Aminoglycoside Forecasting in Neutropenic Patients with Cancer

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


To validate the population pharmacokinetic parameters of aminoglycoside disposition in patients with cancer, a retrospective evaluation of predictive performance of a Bayesian program was performed in 155 patients from 1986 to 1989 who received amikacin, gentamicin or tobramycin. Each patient received 1 of the 3 drugs and had initial drug concentration determination, with a second set of drug concentrations drawn ⩽14 days after the initial dose. Predictions of 64 amikacin, 144 gentamicin and 102 tobramycin concentrations were generated using 1-compartment model pharmacokinetic parameters, serum creatinine values, patients’ dosage history and demographic data. The mean (± SD) observed (and predicted) serum concentrations for amikacin were 18.9 ± 14.8 mg/L (17.2 ± 14.1 mg/L); for gentamicin were 4.49 ± 3.58 mg/L (4.26 ± 3.33 mg/L) and for tobramycin were 4.52 ± 3.70 mg/L (4.05 ± 3.49 mg/L) [p > 0.05]. Results demonstrated minimal bias with a mean error in gentamicin concentrations of −0.236 (95% CI −0.533: 0.0613). Significant (p <0.05) underprediction occurred in concentrations of tobramycin [−0.474 mg/L (95% CI −0.842: −0.0107)] and amikacin [−1.77 mg/L (95% CI −3.42: −0.114)]. Good precision is indicated by a mean squared error for gentamicin of 3.35 mg/L (95% CI 1.70: 4.99) and for tobramycin of 3.64 mg/L (95% CI 1.83: 5.44). Fair precision is demonstrated by amikacin [46.1 mg/L (95% CI 27.3: 65.0)]. Similar results were shown in a separate peak/trough analysis. These data indicate that aminoglycoside pharmacokinetics in patients with cancer can be predicted with minimal bias and good precision using a Bayesian forecasting program for gentamicin and tobramycin.

Original languageEnglish (US)
Pages (from-to)79-87
Number of pages9
JournalClinical Pharmacokinetics
Issue number1
StatePublished - Jan 1993

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmacology (medical)


Dive into the research topics of 'Aminoglycoside Forecasting in Neutropenic Patients with Cancer'. Together they form a unique fingerprint.

Cite this