Individualising gentamicin dosage regimens. A comparative review of selected models, data fitting methods and monitoring strategies

Gentamicin Administration in Dialysis Patients: Before or After Hemodialysis?

BACKGROUND

Gentamicin is used to treat severe infections and has a small therapeutic window. This study aimed to optimize the dosing strategy of gentamicin in intermittently hemodialyzed patients by simulating concentration-time profiles during pre- and postdialysis dosing, based on a published pharmacokinetic model.

METHODS

Pharmacokinetic simulations were performed with virtual patients, including septic patients, who were treated with gentamicin and received weekly hemodialysis with an interval of 48 h-48 h-72 h. The following dosing regimens were simulated: for nonseptic patients, 5 mg/kg gentamicin was given 1 h or 2 h before dialysis or a starting dose of 2.5 mg/kg and a maintenance dose of 1.5 mg/kg immediately after dialysis were given; for septic patients, 6 mg/kg gentamicin was given 1 h or 2 h before dialysis or a starting dose of 3 mg/kg and a maintenance dose of 1.8 mg/kg immediately were given after dialysis. The mean maximum concentration (C max ), area under the curve (AUC) 24 h , and target attainment (TA) of pharmacodynamic targets were calculated and compared. The following targets were adopted from the literature: C max >8 mg/L and <20 mg/L and AUC 24 h >70 mg·h/L and <120 mg·h/L.

RESULTS

In nonseptic patients, postdialysis dosing resulted in a TA of 35% for C max of >8 mg/L, 100% for <20 mg/L and AUC 24 h >70 mg·h/L, and 45% for <120 mg·h/L. Dosing 2 h before dialysis resulted in a TA of 100% for C max of >8 mg/L, 40% for <20 mg/L, 65% for AUC 24 h >70 mg·h/L, and 77% for <120 mg·h/L. Simulations of septic patients resulted in comparable outcomes with higher TAs for C max <20 mg/L (96%), AUC 24 h >70 mg·h/L (90%), and AUC 24 h <120 mg·h/L (53%) for dosing 1 h before dialysis.

CONCLUSIONS

Postdialysis dosing resulted in a low TA of C max >8 mg/L; however, predialysis dosing ensured a high TA of C max >8 mg/L and acceptable TA of C max <20 mg/L, AUC 24 h >70 mg·h/L, and AUC 24 h <120 mg·h/L, which could increase the efficacy of gentamicin. Therefore, clinicians should consider predialysis dosing of gentamicin in patients undergoing intermittent hemodialysis.

Evaluation of Limited Sampling Strategies for Bayesian Estimation of Daptomycin Area Under the Concentration-Time Curve: A Short Communication

PURPOSE

Increasing evidence supports daptomycin therapeutic drug monitoring. The author's reference center used to perform therapeutic drug monitoring in patients who receive high-dose daptomycin for bone and joint infections, with a three-sample strategy to estimate the daptomycin daily area under the concentration-time curve (AUC). The objective of this study was to evaluate simpler strategies based on only 2 or 1 sample(s).

METHODS

The authors used the BestDose software to estimate the daptomycin AUC after Bayesian posterior estimation of individual pharmacokinetic (PK) parameters at steady state. The reference AUC (AUCfull) was based on 3 samples obtained predose (T0) and approximately 1 hour (T1) and 6 hours (T6) after the start of a 30-minute infusion of IV daptomycin. It was compared with the AUC based on all possible 2-sample and 1-sample strategies. Bias, imprecision, regression, and Bland-Altman plots were used to assess the performance of the alternative strategies.

RESULTS

Data from 77 patients were analyzed. The mean AUCfull value was 936 ± 373 mg·h/L. The best 2-sample strategy was T0 + T6, with a mean prediction bias of 0.13 mg·h/L and absolute imprecision of 3%. The T0 + T1 strategy also performed well with a mean bias of -10 mg·h/L and imprecision of 3%. The best 1-sample strategy was the T6 sample only with a bias of 2.19 mg·h/L and imprecision of 6%.

CONCLUSIONS

Bayesian estimation of daptomycin AUC based on a two-sample strategy was associated with negligible bias and imprecision compared with the author's usual three-sample strategy. The trough and peak strategy may shorten and simplify patient visits and reduce assay labor and costs.

Personalized tobramycin dosing in children with cystic fibrosis: a comparative clinical evaluation of log-linear and Bayesian methods

BACKGROUND

Children with cystic fibrosis (CF) pulmonary exacerbations receive IV tobramycin therapy, with dosing guided by either log-linear regression (LLR) or Bayesian forecasting (BF).

OBJECTIVES

To compare clinical and performance outcomes for LLR and BF.

PATIENTS AND METHODS

A quasi-experimental intervention study was conducted at a tertiary children's hospital. Electronic medical records were extracted (from January 2015 to September 2021) to establish a database consisting of pre-intervention (LLR) and post-intervention (BF) patient admissions and relevant outcomes. All consecutive patients treated with IV tobramycin for CF pulmonary exacerbations guided by either LLR or BF were eligible.

RESULTS

A total of 376 hospital admissions (LLR = 248, BF = 128) for CF pulmonary exacerbations were included. Patient demographics were similar between cohorts. There were no significant differences found in overall hospital length of stay, rates of re-admission within 1 month of discharge or change in forced expiratory volume in the first second (Δ FEV1) at the end of tobramycin treatment. Patients treated with LLR on average had twice the number of therapeutic drug monitoring (TDM) blood samples collected during a single hospital admission. The timeframe for blood sampling was more flexible with BF, with TDM samples collected up to 16 h post-tobramycin dose compared with 10 h for LLR. The tobramycin AUC0-24 target of ≥100 mg/L·h was more frequently attained using BF (72%; 92/128) compared with LLR (50%; 124/248) (P < 0.001). Incidence of acute kidney injury was rare in both groups.

CONCLUSIONS

LLR and BF result in comparable clinical outcomes. However, BF can significantly reduce the number of blood collections required during each admission, improve dosing accuracy, and provide more reliable target concentration attainment in CF children.

Does Circadian Rhythm Affect the Pharmacokinetics of Once-Daily Tobramycin in Adults With Cystic Fibrosis?

