Aims: The objectives of this study were to determine the population pharmacokinetics (PK) model of polymyxin B in critically ill patients with or without extracorporeal membrane oxygenation (ECMO) support that investigated the influence of ECMO on PK variability and to identify an optimal dosing strategy. Methods: Forty-four critically ill patients were enrolled, including eight patients with ECMO support. Eight serial serum samples were collected from each patient at steady state. The population PK was determined using NONMEM and Monte Carlo simulation was performed to evaluate the exposures of different dosing regimens. Results: The PK analyses included 342 steady-state concentrations and a two-compartment model was optimal for polymyxin B PK data modelling. In the final model, creatinine clearance (CLCR) was the significant covariate on CL (typical value 1.27 L/h; between-subject variability 15.1%) and ECMO did not show a significant impact on the polymyxin B PK. Additionally, we found that the PK parameter estimates of patients with and without ECMO support were mostly similar. Based on Monte Carlo simulations, the dose escalation of polymyxin B in patients with increased CLCR improved the probability of achieving required exposure. For patients with CLCR≤120 mL/min, a dosage regimen of 100mg every 12h may represent the optimal regimen at an MIC of 1 mg/L. Conclusion: The impact of ECMO on the polymyxin B PK is likely to be minimal. Our study showed a potential relationship between CLCR and polymyxin B CL, and the dose of polymyxin B should be adjusted in patients with increased CLCR.

Aims: This study aimed to investigate the potential impact of tacrolimus (TAC) exposure on clinical outcomes after lung transplantation. Methods: This retrospective observational study enrolled a total of 234 lung transplant recipients. TAC trough levels (C0) were collected for 3 intervals: 0–3 months, 3–12 months, and 12–24 months. The intra-patient variability (IPV) was calculated using coefficient of variation. Genotyping of CYP3A5*3 (rs776746) was performed. Patients were further divided into groups based on the C0 cut-off value of 8 ng/mL and IPV cut-off value of 30%. Cox proportional hazards regression models were used to explore the potential impact of C0 and IPV on outcomes of interests, including donor-specific antibodies (DSA), chronic lung allograft dysfunction (CLAD) and mortality. Results: The influence of CYP3A5*3 polymorphism was only significant for C0 and IPV during the first 3 months. Low C0 (< 8 ng/mL) at 3–12 months increased the risk of DSA (hazard ratio [HR] 2.820, 95% confidence interval [CI] 1.093–7.276) and mortality (HR 2.220, 95% CI 1.162–4.243), while High IPV (>=30%) during this period was associated with an increased risk of mortality (HR 2.100, 95% CI 1.120–3.937). Patients with Low C0/High IPV combination had significantly higher risks for DSA (HR 4.534, 95% CI 1.326–15.507) and survival (HR 4.205, 95% CI 1.739–10.168), surpassing the predictive power provided by C0 or IPV alone. Conclusion: A combination of Low C0/High IPV might be considered in categorizing patients towards risk of adverse clinical outcomes following lung transplantation.

Aims: Imipenem is a widely used antibiotic for the treatment of critically ill patients with severe infections. Here, we present a translational pharmacokinetic/pharmacodynamic mathematical model to assess fT>MIC and evaluate the clinical outcomes of imipenem treatment in critically ill patients. Methods: Critically ill patients with severe infections were included in our study. Blood samples at different time points were collected after imipenem plasma concentration reached a steady state in vivo. A one-compartment model was used for pharmacokinetic profiles. PK/PD parameters were calculated separately with or without a mathematical model. Clinical results were mainly defined as the microbiological results. The resolution of fever and the decrease in PCT and WBC levels were also considered. Results: A total of 54 patients were enrolled in our study. The fT>MIC calculated by the mathematical model was 67.26±39.96%, and the fT>MIC was 73.75±23.11% without the model. The PK/PD parameters calculated between the two groups were not significantly different. Regarding clinical outcomes, 35 (64.3%) patients were defined as having clinical success. The fT>MIC was 83.33±12.90% in the clinical success group and 59.42±19.11% in the clinical failure group. The fT>MIC was significantly different between the two groups (p=0.022). Based on the regimens, the PCT level decreased to at least 20% of the peak level and the WBC level decreased during the first 3 days when patients’ fT>MIC was greater than 70%. Conclusion: The pharmacokinetic mathematical model may be used for PK/PD parameter evaluation. To treat critically ill patients, achieving fT>MIC greater than 70% may be necessary.