Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm. The estimations of the developed model were assessed using mean error (ME) as a measure of bias and mean squared error (MSE) as a measure of precision. A total of 331 serum amikacin concentrations were obtained from 56 patients. The mean (±SD) model parameter values found were V c = 0.2302 ± 0.0866 L/kg, k slope = 0.004045 ± 0.00705 min per unit of creatinine clearance, k 12 = 2.2121 ± 5.506 h−1, and k 21 = 1.431 ± 2.796 h−1. The serum concentration data were estimated with little bias (ME = −0.88) and good precision (MSE = 13.08). The present study suggests that amikacin pharmacokinetics in adult Kuwaiti ICU patients are generally rather similar to those found in other patients. This population model would provide useful guidance in developing initial amikacin dosage regimens for such patients, especially using multiple model (MM) dosage design, followed by appropriate Bayesian adaptive control, to optimize amikacin dosage regimens for each individual patient.

Objective: To compare five published nomograms (Thomson guidelines, Mawer nomogram, rule of eights, Hull-Sarubbi table and Dettli method) for calculating the initial gentamicin dosage regimen in a Kuwaiti population. Materials and Methods: Based on measured peak and trough gentamicin concentrations, the elimination rate constant and volume of distribution of gentamicin were calculated for each patient (n = 56), using a modified two-point Sawchuk-Zaske method. The calculated individual set of pharmacokinetic parameters and the initial dose regimen recommended by each of the five methods were used to predict the steady-state peak and trough of gentamicin concentrations. Results: The Thomson guidelines produced consistent results in predicting gentamicin concentrations within the target ranges of peak plus trough, peak only and trough only (63, 75 and 75%, respectively). The Mawer nomogram, Hull-Sarubbi table and Dettli methods achieved similar percentages of patients (46–50%) within the target ranges (5–10 mg · l^–1 for peak and 0.5–2 for trough), whereas empirical dosing and the rule of eights showed the lowest percentages of patients within the peak plus trough target range (25 and 37%, respectively). However, with respect to the underdosing target range (predicted concentration <5 mg · l^–1), the Thomson guidelines showed that 21% of patients were underdosed. Conclusion: The results show that a large number of patients (37–63%) were outside the target ranges in all initial gentamicin dosing methods evaluated in this study. Therefore, serum concentration measurement can be advised to assist in the optimization of gentamicin dose selection.

Objective: To report the pharmacokinetics of gentamicin using traditional multiple daily doses and a high-dose regimen in an elderly patient. Clinical Presentation and Intervention:An 80-year-old male who presented with mild renal failure received two different gentamicin dosing regimens, 60 mg every 8 h for septicemia and a high dose of 400 mg with extended interval for suspected endocarditis. Based on population parameters of k_e (0.1030 h^–1) and V_d (18.1 liters), the initial gentamicin dosage regimen was calculated to be 80 mg every 12 h. The measured peak and trough concentrations were used to calculate the individual parameters of k_e (0.0749 h^–1) and V_d (30.9 liters). After a 5-mg·kg^–1 gentamicin dose, the Hartford nomogram was used to estimate the extended dosage interval. Conclusion: The Hartford nomogram may be a valid tool for estimating the dosage interval after a 5-mg·kg^–1 single dose of gentamicin.