections.

METHODS

Standard administration of TEI was with three 10 mg/kg Q12h, loading infusions, and maintainance with 10 mg/kg or 15 mg/kg Q24h. During maintenance, 9 samples (3/day) were collected per patient and the PK analyzed with Nonlinear Mixed Effects Model (NONMEM).

RESULTS

Thirty-five percent of concentrations in older children (> or =2 months) vs. 8% in younger infants (<12 months) were below the target MIC. The global bicompartmental population PK parameters were [mean (interindividual CV%)] CL = 0.23 l/h [72%], V = 3.16 l [58%], k12 = 0.23 h(-1), and k21 = 0.04 h(-1). Two PK subpopulations were identified. The older children had CL = 0.29 [23%] l/h, V = 3.9 l and the younger infants, CL = 0.09 [37%] l/h, V = 1.05 l. Residual error was reduced from 52% to around 30% in the final models.

CONCLUSIONS

Older children in the ICU may require relatively higher doses of teicoplanin. However, a study in a larger population is needed.