BACKGROUND

In the era of multiple daily dosing of systemic aminoglycosides, a circadian rhythm in the clearance of these vital antibiotics has been demonstrated in animals and healthy volunteers. Over the past decade, once-daily dosing regimens have been proved to be less nephrotoxic and were therefore adopted worldwide for most indications requiring treatment with an aminoglycoside. In this study, the effect of the time of administration on the pharmacokinetics of once-daily tobramycin in adults with cystic fibrosis (CF) experiencing a pulmonary exacerbation was investigated.

METHODS

In this open randomized study, patients with CF received intravenous tobramycin at 8:00 or 22:00 hours. Pharmacokinetic and kidney function parameters were compared between the 2 groups.

RESULTS

Twenty-five patients were included. The mean weight-corrected clearances of tobramycin were 1.46 versus 1.43 mL/h*kg (P = 0.50) and mean volumes of distribution were 0.25 versus 0.27 L/kg (P = 0.54) for the 8:00 and 22:00 groups, respectively. In addition, no significant differences were detected in changes in estimated clearances of creatinine or tobramycin on day 1 and day 8 in the 8:00 or 22:00 group, indicating that there was no decline in clearance over time. At day 8 of therapy, the increase in serum blood urea nitrogen in the 22:00 group was significantly higher than that in the 8:00 group (1.8 versus 0.2 mmol/L, P = 0.015).

CONCLUSIONS

The time of administration (8:00 versus 22:00) did not affect tobramycin pharmacokinetics in the adult CF population studied. The increase in serum blood urea nitrogen in the 22:00 group requires further investigation.

Therapeutic drug monitoring of antimicrobial drugs in neonates. An opinion paper

PURPOSE

Neonatal infections are associated with high morbidity and mortality rates. Optimal treatment of these infections requires knowledge of neonatal pharmacology and integration of neonatal developmental pharmacokinetics of antimicrobial drugs in the design of dosing regimens for use with different gestational and postnatal ages. Population pharmacokinetic (PK) and pharmacodynamic (PD) models are used to personalize the use of these drugs in these fragile patients. The final step to further minimize variability in an individual patient is therapeutic drug monitoring (TDM), where the same population PK/PD models are used in concert with optimally drawn blood samples to further fine-tune therapy. The purpose of this manuscript is to describe the present status and future role of model-based precision dosing and TDM of antimicrobial drugs in neonates.

METHODS

PubMed was searched for clinical trials or clinical studies of TDM in neonates.

RESULTS

A total of 447 papers were retrieved, of which 19 were concerned with antimicrobial drugs. Two papers (one aminoglycoside and one vancomycin) addressed the effects of TDM in neonates. We found that, in addition to aminoglycosides and vancomycin, TDM also plays a role in beta-lactam antibiotics and antifungal drugs.

CONCLUSION

There is a growing awareness that, in addition to aminoglycosides and vancomycin, the use of beta-lactam antibiotics, such as amoxicillin and meropenem, and other classes of antimicrobial drugs, such as antifungal drugs, may benefit from TDM. However, the added value must be shown. New analytical techniques and software development may greatly support these novel developments.

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.

Concomitant Drug Treatment and Elimination in the RCC-affected Kidneys: Can We Kill Two Birds with One Stone?

BACKGROUND

The kidneys are vital organs acting as the body's filters that eliminate drugs and other waste products from the body. For effective cancer therapy, a delicate balance is required in the drug treatment and its elimination, which is critical for drug accumulation, toxicity, and kidney malfunction. However, how renal cell carcinoma (RCC) affects the kidneys in safely eliminating the byproducts of drug treatments in patients with severely dysregulated kidney functions had remained elusive. Recent advancements in dose adjustment have added to our understanding regarding how drug treatments could be effectively regulated in aberrant kidney cells, driving safe elimination and reducing drug accumulation and toxicity at the right time and space. Dose adjustment is the only standard systemic way applicable; however, it presents certain limitations. There is significant room for developing new strategies and alternatives to improve it.

OBJECTIVES

Our analysis of the available treatments in literature discusses the treatment and their safe eliminations. In this study, we give an overview of the measures that could be taken to maintain the elimination gradient of anti-cancer drugs and restore normal kidney function in RCC. Differential therapeutics of RCC/mRCC in various clinical phase trials and the interaction of targeted therapeutics in response to vascular endothelial growth factor (VEGF) were also discussed.

CONCLUSION

Such information might suggest a new direction in controlling treatment with safe elimination through dose adjustment and its associated alternatives in a judicious manner. A strategy to systematically focus on the safe elimination of anti-cancer drugs in RCC strongly needs advocating.

Predictive performance of gentamicin dosing nomograms

Background

Several nomograms have been proposed to facilitate the determination of initial gentamicin dosing regimens in clinical settings. This study aimed to assess the predictive performance of these nomograms in Korean patients.

Methods

Gentamicin concentrations were determined in 84 patients with infective endocarditis (IE) and in 95 patients with other infections. All patients underwent therapeutic drug monitoring in Seoul National University Hospital from 2006 to 2012. Individual pharmacokinetic parameters were estimated using a Bayesian method, which predicted steady state peak and trough serum concentrations. Six nomograms were evaluated in patients with “other” infections: the Thomson guidelines, Hull-Sarubbi table, and Rule of Eights, for multiple daily dosing; and the Hartford nomogram, Barnes-Jewish Hospital nomogram, and Sanford Guide, for extended-interval dosing. In IE patients, synergistic combination dosing nomograms, based on the American Heart Association dosing interval guidelines, were evaluated.

Results

Gentamicin dosing nomograms performed poorly in attaining the target peak serum concentrations. Multiple-daily dosing nomograms predicted peak serum gentamicin concentrations better than did the extended-interval dosing nomograms (31.9%–72.3% vs 4.3%–45.7%, respectively). Similarly, in patients with IE, the once-daily dosing nomogram resulted in a significantly lower percentage of patients achieving target peak gentamicin concentrations than that associated with the thrice-daily dosing nomogram (P=0.0015). All of the multiple-daily dosing, extended-interval dosing, and synergistic combination dosing nomograms predicted the nontoxic target trough concentrations in >80% of patients.

Conclusion

Gentamicin dosing nomograms performed poorly in achieving the target peak serum concentrations. New gentamicin nomograms may be required in patients with IE, particularly for once-daily dosing. Therapeutic drug monitoring is highly recommended for gentamicin to ensure that the target concentrations are achieved.

Bayesian pharmacokinetic analysis of a gentamicin nomogram in neonates: a retrospective study

BACKGROUND

Although gentamicin is used extensively within the first week of life for suspected sepsis in neonates, little is known about the performance of gentamicin dosing nomograms in this population.

