Aminoglycoside Forecasting in Neutropenic Patients with Cancer

Kosirog, Jane L.; Rospond, Raylene M.; Destache, Christopher J.; Hall, Phillip M.

doi:10.2165/00003088-199324010-00007

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Gross assumption of similarity between populations may give rise to less precise prediction. For example, application of models developed in the critically ill for neutropenic cancer patients resulted only in good to fair precision [62]. Even after selecting the most relevant population model, predictive performance of different models available may be somehow variable [63].…”

Section: Pharmacometric Approachesmentioning

confidence: 99%

Optimization of dosing regimens and dosing in special populations

Sime

Roberts

2015

Clinical Microbiology and Infection

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Treatment of infectious diseases is becoming increasingly challenging with the emergence of less-susceptible organisms that are poorly responsive to existing antibiotic therapies, and the unpredictable pharmacokinetic alterations arising from complex pathophysiologic changes in some patient populations. In view of this fact, there has been a progressive work on novel dose optimization strategies to renew the utility of forgotten old antibiotics and to improve the efficacy of those currently in use. This review summarizes the different approaches of optimization of antibiotic dosing regimens and the special patient populations which may benefit most from these approaches. The existing methods are based on monitoring of antibiotic concentrations and/or use of clinical covariates. Measured concentrations can be correlated with predefined pharmacokinetic/pharmacodynamic targets to guide clinicians in predicting the necessary dose adjustment. Dosing nomograms are also available to relate observed concentrations or clinical covariates (e.g. creatinine clearance) with optimal dosing. More precise dose prediction based on observed covariates is possible through the application of population pharmacokinetic models. However, the most accurate estimation of individualized dosing requirements is achieved through Bayesian forecasting which utilizes both measured concentration and clinical covariates. Various software programs are emerging to ease clinical application. Whilst more studies are warranted to clarify the clinical outcomes associated with the different dose optimization approaches, severely ill patients in the course of marked infections and/or inflammation including those with sepsis, septic shock, severe trauma, burns injury, major surgery, febrile neutropenia, cystic fibrosis, organ dysfunction and obesity are those groups which may benefit most from individualized dosing.

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Section: Pharmacometric Approachesmentioning

confidence: 99%

Optimization of dosing regimens and dosing in special populations

Sime

Roberts

2015

Clinical Microbiology and Infection

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“…The most useful method for such an analysis is the population approach (33), which we recently used to study the pharmacokinetics of teicoplanin in the same population (21). Knowledge of population pharmacokinetic parameters allows individualization of the antibiotic dosage either before or after drug administration by using Bayesian methods (19).…”

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confidence: 99%

Population Pharmacokinetic Study of Amikacin Administered Once or Twice Daily to Febrile, Severely Neutropenic Adults

Tod

Lortholary

Seytre

et al. 1998

Antimicrob Agents Chemother

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Once-daily (o.d.) administration of 20 mg of amikacin per kg of body weight to neutropenic patients has been validated by clinical studies, but amikacin pharmacokinetics have been documented only for the 7.5-mg/kg twice-daily (b.i.d.) regimen in this population. In order to determine in neutropenic patients (i) the influence of the dosing regimen on the kinetics of amikacin, (ii) the linearity of kinetics of amikacin in the range of 7.5 to 20 mg/kg, and (iii) the influence of patient characteristics on the disposition of amikacin and (iv) to provide a rationale for dosing recommendations, we evaluated the population pharmacokinetics of amikacin administered to 57 febrile neutropenic adults (neutrophil count, <500/mm3) being treated for a hematological disorder and receiving amikacin at 7.5 mg/kg b.i.d. (n = 29) or 20 mg/kg o.d. (n = 28) and administered intravenously over 0.5 h. A total of 278 blood samples were obtained (1 to 14 samples per patient) during one or several administration intervals (1 to 47). Serum amikacin levels were measured by the enzyme-multiplied immunoassay technique. A mixed-effect modeling approach was used to fit a bicompartmental model to the data (NONMEM software). The influences of the dosing regimen and the demographic and biological indices on the pharmacokinetic parameters of amikacin were evaluated by the maximum-likelihood ratio test on the population model. The dosing regimen had no influence on amikacin pharmacokinetic parameters, i.e., the kinetics of amikacin were linear over the range of 7.5 to 20 mg/kg. Amikacin elimination clearance (CL) was only correlated with creatinine clearance or its covariates, namely, sex, age, body weight, and serum creatinine level. The interindividual variability of CL was 21%, while those of the central volume of distribution, the distribution clearance, and the tissue volume of distribution were 15, 30, and 25%, respectively. On the basis of the expected distribution of amikacin concentrations in this population, dosing recommendations as a function of creatinine clearance (CLCR) are proposed: for patients with normal renal function (CLCR of 80 to 130 ml/min), 20 mg/kg o.d. is recommended, whereas for patients with severe renal impairment (CLCR, 10 to 20 ml/min), a dosage of 17 mg/kg every 48 h is recommended.

