Background:Invasive fungal infection (IFI) is one of the leading causes of early death after renal transplantation. Voriconazole (VRC) is the first-line drug of IFI. Because of the large inter- and intraindividual variability in VRC plasma concentrations and the narrow therapeutic window for treating patients with IFIs, it is crucial to study the factors which could influence pharmacokinetic variability. We performed a population pharmacokinetics (PPK) study of VRC for personalized medicine.Methods:A total of 125 trough concentrations (Cmin) from 56 patients were evaluated, retrospectively. Nonlinear mixed effect model was used to describe a PPK model that was internally validated by bootstrap method. Potential covariates included demographic characteristics, physiological and pathological data, concomitant medications, and CYP2C19 genotype.Results:A 1-compartment model with first-order absorption and elimination was fit to characterize the VRC pharmacokinetics in renal transplant recipients (RTRs). Aspartate aminotransferase (AST) had a significant influence on clearance (CL) while CYP2C19 genotype had a major impact on the volume of distribution (V). The parameters of CL and V were 4.76 L/h and 22.47 L, respectively. The final model was V (L) = 22.47 × [1 + 2.21 × (EM = 1)] × [1 + 4.67 × (IM = 1)] × [1 + 3.30 × (PM = 1)] × exp (0.96); CL (L/h) = 4.76 × (AST/33)^(−0.23) × exp (0.14). VRC Cmin in intermediate metabolizers was significantly higher than in extensive metabolizers.Conclusions:Liver function and CYP2C19 polymorphism are major determinants of VRC pharmacokinetic variability in RTRs. Genotypes and clinical biomarkers can determine the initial scheme. Subsequently, therapeutic drug monitoring can optimize clinical efficacy and minimize toxicity. Hence, this is a feasible way to facilitate personalized medicine in RTRs. In addition, it is the first report about PPK of VRC in RTRs.

Using a combination of CYP2C19 genotype and postoperative time to determine the initial voriconazole dosing regimens followed by therapeutic drug monitoring could help to advance individualized treatment in renal transplantation patients with invasive fungal infections.

Voriconazole is a broad-spectrum antifungal agent for the treatment of invasive fungal infections. There is limited information about the pharmacokinetics and appropriate dosage of voriconazole in patients with liver dysfunction. This study aimed to explore the relationship between voriconazole trough concentration (C trough) and toxicity, identify the factors significantly associated with voriconazole pharmacokinetic parameters and propose an optimised voriconazole dosing regimen for patients with liver dysfunction. Methods: The study prospectively enrolled 51 patients with 272 voriconazole concentrations. Receiver operating characteristic curves were used to explore the relationship between voriconazole C trough and toxicity. The pharmacokinetic data was

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Background Tigecycline has been widely used to treat hospital‐acquired pneumonia (HAP) off‐label since it is effective against a wide range of multidrug‐resistant bacteria. However, no recommended dosage for this indication has been evaluated, resulting in possible inadequate treatment. Aims The aims of this study are to establish the population pharmacokinetic (PPK) model of tigecycline in Chinese patients with HAP, as well as to evaluate the exposure‐response relationship for the treatment of HAP with multidrug‐resistant gram‐negative bacteria. Methods A PPK analysis of tigecycline was conducted on pooled data from 328 blood samples obtained from 89 patients with HAP. Tigecycline plasma concentrations were measured by a two‐dimensional liquid chromatographic system and the data were analysed using Phoenix NLMETM software. Exposure‐response analyses for efficacy were performed based on the data from 79 HAP patients with multidrug‐resistant gram‐negative infections. Classification and regression tree and logistic regression analyses were employed to identify which pharmacokinetic‐pharmacodynamic (PK‐PD) indices and magnitudes were the significant predictors of tigecycline efficacy. Results A two‐compartment model with zero‐order absorption and first‐order elimination adequately described the data. A larger body weight was associated with increased central volume of distribution and clearance (P < .005), and increased age, baseline creatinine concentration and aspertate aminotransferase were associated with decreased clearance (P < .005). The AUC0‐12h × V/MIC ratio, APACHEII score and combined Pseudomonas aeruginosa infection are the strong predictors for tigecycline clinical response. Classification and regression tree analyses indicated that the combination of APACHEII score < 24 and AUC0‐12h × V/MIC ratio ≥ 100 was associated with clinical success. Conclusions The proposed PPK model may serve as the basis for estimating tigecycline exposure for PK‐PD analyses, and the PK‐PD index and magnitude found in this study could be used for designing proper dosage regimens of tigecycline.