OBJECTIVE

The goal of our study was to retrospectively assess the performance of a gentamicin dosing nomogram in neonates given gentamicin during the first week after birth.

METHODS

In this retrospective study, gentamicin therapeutic drug monitoring data were collected during routine clinical care for all neonates who were born in St. Boniface General Hospital (Winnipeg, Manitoba, Canada) between January 1999 and April 2001 and given gentamicin during the first week after birth. We used Bayesian pharmacokinetic analysis to retrospectively assess the performance of our gentamicin dosing nomogram in neonates born at gestation ages <32 weeks, between 32 and 34 weeks, and >34 weeks. Bayesian pharmacokinetic values for parameters within groups were compared and used to explore predicted peak and trough serum gentamicin concentrations based on the institutional dosing nomogram.

RESULTS

In a total of 58 neonates, those neonates born at ≤34 weeks' gestation had a weight-normalized apparent volume of gentamicin distribution 1.6 times larger than infants born after 34 weeks' gestation (P<0.001), as identified by Bayesian analysis. Weight-normalized gentamicin clearance was 22% lower in the youngest age category (P<0.01). Only 33% of predicted peak serum gentamicin concentrations were >6 mg/L for neonates born at ≤34 weeks' gestation, whereas 90% were therapeutic in neonates born at >34 weeks' gestation (P<0.001). With the present nomogram, the likelihood of an indication for adjustment of the dosing regimen was 12.4-fold higher (95% CI, 3.5-43.7) for those neonates born at ≤34 weeks' gestation.

CONCLUSIONS

These results have important clinical implications with regard to the advisability of determining peak serum gentamicin concentrations in neonates born at ≤34 weeks' gestation. Sampling of peak serum concentrations is indicated in this population to avoid underdosing and potential loss of therapeutic efficacy.

Comparison of four renal function estimation equations for pharmacokinetic modeling of gentamicin in geriatric patients

Most aminoglycoside pharmacokinetic models include an index of renal function, such as creatinine clearance, to describe drug clearance. However, the best clinical descriptor of renal function for the pharmacokinetic modeling of aminoglycosides has not been established. This analysis was based on 412 gentamicin concentrations from 92 geriatric patients who received intravenous gentamicin for various infectious diseases. Four two-compartment population models were fitted to gentamicin concentrations in a learning set of 64 patients using the nonparametric adaptive grid (NPAG) algorithm. Each model included an index of renal function, namely, the Cockcroft-Gault (CG), Jelliffe (JEL), modification of diet in renal disease (MDRD), or modified MDRD (MDRDm; adjusted to individual body surface area) equation as a covariate influencing gentamicin serum clearance. Goodness of fit and predictive performance of the four models were compared using standard criteria in both the learning set and in a validation set of 28 patients. A final analysis was performed to estimate the population pharmacokinetic parameter values of the entire 92-patient group. In the learning set, the CG-based model best fit the data, followed by JEL-, MDRD-, and MDRDm-based models, with relative reductions of the Akaike information criterion of 29.4, 20.2, 14.2, and 4.2, respectively. Bias and precision of population predictions were significantly different among the four models. In the validation set, individual predictions from the four models showed marginally different biases. The final estimation confirmed the previous results. Specifically, the CG-based model showed predictive performance that was comparable to or better than that of the MDRD-based model at each stage of the analysis. This study shows that methods used to estimate renal function should not be considered interchangeable for the model-based estimation of gentamicin concentrations.

Therapeutic drug monitoring of aminoglycosides in neonates

The efficacy and toxicity of aminoglycosides show a strong direct positive relationship with blood drug concentrations, therefore, therapy with aminoglycosides in adults is usually guided by therapeutic drug monitoring. Dosing regimens in adults have evolved from multiple daily dosing to extended-interval dosing. This evolution has also taken place in neonates. Neonates, however, display large interindividual differences in the pharmacokinetics of aminoglycosides due to developmental differences early in life. The volume of distribution of aminoglycosides shows a strong relationship with bodyweight, which tends to be larger (corrected for bodyweight) in more premature infants and those with sepsis. Renal clearance of aminoglycosides increases with gestational age and accelerates immediately after birth. Because of these developmental influences, there is great inter- and intraindividual variability in the volume of distribution and clearance of these drugs, and investigators have established aminoglycoside dosing regimens based on bodyweight and/or gestational age. Widely practised dosing regimens comprise 4-5 mg/kg bodyweight of gentamicin every 24-48 hours as a first dose, followed by dose adjustment based on therapeutic drug monitoring. Although formal toxicity studies are scarce, there is no evidence that aminoglycoside toxicity in neonates differs from that in adults. Monitoring of blood drug concentrations and intelligent reconstruction of individual pharmacokinetic behaviour using a population pharmacokinetic model, optimally chosen blood sampling times and appropriate pharmacokinetic software, help clinicians to quickly optimize aminoglycoside dosing regimens to maximize the clinical effect and minimize the toxicity of these drugs.

The influence of weight with assay error on gentamicin pharmacokinetics using the Bayesian and nonlinear least square regression analysis in appendicitis patients

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appendicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected 48 h after the first dose from all patients at the following times, just before the regularly scheduled infusion, at 0.5 h and 2 h after the end of the 0.5 h infusion. Serum gentamicin levels were analysed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12 and 16 microg/ml in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (microg/ml) = 0.0246-(0.0495C) + (0.00203C(2)). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and the better, safer care of patients receiving gentamicin.

The influence of assay error weight on gentamicin pharmacokinetics using the bayesian and nonlinear least square regression analysis in appendicitis patients

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appendicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected at 48 h after the first dose from all patients at the following times, just before regularly scheduled infusion, at 0.5 h and 2 h after the end of 0.5 h infusion. Serum gentamicin levels were analyzed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12, and 16 microg/mL in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (microg/mL) = 0.0246 - (0.0495C) + (0.00203C2). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and better, safer care of patients receiving gentamicin.

Development of an optimal sampling strategy for clinical pharmacokinetic studies of the novel anthracycline disaccharide analogue MEN-10755

AIM

MEN-10755 is a novel anthracycline analogue that has shown an improved therapeutic efficacy over doxorubicin in animal models, especially in gynaecological and lung cancers and is currently under clinical development for the treatment of solid tumours. The aim of the project was to develop an optimal sampling strategy for MEN-10755 to provide an efficient basis for future pharmacokinetic/pharmacodynamic investigations.

