There are limited intravenous fosfomycin disodium (IVFOS) dosing regimens to treat carbapenem-resistant Enterobacterales (CRE) infections. This study aimed to use Monte Carlo simulation (MCS) for evaluation of IVFOS dosing regimens in critically ill patients with CRE infections. The dosing regimens in critically ill patients with various creatinine clearance were evaluated with MCS using minimum inhibitory concentration (MIC) distributions of fosfomycin against CRE clinical isolates in Thailand and the 24 h area under the plasma drug concentration–time curve over the minimum inhibitory concentration (AUC_0-24/MIC) of ≥21.5 to be a target for IVFOS. The achieved goal of the probability of target attainment (PTA) and a cumulative fraction of response (CFR) were ≥90%. A total of 129 non-duplicated CRE clinical isolates had MIC distributions from 0.38 to >1024 mg/L. IVFOS 8 g every 8 h, 1 h, or 4 h infusion, could achieve approximately 90% PTA of AUC_0-24/MIC target to treat CRE infections with MICs ≤ 128 mg/L. According to PTA target, an IVFOS daily dose to treat carbapenem-resistant Escherichia coli based on Clinical Laboratory Standards Institute (CLSI) breakpoints for urinary tract infections and one to treatment for CRE infections based on the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints were 16 g/day and 8 g/day, respectively. All dosing regimens of IVFOS against CRE achieved CFR ≤ 70%. This study proposes the IVFOS dosing regimens based on CLSI and EUCAST breakpoints for the treatment of CRE infections. However, further clinical studies are needed to confirm the results of these findings.

OBJECTIVE: Although vancomycin dosage recommendations in the pediatric setting for methicillin-resistant Staphylococcus aureus (MRSA) infection indicate that ≥60 mg/kg/day is correlated to a desired area under the vancomycin concentration time curve from 0 to 24 hours to minimum inhibitory concentration ratio (AUC_{0–24 hr}/MIC) ≥400, for some patients this dosage is inadequate or relates to toxicity. This study purposed to explore vancomycin dosing for pediatrics with various degrees of renal function. METHODS: Routine monitoring data were retrospectively collected from patients, aged 1 month to 18 years. Population pharmacokinetic analysis was performed by using non-linear mixed-effect model with NONMEM software, and Monte Carlo simulation was conducted by using Crystal Ball software. RESULTS: Two hundred twelve patients with 348 vancomycin serum concentrations were included. Median age was 3.5 years (IQR, 0.9–10.9), median weight was 14.0 kg (IQR, 7.2–30.4), with baseline estimated glomerular filtration rate (eGFR) ranging from 15.5 to 359.3 mL/min/1.73 m². A 1-compartment model with first-order elimination sufficiently described vancomycin PK. The dosing targeting AUC_0–24hr/MIC ≥400 and AUC_0–24hr 85% of the cumulative fraction of response across the MIC distribution of MRSA. CONCLUSIONS: Vancomycin dosing of 12.5, 25, 40, 60, and 70 mg/kg/day is suggested for pediatric patients with eGFRs of 15 to 29, 30 to 59, 60 to 89, 90 to 129, and 130 to 160 mL/min/1.73 m², respectively.

Background and objective Vancomycin pharmacokinetics have been described by both one- and two-compartment models. One-compartment models are widely used to predict the area under the curve (AUC), a useful parameter for determining the efficacy and safety of vancomycin, based on sparse data collected during therapeutic drug monitoring. It is uncertain whether AUCs from one-compartment models with sparsely sampled data can sufficiently represent the true AUC. This study aimed to compare AUC estimates from one- and two-compartment models using sparse data. The reliability of AUCs from models constructed with trough-only data was also assessed. Methods A previously published robust model was used to simulate vancomycin concentration points at 15 min intervals in 100 patients. From these simulated data, the reference AUC (AUC_ref) was calculated and two depleted dataset versions (trough-only and peak-trough datasets) were also created. One- and two-compartment models were built from the depleted datasets with the use of NONMEM. Vancomycin 24-hour AUC was calculated from concentration–time profiles of each model by a linear trapezoidal formula at three different time periods: 0–24 hours (AUC_0–24), 24–48 hours (AUC_24–48) and 0–48 hours (AUC_avg). The deviation of each of the AUCs from the AUC_ref was examined to assess the AUC predictability of models from sparse data. The difference in AUCs between one- and two-compartment models was analysed from statistical and clinical perspectives. Results When assessing the deviation of each AUC from the AUC_ref, the one-compartment model from both peak-trough and trough-only data could adequately represent the true AUC with no statistically significant differences. Two-compartment model from peak-trough data also provided similar AUC estimates with the AUCref. However, AUCs from the two-compartment model with trough-only data did not adequately represent the true AUC, with significant differences of 25.16% for AUC_0–24, 15.92% for AUC_24–48 and 19.45% for AUC_avg. Conclusion Regardless of statistically significant differences between AUCs from one- and two-compartment models, the level of difference was acceptable from the clinical perspective, being <17% in models from peak-trough data. Therefore, both one- and two-compartment models with sparse data having at least a pair of peak-trough data per patient could be reliable for predicting AUC. Furthermore, AUCs of the one-compartment model from trough-only data did not show a significant difference from the AUC_ref. Hence, one-compartment models developed from trough-only data could be useful for predicting AUC when models with rich data are not available for the intended population. However, it is suggested that the use of the two-compartment model built from trough-only data should be avoided.