No abstract

This study showed that optimal voriconazole dosage regimens could be determined successfully with prospective population pharmacokinetic analyses and Monte Carlo simulations.

Background Clinical practice guidelines or recommendations often require timely and regular updating as new evidence emerges, because this can alter the risk-benefit trade-off. The scientific process of developing and updating guidelines accompanied by adequate implementation can improve outcomes. To promote better management of patients receiving vancomycin therapy, we updated the guideline for the therapeutic drug monitoring (TDM) of vancomycin published in 2015. Methods Our updated recommendations complied with standards for developing trustworthy guidelines, including timeliness and rigor of the updating process, as well as the use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. We also followed the methodology handbook published by the National Institute for Health and Clinical Excellence and the Spanish National Health System. Results We partially updated the 2015 guideline. Apart from adults, the updated guideline also focuses on pediatric patients and neonates requiring intravenous vancomycin therapy. The guideline recommendations involve a broadened range of patients requiring TDM, modified index of TDM (both 24-hour area under the curve and trough concentration), addition regarding the necessity and timing of repeated TDM, and initial dose for specific subpopulations. Overall, 1 recommendation was deleted and 3 recommendations were modified. Eleven new recommendations were added, and no recommendation was made for 2 clinical questions. Conclusions We updated an evidence-based guideline regarding the TDM of vancomycin using a rigorous and multidisciplinary approach. The updated guideline provides more comprehensive recommendations to inform rational and optimized vancomycin use and is thus of greater applicability.

To investigate the risk factors associated with the development of thrombocytopenia, and define the thresholds of efficacy and safety in critically ill patients who received linezolid therapy. A retrospective study was performed in critically ill patients treated with linezolid. Risk factors associated with thrombocytopenia were identified via medical records and trough levels (C(min)) measured during linezolid treatment. By establishing a logistic model, the risks were predicted by the receiver operating characteristic (ROC) curve and the thresholds of efficacy and safety were identified in the patients. Logistic analysis showed that, weight (OR = 0.906; 95% CI, 0.839-0.978; P = 0.011), baseline platelet count (OR = 0.989; 95% CI, 0.977-1.000; P = 0.049), C(min) (OR = 1.545; 95% CI, 1.203-1.983; P = 0.001), and APACHE II score (OR = 1.130; 95% CI, 1.003-1.273; P = 0.044) were significant factors for linezolid-associated thrombocytopenia. The area under the ROC curve of the combined predictor was larger based on the above factors. When the Youden index was the maximum, the best optimal cut-off point was 205.6 on the ROC curve; when C(min) ≥ 2 mg/L, the probability of bacterial eradication was more than 80%; when C(min) ≥ 6.3 mg/L, the probability of thrombocytopenia was more than 50 %. In clinical practice, when the calculating results of the combined predictor ≤205.6, the risk of the development of thrombocytopenia may be higher. Furthermore, maintenance of C(min) between 2 and 6.3 mg/L over time may be helpful in retaining appropriate efficacy and reducing the associated thrombocytopenia.

We sought to describe the population pharmacokinetics of tigecycline in critically ill patients and to determine optimized dosing regimens of tigecycline for different bacterial infections. This prospective study included 10 critically ill patients given a standard dose of tigecycline. Blood samples were collected during one dosing interval and were analyzed using validated chromatography. Population pharmacokinetics and Monte Carlo dosing simulations were undertaken using Pmetrics. Three target exposures, expressed as ratios of the 24-h area under the curve to MICs (AUC 0 -24 /MIC), were evaluated (Ն17.9 for skin infections, Ն6.96 for intraabdominal infections, Ն4.5 for hospital-acquired pneumonia). The median age, total body weight, and body mass index (BMI) were 67 years, 69.1 kg, and 24.7 kg/m 2 , respectively. A two-compartment linear model best described the time course of tigecycline concentrations. The parameter estimates (expressed as means Ϯ standard deviations [SD]) from the final model were as follows: clearance (CL), 7.50 Ϯ 1.11 liters/h; volume in the central compartment, 72.50 Ϯ 21.18 liters; rate constant for tigecycline distribution from the central to the peripheral compartment, 0.31 Ϯ 0.16 h Ϫ1 ; and rate constant for tigecycline distribution from the peripheral to the central compartment, 0.29 Ϯ 0.30 h Ϫ1 . A larger BMI was associated with increased CL of tigecycline. Licensed doses were found to be sufficient for Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and methicillin-resistant Staphylococcus aureus for an AUC 0 -24 /MIC target of 4.5 or 6.96. For a therapeutic target of 17.9, an increased tigecycline dose is required, especially for patients with higher BMI. The dosing requirements of tigecycline differ with the indication, with pathogen susceptibility, and potentially with patient BMI.

Male breast cancer (MBC) is rare, and most patients are diagnosed at an advanced stage. We aimed to develop a reliable nomogram to predict breast cancer-specific survival (BCSS) for MBC patients, thus helping clinical diagnosis and treatment. Based on data from the Surveillance, Epidemiology, and End Results (SEER) database, 2,451 patients diagnosed with MBC from 2010 to 2015 were selected for this study. They were randomly assigned to either a training cohort ( n = 1715) or a validation cohort ( n = 736). The Multivariate Cox proportional hazards regression analysis was used to determine the independent prognostic factors, which were then utilized to build a nomogram for predicting 3- and 5-year BCSS. The discrimination and calibration of the new model was evaluated using the Concordance index (C-index) and calibration curves, while its accuracy and benefits were assessed by comparing it to the traditional AJCC staging system using the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and the decision curve analysis (DCA). Multivariate models revealed that age, AJCC stage, ER status, PR status, and surgery all showed a significant association with BCSS. A nomogram based on these variables was constructed to predict survival in MBC patients. Compared to the AJCC stage, the C-index (training group: 0.840 vs. 0.775, validation group: 0.818 vs. 0.768), the areas under the receiver operating characteristic curve of the training set (3-year AUC: 0.852 vs. 0.778, 5-year AUC: 0.841 vs. 0.774) and the validation set (3-year AUC: 0.778 vs. 0.752, 5-year AUC: 0.852 vs. 0.794), and the calibration plots of this model all exhibited better performance. Additionally, the NRI and IDI confirmed that the nomogram was a great prognosis tool. Finally, the 3- and 5-year DCA curves yielded larger net benefits than the traditional AJCC stage. In conclusion, we have successfully established an effective nomogram to predict BCSS in MBC patients, which can assist clinicians in determining the appropriate therapy strategies for individual male patients.

No abstract

Multidrug resistance and tumor migration and invasion are the major obstacles to effective breast cancer chemotherapy, but the underlying molecular mechanisms remain unclear. This study investigated the potential of transgelin 2 and salvianolic acid A to modulate the resistance and the migration and invasion abilities of paclitaxel-resistant human breast cancer cells (MCF-7/PTX). MCF-7/PTX cells were found to exhibit not only a high degree of resistance to paclitaxel, but also strong migration and invasion abilities. Small interfering RNA-mediated knockdown of TAGLN2 sensitized the MCF-7/PTX cells to paclitaxel, and inhibited their migration and invasion abilities. In addition, we also observed that combined salvianolic acid A and paclitaxel treatment could reverse paclitaxel resistance, markedly inhibit tumor migration and invasion, and suppress the expression of transgelin 2 in MCF-7/PTX cells. These findings indicate that salvianolic acid A can reverse the paclitaxel resistance and inhibit the migration and invasion abilities of human breast cancer cells by down-regulating the expression of transgelin 2, and hence could be useful in breast cancer treatments.