METHODS

Data from 24 patients who participated in a phase I clinical pharmacokinetic study of MEN-10755 administered as a short i.v. infusion were included. Individual pharmacokinetic values were calculated by fitting the plasma concentration data to a two-compartment model using nonlinear least-squared regression (KINFIT, Ed 3.5). Population pharmacokinetic analysis was carried out using (a) the traditional standard two-stage method (STS) based on all data (KINFIT-ALL), (b) the iterative two-stage Bayesian (IT(2)B) population modelling algorithm (KINPOP), and (c) the STS method using KINFIT and using four optimally timed plasma concentrations (KINFIT-OSS4). Determinant (D) optimal sampling strategy (OSS) was used to evaluate the four most information-rich sampling times. The pharmacokinetic parameters V(c) (l), k(el) (h(-1)), k(12) (h(-1)) and k(21) (h(-1)) calculated using KINPOP served as a model for calculation of four D-optimal sampling times. D-optimal sampling data sets were analysed using KINFIT-OSS4 and compared with the population model obtained by the traditional standard two-stage approach for all data sets (KINFIT-ALL).

RESULTS

The optimal sampling times were: the end of the infusion, and 1.5 h, 3.8 h and 24 h after the start of the infusion. The four-point D-optimal sampling design determined in this study gave individual parameter estimates close to the basic standard estimates using the full data set.

CONCLUSION

Because accurate estimates of pharmacokinetic parameters were achieved, the four-point D-optimal sampling design may be very useful in future studies with MEN-10755.

Bayesian Pharmacokinetics of Gentamicin in a Haemodialysis Population

BACKGROUND

Aminoglycosides are commonly used in the haemodialysis population. Standard pharmacokinetic approaches require multiple sampling to describe the parameters of drug distribution and elimination in the intra- and interdialytic periods.

OBJECTIVE

To characterise the pharmacokinetics of gentamicin in a haemodialysis population by using Bayesian pharmacokinetic methods and only two plasma concentrations.

DESIGN AND PARTICIPANTS

Prospective case series of 13 adult (aged 36-70 years) haemodialysis patients (Fresenius F80 dialysers were used) receiving gentamicin.

METHODS

Patients with suspected or confirmed Gram-negative infections were given gentamicin. At 48 hours after receiving the dose (at the next haemodialysis session), patients provided two blood samples, one immediately before the dialysis session and another 1 hour after haemodialysis. Data on dosage, timing and plasma concentrations for all subjects were analysed with PASTRX version 10.6 and Bayesian pharmacokinetic analysis. Volume of distribution (Vd), interdialytic elimination rate constant (k(inter)), interdialytic elimination half-life (t1/2beta, inter)) and interdialytic clearance (CL(inter)) were determined from a single predialysis plasma concentration. Elimination rate constant (k(dial)), elimination half-life (t1/2beta, dial)) and clearance (CL(dial)) during 3.5-4 hours of dialysis were also determined from the pre- and post-plasma concentrations.

RESULTS

Pharmacokinetic parameters (mean +/- SD) were: Vd 0.288 +/- 0.002 L/kg, k(inter) 0.015 +/- 0.004h(-1), t1/2beta, inter) 48 +/- 11h, CL(inter) 5.9 +/- 2.4 mL/min, k(dial) 0.25 +/- 0.05 h(-1), t1/2beta, dial) 3.0 +/- 1.0h and CL(dial) 91 +/- 24 mL/min.

CONCLUSIONS

The rate of elimination of gentamicin was 17-fold greater (95% CI 13.7-20.7) on haemodialysis with a Fresenius F80 than off haemodialysis. All of the pharmacokinetic parameters of interest were determined using Bayesian pharmacokinetic procedures and only two plasma gentamicin concentrations.

Population pharmacokinetics and relationship between demographic and clinical variables and pharmacokinetics of gentamicin in neonates

Population pharmacokinetic parameter estimates were calculated from 725 routine plasma gentamicin concentrations obtained in 177 neonates of 24 to 42 weeks' gestational age in their first week of life. Kel increases and V/W decreases with increasing gestational age. Almost identical results were obtained with iterative two-stage Bayesian fitting (MW\PHARM 3.30) as with a non-parametric maximization algorithm (NPEM2). The effect of various covariates on drug disposition was investigated retrospectively using multiple regression analysis. Predictive power for Kel increases with rising gestational age. For neonates </=28.5 weeks and neonates >28.5 weeks and </=30.9 weeks, the predictive power of the regression equation for Kel was relatively low (r2 respectively 0.270 and 0.364). Better predictivity was found for Kel at gestational ages >30.9 weeks (r2 = 0.482), with gestational age, postnatal age, and Apgar score at 5 minutes being predictors. A very strong correlation existed between volume of distribution and weight (r2 = 0.83). Volume as a function of weight could be described with low predictivity by gestational age and to a lesser degree by Apgar score at 5 minutes (r2 = 0.298). The developed models need appropriate prospective clinical validation.

Individualising aminoglycoside dosage regimens after therapeutic drug monitoring: simple or complex pharmacokinetic methods?

Measurements of aminoglycoside concentration in serum are used to individualise dosage regimens (dose per administration and/or administration interval) with the goal of attaining the desired therapeutic range as quickly as possible. Therapeutic range is defined in terms of peak concentration (to monitor effectiveness) and trough concentration (to avoid toxicity). This article focuses on methods to individualise aminoglycoside dosage regimens in the context of extended dosage intervals. Simple pharmacokinetic methods involve linear dosage adjustment based on peak or trough concentrations or area under the concentration-time curve, or nomograms. The once daily aminoglycoside nomogram determines the dosage interval for aminoglycosides given as a fixed dose per administration, based on a single concentration measurement drawn 6 to 14 hours after the start of the first infusion. This is a preferred method because of its simplicity, strong pharmacodynamic rationale and prospective validation in a large population. However, it does not work when the fixed dose assumed is not relevant, for example for patients with burns, cystic fibrosis, ascites or pregnancy. Furthermore, it has not been validated in children. In these cases, a more sophisticated method is required. Complex pharmacokinetic methods require dedicated software. Non-Bayesian least-squares methods allow the optimisation of both the dose and the dosage interval, but require aminoglycoside concentrations from two or more samples taken in the post-distributive phase during a single dosage interval. With Bayesian least-squares methods, only one concentration measurement is required, although any number of samples can be taken into account. In the Bayesian maximum a posteriori (MAP) method, the parameter estimates are taken as the values corresponding to the maximum of the posterior density. In 'full' Bayesian approaches (also called stochastic control), all the information about the parameters revealed by the posterior distribution is taken into account, and the optimal regimen is found by minimising the expected value of the weighted sum of squared deviations between predicted and target concentrations. If the population model is reasonably well known, Bayesian methods (MAP or stochastic control) should be used because of their good predictive performance. Although only one concentration measurement is required, better precision is afforded by a two-sample strategy, preferably drawn 1 and 6 hours after the start of the first infusion. If the population model is not known, then the non-Bayesian least-squares method is the method of choice, because of its robustness and lack of requirement for prior information about the distribution of parameters in the population.

