Abstract: Voriconazole is a triazole antifungal agent with broad-spectrum activity against several common yeast and mold species. However, hepatotoxicity is a major adverse effect of voriconazole, and the lack of specific biomarkers for the detection and prediction of voriconazole-induced hepatotoxicity remains an urgent issue. In this study, ultra-high-performance liquid chromatography was coupled with mass spectrometry to analyze plasma and liver metabolites and determine possible plasma biomarkers for hepatotoxicity. Firstly, male C57BL/6J mice were randomly divided into four groups (each n = 7): control (0 mg/kg voriconazole), and 20, 40, and 80 mg/kg voriconazole. Voriconazole was intraperitoneally injected, and the resulting differential plasma and liver metabolites were identified to determine which differential metabolites were released from the liver into the periphery. Additionally, 133 plasma samples obtained from patients with (n = 45) and without (n = 88) hepatotoxicity were collected to further validate the differential metabolites found in the animal experiment. Alpha-ketoglutarate (AKG), 5-hydroxyindole, 7-ketolithocholic acid, 3-methylglutarylcarnitine, uracil, phosphatidylcholine (20:3/20:4), and lysophosphatidylcholine (22:6) were associated with voriconazole hepatotoxicity in a dose-dependent manner. Uracil (area under the curve (AUC): 0.979, 95% confidence interval (CI): 0.958-0.999), and AKG (AUC: 0.877, 95% CI: 0.812-0.941) were biomarkers of voriconazole-induced hepatotoxicity and showed great potential for clinical diagnosis. The combined biomarker composed of uracil, AKG, and alkaline phosphatase reached an AUC of 0.997 (95% CI: 0.993-0.999), showing that the combination of the three is a good choice to judge voriconazole-induced hepatotoxicity.

Aims: The pharmacokinetic (PK) profiles of voriconazole in intensive care unit (ICU) patients is quite different. We aimed to develop a population pharmacokinetic (PopPK) model to evaluate the effects of various biological covariates and the use of extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT). Methods: The modeling analysis of the pharmacokinetic parameters were conducted using the nonlinear mixed-effects modeling method (NONMEM) using a two-compartment model. Monte Carlo simulations (MCSs) were performed to observe the probability of target attainment (PTA) when receiving CRRT or not under different dosage regimens, different quick C-reactive protein (qCRP), and different minimum inhibitory concentration (MIC) ranges. Results: A total of 408 critically ill patients with 746 voriconazole concentration–time data points were included in this study. A two-compartment population PK model with qCRP, CRRT, creatinine clearance rate (CLCR), platelet (PLT), and prothrombin time (PT) as fixed effects was developed using the NONMEM. Conclusion: The results showed that qCRP, CRRT, CLCR, PLT, and PT affected the PK parameter clearance. The most commonly used clinical regimen of 200 mg q12h is sufficient for the most common sensitive pathogens (MIC ≤ 0.25 mg/L) in China, regardless of whether CRRT is performed, and at what level qCRP is. When the MIC is 0.5 mg/L, 200 mg q12h is insufficient only when qCRP is less than 40 mg/L and CRRT is performed. When MIC ≥ 2 mg /L, a dose of 300 mg q12h cannot achieve ≥ 90% PTA, and a higher dose needs to be explored.

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.

Background: Whether there is also a correlation between viral shedding duration and the clinical course of illness in COVID-19 has not yet been determined. Methods: In this retrospective study, we included 239 adult inpatients (all ≥18 years old) with laboratory-confirmed COVID-19 from the Wuhan Tongji Hospital (Wuhan, China). Results: Of the subset of 239 patients included in this study, 33 patients died due to COVID-19 and 45 patients demonstrated clinical progression to critical illness. Patients with a long duration of viral RNA shedding as compared to short duration of viral RNA shedding also had significantly lower mortality rates ((9.5% vs. 18.6%, P= 0.04) and a lower rate of progression to critical illness (16.7% vs. 21.2%, P= 0.37). Viral RNA shedding is an independent risk factor for mortality within 28 days of observation (OR 0.94, 95% CI: 0.88-0.99, P= 0.025, by multivariable regression analyses) and increased duration of viral shedding is correlated with a significant survival advantage (P =0.047) and lower risk of progression to critical illness (P =0.029, by Kaplan-Meier analyses). Conclusions: Prolonged viral RNA shedding duration is associated with improved patient prognosis and reduces the risk of progression to critical illness in this subset of COVID-19 patients. Further clinical studies are necessary to determine if a longer duration of viral RNA shedding with COVID-19 is predictive of an overall better patient prognosis and outcome.