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“…Those investigators concluded that the combination of an optimal sampling strategy with Bayesian parameter estimation was a sound alternative means of determining pharmacokinetic parameters, in contrast to conventional, extensive sampling strategies. Bayesian parameter estimation has been applied to target populations such as critically ill and elderly individuals for several antibiotics (e.g., vancomycin [16], quinolones [4,12], and aminoglycosides [8,21]) but not for ceftazidime. Since the only population to which these tools have been applied for ceftazidime is volunteers with cystic fibrosis (a population with high drug clearance) and since ceftazidime is used in many patients with impaired renal function, such an analysis was warranted.…”

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confidence: 99%

Development and evaluation of a Bayesian pharmacokinetic estimator and optimal, sparse sampling strategies for ceftazidime

Kashuba

Ballow

1996

Antimicrob Agents Chemother

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Data were gathered during an activity-controlled trial in which seriously ill, elderly patients were randomized to receive intravenous ceftazidime or ciprofloxacin and for which adaptive feedback control of drug concentrations in plasma and activity profiles was prospectively performed. The adaptive feedback control algorithm for ceftazidime used an initial population model, a maximum a posteriori (MAP)-Bayesian pharmacokinetic parameter value estimator, and an optimal, sparse sampling strategy for ceftazidime that had been derived from data in the literature obtained from volunteers. Iterative two-stage population pharmacokinetic analysis was performed to develop an unbiased MAP-Bayesian estimator and updated optimal, sparse sampling strategies. The final median values of the population parameters were follows: the volume of distribution of the central compartment was equal to 0.249 liter/kg, the volume of distribution of the peripheral compartment was equal to 0.173 liter/kg, the distributional clearance between the central and peripheral compartments was equal to 0.2251 liter/h/kg, the slope of the total clearance (CL) versus the creatinine clearance (CLCR) was equal to 0.000736 liter/h/kg of CL/1 ml/min/1.73 m2 of CLCR, and nonrenal clearance was equal to + 0.00527 liter/h/kg. Optimal sampling times were dependent on CLCR; for CLCR of > or = 30 ml/min/1.73 m2, the optimal sampling times were 0.583, 3.0, 7.0, and 16.0 h and, for CLCR of < 30 ml/min/1.73 m2, optimal sampling times were 0.583, 4.15, 11.5, and 24.0 h. The study demonstrates that because pharmacokinetic information from volunteers may often not be reflective of specialty populations such as critically ill elderly individuals, iterative two-stage population pharmacokinetic analysis, MAP-Bayesian parameter estimation, and optimal, sparse sampling strategy can be important tools in characterizing their pharmacokinetics.

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Aminoglycoside Forecasting in Neutropenic Patients with Cancer

Cited by 8 publications

References 25 publications

Optimization of dosing regimens and dosing in special populations

Optimization of dosing regimens and dosing in special populations

Population Pharmacokinetic Study of Amikacin Administered Once or Twice Daily to Febrile, Severely Neutropenic Adults

Development and evaluation of a Bayesian pharmacokinetic estimator and optimal, sparse sampling strategies for ceftazidime

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