Pharmacokinetic aspects of treating infections in the intensive care unit: focus on drug interactions

Pharmacokinetic interactions involving anti-infective drugs may be important in the intensive care unit (ICU). Although some interactions involve absorption or distribution, the most clinically relevant interactions during anti-infective treatment involve the elimination phase. Cytochrome P450 (CYP) 1A2, 2C9, 2C19, 2D6 and 3A4 are the major isoforms responsible for oxidative metabolism of drugs. Macrolides (especially troleandomycin and erythromycin versus CYP3A4), fluoroquinolones (especially enoxacin, ciprofloxacin and norfloxacin versus CYP1A2) and azole antifungals (especially fluconazole versus CYP2C9 and CYP2C19, and ketoconazole and itraconazole versus CYP3A4) are all inhibitors of CYP-mediated metabolism and may therefore be responsible for toxicity of other coadministered drugs by decreasing their clearance. On the other hand, rifampicin is a nonspecific inducer of CYP-mediated metabolism (especially of CYP2C9, CYP2C19 and CYP3A4) and may therefore cause therapeutic failure of other coadministered drugs by increasing their clearance. Drugs frequently used in the ICU that are at risk of clinically relevant pharrmacokinetic interactions with anti-infective agents include some benzodiazepines (especially midazolam and triazolam), immunosuppressive agents (cyclosporin, tacrolimus), antiasthmatic agents (theophylline), opioid analgesics (alfentanil), anticonvulsants (phenytoin, carbamazepine), calcium antagonists (verapamil, nifedipine, felodipine) and anticoagulants (warfarin). Some lipophilic anti-infective agents inhibit (clarithromycin, itraconazole) or induce (rifampicin) the transmembrane transporter P-glycoprotein, which promotes excretion from renal tubular and intestinal cells. This results in a decrease or increase, respectively, in the clearance of P-glycoprotein substrates at the renal level and an increase or decrease, respectively, of their oral bioavailability at the intestinal level. Hydrophilic anti-infective agents are often eliminated unchanged by renal glomerular filtration and tubular secretion, and are therefore involved in competition for excretion. Beta-lactams are known to compete with other drugs for renal tubular secretion mediated by the organic anion transport system, but this is frequently not of major concern, given their wide therapeutic index. However, there is a risk of nephrotoxicity and neurotoxicity with some cephalosporins and carbapenems. Therapeutic failure with these hydrophilic compounds may be due to haemodynamically active coadministered drugs, such as dopamine, dobutamine and furosemide, which increase their renal clearance by means of enhanced cardiac output and/or renal blood flow. Therefore, coadministration of some drugs should be avoided, or at least careful therapeutic drug monitoring should be performed when available. Monitoring may be especially helpful when there is some coexisting pathophysiological condition affecting drug disposition, for example malabsorption or marked instability of the systemic circulation or of renal or hepatic function.

Mixed effects modeling of the disposition of gentamicin across domestic animal species

An interspecies pharmacokinetic model for gentamicin was developed using the mixed effects modeling approach and serum disposition data obtained from the Food Animal Residue Avoidance Databank (FARAD). Data that met a priori quality criteria was obtained from the database and analysed using the traditional double logarithmic analysis and the mixed effects modeling approach. Body weight, brain weight and fever were the covariates of interest in our study. Population pharmacokinetic models across species were developed and validated with swine data. The parameter volume of distribution was modeled as a function of body weight. The total clearance was initially modeled as a function of body weight. The predictability performance of the model improved dramatically when the parameter brain weight was included in the covariate model for clearance. This was a surprising finding worthy of further study. The covariate fever seemed to influence the magnitude of the volume of distribution, although the scarcity of data pertaining to diseased animals makes this finding uncertain. We conclude that the pharmacokinetic characteristics of drugs such as gentamicin, can be predicted across species using a population pharmacokinetics modeling approach, and that clinical features that affect species in a similar manner can be also explored in this fashion.

Estimation of glomerular filtration rate in cancer patients

The frequent need to obtain an estimate of renal function in cancer patients, not least for targeting carboplatin dose, has led to a number of approaches to estimate glomerular filtration rate (GFR). This study aimed to develop a simple and reliable method to estimate GFR using readily-available patient characteristics. Data from 62 patients with estimates of 51Cr-EDTA clearance were analysed to determine the most appropriate formula relating this method of measuring GFR to patient characteristics. The population pharmacokinetics of 51Cr-EDTA were analysed using NONMEM to evaluate the influence of each covariate. The formulae derived were then validated using a further 38 patients and compared with those obtained using existing formulae. 51Cr-EDTA clearance (GFR) was positively related to Dubois surface area, negatively related to age, and inversely related to serum creatinine (SCr). Females had lower 51Cr-EDTA clearance than males. The enzymatic method of SCr assay gave more reliable results than the Jaffe colorimetric method. A measure of creatine kinase significantly improved the estimation of GFR. The new formula produced estimates of GFR which were less biased (Mean Prediction Error = -3%) and more precise (Mean Absolute Prediction Error = 12%) than Cockcroft and Gault (-8% and 16%) or Jelliffe (-15% and 19%) estimates. The formulae developed here can be used to provide reliable estimates of GFR, particularly in regard to targeted dosing of carboplatin.

Gentamicin for the practicing urologist: review of efficacy, single daily dosing and "switch" therapy

PURPOSE

We review the literature on gentamicin, including single daily dosing and "switch" therapy.

MATERIALS AND METHODS

We used MEDLINE to search the literature from 1966 to June 1997, and then manually searched bibliographies to identify studies that our initial search might have missed.

