Population Pharmacokinetics of Intraperitoneal Gentamicin and the Impact of Varying Dwell Times on Pharmacodynamic Target Attainment in Patients with Acute Peritonitis Undergoing Peritoneal Dialysis.

While the use of intraperitoneal (i.p.) gentamicin is common in the treatment of peritoneal dialysis (PD)-related infections, the ability of these regimens to attain pharmacodynamic target indices of interest in blood and dialysate has not been widely reported. Pharmacokinetic (PK) data were obtained and analyzed from a multiple-dose PK study of i.p. gentamicin with 24 patients who received the drug at 0.6 mg/kg dose of body weight. The probability of target attainment (PTA) for indices of treatment success (i.p. peak/MIC ratio > 10) and toxicity (plasma area under the concentration-time curve [AUC] < 120 mg·h/L) was determined for 0.3- to 1.2-mg/kg i.p. regimens every 24 h for dwell times of 2 to 6 h and for the duration of a 2-week course. In the peritoneum, successful PTA was achieved by all of the simulated regimens up to an MIC of 1 mg/L and by doses equal to or greater than 0.6 mg/kg up to the MIC of 2 mg/L. At the susceptibility breakpoint of 4 mg/L, only the highest dose of 1.2 mg/kg is likely to provide adequate PTA. The probability of achieving exposure below the threshold of 120 mg·h/L in the daily AUC in plasma seems acceptable for all regimens at or below 0.6 mg/kg. Based on the model we developed, a gentamicin dose of 0.6 mg/kg is sufficient to treat organisms with an MIC of ≤2 mg/L without the risk of significant systemic exposure. The 1.2-mg/kg dose necessary to reach the pharmacodynamic target for efficacy at the clinical breakpoint of 4 mg/L is likely to produce early toxic levels of exposure that are expected to be detrimental to the renal system.

Comparative Evaluation of the Predictive Performances of Three Different Structural Population Pharmacokinetic Models To Predict Future Voriconazole Concentrations.

Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously, but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and nonlinear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into individually designed optimum dosing strategies (ID-ODS) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used were from two independent data sets, patients with proven or suspected invasive fungal infections (n = 57) and hematopoietic stem cell transplant recipients (n = 10). Observed voriconazole concentrations were predicted whereby for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95% CI) were calculated to measure absolute bias and precision, while ΔME and ΔMSE and their 95% CI were used to measure relative bias and precision, respectively. A total of 519 voriconazole concentrations were analyzed using three models. MEs (95% CI) were 0.09 (-0.02, 0.22), 0.23 (0.04, 0.42), and 0.35 (0.16 to 0.54) while the MSEs (95% CI) were 2.1 (1.03, 3.17), 4.98 (0.90, 9.06), and 4.97 (-0.54 to 10.48) for the linear, mixed, and nonlinear models, respectively. In conclusion, while simulations with the linear model were found to be slightly more accurate and similarly precise, the small difference in accuracy is likely negligible from the clinical point of view, making all three approaches appropriate for use in a voriconazole TDM program.