RESULTS

Gentamicin has attractive characteristics, including wide spectrum, infrequent resistance, economy and familiarity. Although limited by well known toxicities, gentamicin remains a drug of choice for serious Gram-negative infections. Dosing strategies, such as single daily dosing and switch therapy, have renewed enthusiasm for this time-honored drug.

CONCLUSIONS

Gentamicin remains a valuable drug in urology. Once daily dosing and switch therapy offer the potential to increase effectiveness and convenience while decreasing toxicity and costs.

Population pharmacokinetics in veterinary medicine: potential use for therapeutic drug monitoring and prediction of tissue residues

Population pharmacokinetics can be defined as a study of the basic features of drug disposition in a population, accounting for the influence of diverse pathophysiological factors on pharmacokinetics, and explicitly estimating the magnitude of the interindividual and intraindividual variability. It is used to identify subpopulations of individuals that may present with differences in drug kinetics or in kinetic/dynamic responses. Rooted in procedures used in engineering systems, population pharmacokinetics methods were conceived as a means to determine the pharmacokinetic profile in populations in which a sparse number of samples were obtained per individual, such as those in late stage human clinical trials. This is the situation commonly encountered in all aspects of veterinary medicine. The exploratory nature of this technique allows one to probe relationships between clinical factors (such as age, gender, renal function, etc.) and drug disposition and/or effect. Similarly, the utilization of these techniques in the clinical research phases of drug development optimize the determination of efficacy and safety of drugs. Given the observational nature of most studies published so far, statistical methods to validate the population models are necessary. Simulation studies may be conducted to explore data collection designs that maximize information yield with a minimum expenditure of resources. The breadth of this approach has allowed population studies to be more commonly employed in all areas of drug therapy and clinical research. Finally, in veterinary medicine, there is an additional field in which population studies are potentially ideally suited: the application of this methodology to the study of tissue drug depletion and drug residues in production animals, and the establishment of withdrawal times tailored to the clinical or production conditions of populations or individuals. Such application would provide a major step toward assuring a safe food supply under a wide variety of dose and off-label clinical uses. Population pharmacokinetics is an ideal method for generating data in support of the implementation of flexible labelling policies and extralabel drug use recently approved under AMDUCA (Animal Medicinal Drug Use Clarification Act. 21 CFR Part 530).

A population pharmacokinetic model of cyclosporine in the early postoperative phase in patients with liver transplants, and its predictive performance with Bayesian fitting

The availability of personal computer programs to individualize drug regimens has stimulated interest in modeling population pharmacokinetics. This study used the NPEM2 software to determine cyclosporine population pharmacokinetic parameter values and distributions in a first group of 25 recipients of liver transplants during their first postoperative week. On a second group of 25 patients, the authors used these values to evaluate Bayesian predictive performance of cyclosporine blood concentrations with the USC*PACK PC program. During the study period, all the patients have been treated by continuous intravenous infusion. The one-compartment model pharmacokinetic parameter-the slope of volume to body weight (Vs) and the elimination rate constant (Kel) values found (mean values: Vs = 2.177 l/kg, Kel = 0.235 h(-1); median values: Vs = 1.559 l/kg, Kel = 0.163 h(-1); the percent coefficient of variation (Vs = 92%, Kel = 79%) appear reasonable and show the ability of NPEM2 to deal with sparse data. When the predictions were studied with day 1, day 2, or day 3 concentrations, predictive bias was respectively -0.030, -0.013, and 0.013 microg/ml, suggesting a greater clearance of cyclosporine immediately after surgery, the clearance decreasing in the days after. With the first three blood levels and the Bayesian fitting procedure, it was possible to predict at least half the subsequent measured blood levels of each patient accurately (within 20%) in more than three-quarters (76%) of the second group of recipients of transplants, and for 40% of patients the authors obtained accurate predictions in 100% of the subsequent blood levels. For a few patients (12%) they found quite poor predictions. The reason for this is unclear. The results suggest that this population model and the Bayesian fitting procedure using two or three blood levels can be reasonably and carefully used to control, in real time, cyclosporine blood levels in a majority of new patients with liver transplants.

Effect of the number of samples on Bayesian and non-linear least-squares individualization: a study of cyclosporin treatment of haematological patients with multidrug resistance

We have studied whether the prediction of drug concentrations improves as the number of samples used for individualization is increased, and whether the Bayesian method of individualization is superior to the non-linear least-squares method. Data were obtained from ten adult haematological patients with multidrug resistance who were treated with cyclosporin. The predictions of blood-cyclosporin concentrations were made using the Abbott PKS program. The number of samples used for individualization was increased from 1 to 30 for the Bayesian method and from 4 to 30 for the non-linear least-squares method. Linear regression, percentage prediction error, and absolute and relative predictive performance were used to evaluate the predictions. The results show that the Bayesian method affords greater precision than the non-linear least-squares method, but that the non-linear least-squares method is more accurate and results in less bias. Whereas for linear regression predictions improve as the number of samples is increased, other evaluations show improvement in the range from 5 to 11 samples; linear regression, percentage prediction errors and prediction bias support the opinion that the Bayesian method progressively becomes the non-linear least-squares method as the number of samples used for individualization is increased, but the accuracy and precision of prediction do not support this opinion. The study supports the statement that Bayes' law requires parameters from an infinite population, otherwise the advantage of the Bayesian method might be marginal.

Predictive performance of a vancomycin-aminoglycoside population model

OBJECTIVE

To evaluate the Wragge-Cooper method of predicting vancomycin serum concentrations utilizing knowledge of aminoglycoside pharmacokinetic parameters in general medicine and intensive care unit populations, and to develop a revised model if necessary.

DESIGN

This study consists of two phases evaluating 50 adults receiving concurrent vancomycin and aminoglycoside therapy. Patients were identified by a retrospective review of medical records. Bayesian analysis of measured serum aminoglycoside and vancomycin concentrations was performed to determine the individualized pharmacokinetic parameters. Phase I of the study tested the predictive performance of a published model incorporating aminoglycoside elimination (Wragge-Cooper) in 25 patients (group 1), and a revised model was developed. Phase II determined the predictive performance of the revised model (revised) and its performance relative to the Wragge-Cooper model and a traditional model incorporating estimated creatinine clearance (traditional) in an additional 25 patients (group 2).

SETTING

Two tertiary care university teaching hospitals.

MAIN OUTCOME MEASURES

The predictive performance of the models was determined by comparing predicted with measured vancomycin serum concentrations. Bias and precision were evaluated by calculating the mean prediction error (ME) and mean absolute error (MAE), respectively. Linear regression was performed to determine relationships between parameters.

RESULTS

The Wragge-Cooper model consistently underpredicts vancomycin serum concentrations in general medicine and intensive care unit populations (ME = -5.18, MAE = 6.63). Relative predictive performance analysis indicates no significant difference in bias or precision between the traditional and Wragge-Cooper models (delta ME 1.17, delta MAE -0.80). Regression analysis of individualized aminoglycoside and vancomycin elimination derived from patients in group 1 reveals the following relationship: vancomycin k10 (1/h) = 0.081 + 1.037ke,amg, r = 0.73. The revised model is significantly less biased and more precise compared with the traditional model (delta ME -4.48; delta MAE 1.22), and is significantly less biased (delta ME 4.29) but no more precise than the Wragge-Cooper model (delta MAE -0.58), using patients from group 2.

CONCLUSIONS

The revised model is an accurate method of predicting vancomycin serum concentrations in both general medicine and intensive care unit populations. Use of this model enables individualization of vancomycin dosage in patients receiving concurrent aminoglycoside therapy and minimizes vancomycin serum concentration monitoring.

Pharmacokinetics of drugs used in critically ill adults

Critically ill patients exhibit a range of organ dysfunctions and often require treatment with a variety of drugs including sedatives, analgesics, neuromuscular blockers, antimicrobials, inotropes and gastric acid suppressants. Understanding how organ dysfunction can alter the pharmacokinetics of drugs is a vital aspect of therapy in this patient group. Many drugs will need to be given intravenously because of gastrointestinal failure. For those occasions on which the oral route is possible, bioavailability may be altered by hypomotility, changes in gastrointestinal pH and enteral feeding. Hepatic and renal dysfunction are the primary determinants of drug clearance, and hence of steady-state drug concentrations, and of efficacy and toxicity in the individual patient. Oxidative metabolism is the main clearance mechanism for many drugs and there is increasing recognition of the importance of decreased activity of the hepatic cytochrome P450 system in critically ill patients. Renal failure is equally important with both filtration and secretion clearance mechanisms being required for the removal of parent drugs and their active metabolites. Changes in the steady-state volume of distribution are often secondary to renal failure and may lower the effective drug concentrations in the body. Failure of the central nervous system, muscle, the endothelial system and endocrine system may also affect the pharmacokinetics of specific drugs. Time-dependency of alterations in pharmacokinetic parameters is well documented for some drugs. Understanding the underlying pathophysiology in the critically ill and applying pharmacokinetic principles in selection of drug and dose regimen is, therefore, crucial to optimising the pharmacodynamic response and outcome.

Decision support in healthcare

To address the recognized problems associated with information overload and limited human memory, computer-based systems which help healthcare providers use information to make better decisions have been developed and implemented. These decision aids are designed to improve the quality and reduce the cost of healthcare. Currently, the most widely used computer application is to simply provide needed facts about the patient in an organized and timely fashion. Additionally, healthcare workers can access literature, ask questions of aggregates of patient data for clinical or administrative decisions, receive warnings or suggestions when the patient's data satisfy certain logical rules receive critiques when proposing therapies or ordering diagnostic tests, receive guidelines for standards of care, access programs which analyze tradeoffs and likelihoods of alternative outcomes (decision analysis) and receive lists of differential diagnoses. Given this wonderful panoply of capabilities, the question becomes 'why aren't more people using these aids and what are the demonstrated benefits of such capabilities?' In this paper we review the types of decision aids which have been successfully implemented and the challenges to implementation (knowledge representation, connections to databases, need for comprehensive, coded databases and evaluation of benefits).

Aminoglycoside pharmacokinetics: volume of distribution in specific adult patient subgroups

OBJECTIVE

To review the published clinical studies reporting volume of distribution for aminoglycosides in patient population subgroups. Controversies in methods used are discussed.

DATA SOURCES

National Library of Medicine Medlars, searched using MEDLINE.

STUDY SELECTION

Published articles primarily relating to pharmacokinetic properties of aminoglycosides in specific adult patient subgroups published within the past 20 years were included. Pediatric studies were excluded.

DATA EXTRACTION

The methodology, assay technique, and results from clinical studies reporting aminoglycoside volume of distribution were evaluated.

DATA SYNTHESIS

Clinical studies reporting aminoglycoside volume of distribution subgroups, including the methods by which their results were derived, are tabulated.

CONCLUSIONS

The expected aminoglycoside volume of distribution can vary, depending on a patient's clinical presentation. Data presented will allow a more refined initial estimation of volumes of distribution, which may facilitate the achievement of desired aminoglycoside serum concentrations.

Adaptive control of drug dosage regimens: basic foundations, relevant issues, and clinical examples

In this paper we examine several of the fundamental foundations and relevant clinical issues in adaptive control of drug dosage regimens for patients. Truly individualized therapy with drugs having narrow margins of safety first requires a practical pharmacokinetic/dynamic model of the behavior of a drug. Past experience with a drug is stored in the form of a population model. Next, using the information in such a model and its relationship to the incidence of adverse reactions, a specific, explicit therapeutic goal must be selected by the responsible clinician, based on the patient's need for the drug and the risk of adverse reactions felt to be justified by each patient's need, small, moderate, or great. Individualized drug therapy thus begins with the selection of individualized therapeutic goals (low, moderate, or high) for each patient. Using subsequent feedback from the patient's serum drug levels, and using Bayesian fitting, the model is then linked to each patient as a patient-specific model. Control of the model by the dosage regimen increasingly controls the patient, to better obtain the desired explicit therapeutic goals. This process is essentially similar to that of a flight control or missile guidance system.

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

We report an alternative dose-finding approach for the selection of optimal prophylactic aminoglycoside dosage in specific (sub)populations of patients. Relative a priori utility of several intervals of gentamicin or tobramycin (AMG) peak and trough serum levels were assigned by a group of pharmacokinetics experts, assuming prophylactic administration for laryngectomy interventions. A group of 27 adult patients, with normal renal function, undergoing elective surgery for laryngeal problems and treated prophylactically with gentamicin (80 mg t.i.d.) or tobramycin (100 mg t.i.d.) was studied. Two blood samples (peak and trough) were drawn at steady-state for AMG assay. Three different methods, standard two-stage (STS), extended least-squares non-linear regression [MULTI(ELS)] and non-parametric expected maximization (NEPM), were used to estimate the pharmacokinetic (PK) population parameters. PK simulations were applied to estimate the AMG steady-state concentrations from the PK population parameters. From these data, relative utility values were calculated, allowing the selection of the optimal dosage schedule for this group of patients. There were no statistically significant differences between the PK population estimates as generated by the three methods. Using the STS estimates, the simulation of several dosages indicated that the optimal dosage is 170 mg every 8 h. Conversely, using the individual PK parameters and the mean AMG levels simulated from them, the dose with best relative utility is 130 mg every 8 h. This important difference points out the relevance of the use of relative utilities for the AMG serum concentrations in the selection of optimal a priori dosage. We propose the use of 120 mg every 8 h as the safer dose for our population. Further studies are needed to validate this proposal in patients similar to ours.

A graphical user interface to facilitate patient-specific drug dosing

This paper describes the development of a graphical user-interface (GUI) designed to facilitate the process of generating patient-specific drug doses using bayesian modelling software. The graphical user interface was developed in Visual Basic (Microsoft, Inc.) and runs under the Windows 3.1 (Microsoft, Inc.) operating system. Patient demographic data are stored in a relational database (Access, Microsoft, Inc.). The GUI and the database communicate via dynamic data exchange links. The largely object-oriented nature of the language allowed us to change the entire look and feel of the system with a few simple changes to the underlying code. Use of the relational database to store patient demographic information allows greater flexibility in searching for and displaying patient specific information. The GUI reduces the time required to enter data. The prototype has allowed us to experiment with different presentation methods, greatly improving the clinical acceptance of the dosing programs.

Aminoglycoside monitoring in the once- or twice-daily era. The Dutch situation considered

The results of an inquiry among Dutch hospital pharmacists on the monitoring of aminoglycosides are presented and the relevance of monitoring is discussed. The vast majority of Dutch hospitals (47 out of 65) use aminoglycosides in a twice-daily dosage regimen, whereas 12 hospitals use a once-daily dose. The timing of peak level sampling is usually 30 min after the end of an intravenous infusion of 20-30 min. Mean 'therapeutic' peak levels of gentamicin were 7-13 mg/l in the once-daily group, 6.4-9.6 mg/l in the twice-daily group and 5-9 mg/l in the small thrice-daily group. Little or no evidence has been published to substantiate a real therapeutic range for aminoglycosides, concerning a relationship between peak or trough levels of aminoglycosides and clinical efficacy, ototoxicity and nephrotoxicity. All studies have been performed with the conventional thrice-daily regimen. No therapeutic range can be defined yet for once-daily or twice-daily aminoglycosides. The monitoring of aminoglycosides may be helpful to reduce the variability in serum levels after a standard dose.

Studies on drug monitoring in thrice and once daily treatment with aminoglycosides

OBJECTIVES

To investigate at what time the peak level should be determined under conventional thrice daily (t.i.d.) administration of the aminoglycoside netilmicin and to study its serum concentrations under once daily (od) treatment to define the required daily dose and to gain information about convenient drug monitoring.

DESIGN

The design of the study was a consecutive sample trial.

SETTING

The study took place in a university hospital.

PATIENTS

41 intubated patients of a surgical ICU who received netilmicin as a short-term infusion over 30 min for life-threatening infections were included in the study.

INTERVENTIONS

In 21 patients netilmicin was administered t.i.d. The virtual peak levels which had been determined by pharmacokinetic dosage calculation were compared with the serum concentrations obtained directly after the administration as well as after 15, 30, 60 and 180 min. In 20 patients the netilmicin serum concentrations during od treatment were determined directly before and immediately after the application as well as 0.5, 1, 3, 7 and 12 h later. To achieve a virtual peak level of 25 mg/l and a trough level of 0.5 mg/l individual adjustment of the dosage based on pharmacokinetic calculations was performed.

MEASUREMENTS AND RESULTS

In t.i.d. treatment the serum concentration measured after 30 min was closest to the virtual peak level; therefore, this is the best time to determine the peak level. In od treatment the required daily dose was 7.86 mg/kg body weight (median) in patients with normal renal function. During od dosing the trough level was extremely important in drug monitoring, whereas determination of the high peak level was of doubtful value.

CONCLUSIONS

The peak level should be determined during t.i.d. administration at 30 min. In od treatment the initial daily dose should be 7 mg/kg body weight; in drug monitoring the trough level is very important.

Population pharmacokinetic parameters for Bayesian monitoring of amikacin therapy in intensive care unit patients

The pharmacokinetics of amikacin has been studied in 40 intensive care unit patients using the bayesian estimation method implemented in the USC PC PACK program of Jelliffe. The volume of the central compartment was significantly higher in these patients than in the reference population, while other pharmacokinetic parameters did not differ significantly from the reference values. The population values may be employed, in addition to those supplied with the software, to adapt dosage regimens of amikacin in ICU patients.

Population pharmacokinetics of gentamicin. Use of the nonparametric expectation maximisation (NPEM) algorithm

A new nonparametric expectation maximisation (NPEM) algorithm for the estimation of population pharmacokinetic parameter values was evaluated. The algorithm, in the form of a personal computer program, was used to compute population pharmacokinetic parameter densities of gentamicin in a group of 9 patients with indicators of malnutrition. The 1-compartment parameter values for clearance (CL), volume of distribution (Vd) and elimination rate constant (k) were compared with values generated using a standard 2-stage (STS) approach. NPEM was used with a full data set (72 gentamicin concentrations) and a sparse data set (only peak and trough concentrations for each patient; 18 in total). There were no differences in parameter value estimations between the STS and NPEM with all the data (p greater than 0.05) or with the sparse data (p greater than 0.05). Mean parameter value estimates from the STS and NPEM (with sparse data) were used as a priori data sets in the USC*PACK gentamicin Bayesian program to predict concentrations in 8 subsequent patients with similar indicators of malnutrition. There were no differences in predicted gentamicin concentrations between STS (3.75 +/- 2.06 mg/L) and NPEM (3.75 +/- 2.17 mg/L). NPEM was able to generate population pharmacokinetic parameter values for gentamicin in a defined population of patients using sparse routine clinical data. It was also shown to function with only a single data point per patient.