Simulated drug disposition in critically ill patients to evaluate effective PK/PD targets for combating Pseudomonas aeruginosa resistance to meropenem.

Bacterial infections, including those caused by Pseudomonas aeruginosa, often lead to sepsis, necessitating effective antibiotic treatment like carbapenems. The key pharmacokinetic/pharmacodynamic (PK/PD) index correlated to carbapenem efficacy is the fraction time of unbound plasma concentration above the minimum inhibitory concentration (MIC) of the pathogen (%fT > MIC). While multiple targets exist, determining the most effective one for critically ill patients remains a matter of debate. This study evaluated meropenem's bactericidal potency and its ability to combat drug resistance in Pseudomonas aeruginosa under three representative PK/PD targets: 40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC. The hollow fiber infection model (HFIM) was constructed, validated, and subsequently inoculated with a substantial Pseudomonas aeruginosa load (1 × 108 CFU/mL). Different meropenem regimens were administered to achieve the specified PK/PD targets. At specified intervals, samples were collected from the HFIM system and subjected to centrifugation. The resulting supernatant was utilized to determine drug concentrations, while the precipitates were used to track changes in both total and drug-resistant bacterial populations over time by the spread plate method. The HFIM accurately reproduced meropenem's pharmacokinetics in critically ill patients. All three PK/PD target groups exhibited a rapid bactericidal response within 6 h of the initial treatment. However, the 40% fT > MIC and 100% fT > MIC groups subsequently showed bacterial resurgence and resistance, whereas the 100% fT > 4× MIC group displayed sustained bactericidal activity with no evidence of drug resistance. The HFIM system revealed that maintaining 100% fT > 4× MIC offers a desirable microbiological response for critically ill patients, demonstrating strong bactericidal capacity and effective prevention of drug resistance.

Physiologically based pharmacokinetic modelling of cefoperazone in paediatrics.

AIMS: Cefoperazone is commonly used off-label in the treatment of bacterial meningitis and sepsis in children, and the pharmacokinetic (PK) data are limited in this vulnerable population. The goal of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict pediatric cefoperazone exposure for rational dosing recommendations. METHODS: A cefoperazone PBPK model for adults was first constructed using Simcyp V22 simulator. Subsequently, the model was extended to children based on the built in age-dependent physiological parameters, while the drug characteristics remained unchanged. The verified pediatric PBPK model was then utilized to assess the rationality of the common dosing regimens for children at different age groups. RESULTS: Cefoperazone PBPK model included elimination via biliary excretion, glomerular filtration, and organic anion transporter 3 (OAT3)-mediated tubular secretion. 95.2% of the observed mean concentrations and 100% of the area under the plasma drug concentration-time curve (AUC) and peak concentration (Cmax) in adults were within a twofold range of model mean predictions. Good predictive accuracy was also observed in children, including neonates. 50 mg/kg q12h cefoperazone demonstrated effective target attainment in virtual term neonates (<1 month) when the MIC was ≤1 mg/L, adhering to the stringent PK/PD target of 75% fT > MIC. 37.5 mg/kg q12h cefoperazone achieved the common 50% fT > MIC target for an MIC ≤ 0. 25 mg/L in virtual pediatric patients ranging from 1 month to 18 years of age. CONCLUSIONS: A pediatric PBPK model was developed for cefoperazone, and it could serve as the basis for deriving rational dosing regimens in children.

Evaluation of Various Approaches to Estimate Transplacental Clearance of Vancomycin for Predicting Fetal Concentrations using a Maternal-Fetal Physiologically Based Pharmacokinetic Model.

BACKGROUND: Evaluating drug transplacental clearance is vital for forecasting fetal drug exposure. Ex vivo human placenta perfusion experiments are the most suitable approach for this assessment. Various in silico methods are also proposed. This study aims to compare these prediction methods for drug transplacental clearance, focusing on the large molecular weight drug vancomycin (1449.3 g/mol), using maternal-fetal physiologically based pharmacokinetic (m-f PBPK) modeling. METHODS: Ex vivo human placenta perfusion experiments, in silico approaches using intestinal permeability as a substitute (quantitative structure property relationship (QSPR) model and Caco-2 permeability in vitro-in vivo correlation model) and midazolam calibration model with Caco-2 scaling were assessed for determining the transplacental clearance (CLPD) of vancomycin. The m-f PBPK model was developed stepwise using Simcyp, incorporating the determined CLPD values as a crucial input parameter for transplacental kinetics. RESULTS: The developed PBPK model of vancomycin for non-pregnant adults demonstrated excellent predictive performance. By incorporating the CLPD parameterization derived from ex vivo human placenta perfusion experiments, the extrapolated m-f PBPK model consistently predicted maternal and fetal concentrations of vancomycin across diverse doses and distinct gestational ages. However, when the CLPD parameter was derived from alternative prediction methods, none of the extrapolated maternal-fetal PBPK models produced fetal predictions in line with the observed data. CONCLUSION: Our study showcased that combination of ex vivo human placenta perfusion experiments and m-f PBPK model has the capability to predict fetal exposure for the large molecular weight drug vancomycin, whereas other in silico approaches failed to achieve the same level of accuracy.

Development and validation of a UPLC-PDA method for quantifying ceftazidime in dried blood spots.

Bacterial infection is a leading cause of neonatal death. Ceftazidime, commonly used for neonatal infections, is often used off-label. Blood sampling limits pharmacokinetic (PK) studies in neonatal patients. The dried blood spots (DBS) are a potential matrix for microsampling. Herein, we describe an ultra-performance liquid chromatography with a photodiode array (UPLC-PDA) to determine ceftazidime in DBS from neonatal patients in support of pharmacokinetic studies. The Capitainer® device-based DBS samples containing 10 µL blood were extracted in 70% methanol/water (v/v) with acetaminophen as the internal standard (IS). The extraction process was carried out at 20 °C using a block bath shaker at 1000 rpm for 30 min. The extracted ceftazidime was subsequently eluted through an Acquity UPLC HSS T3 column (2.1 × 50 mm, 1.8 µm). Elution was achieved using a water (containing 0.1% trifluoroacetic acid)/acetonitrile linear gradient at a flow rate of 0.5 mL/min, and the analytical time was 3.2 min. The PDA detection wavelength was set at 259 nm. The method underwent thorough validation following the recommendation of the European Bioanalysis Forum (EBF) and the bioanalytical guideline established by the European Medicines Agency (EMA). No interfering peaks were detected at the retention times of ceftazidime and IS. The ceftazidime exhibited a quantification range spanning from 0.5 to 200 µg/mL, and the assay demonstrated good accuracy (intra/inter-assay ranging from 90.1% to 104.8%) and precision (intra/inter-assay coefficient of variations ranging from 4.8% to 11.7%). The method's applicability was demonstrated by analyzing clinical DBS samples collected from neonatal patients.

Potential prognosis and immunotherapy predictor TFAP2A in pan-cancer.

BACKGROUND: TFAP2A is critical in regulating the expression of various genes, affecting various biological processes and driving tumorigenesis and tumor development. However, the significance of TFAP2A in carcinogenesis processes remains obscure. METHODS: In our study, we explored multiple databases including TCGA, GTEx, HPA, cBioPortal, TCIA, and other well-established databases for further analysis to expound TFAP2A expression, genetic alternations, and their relationship with the prognosis and cellular signaling network alternations. GO term and KEGG pathway enrichment analysis as well as GSEA were conducted to examine the common functions of TFAP2A. RT-qPCR, Western Blot and Dual Luciferase Reporter assay were employed to perform experimental validation. RESULTS: TFAP2A mRNA expression level was upregulated and its genetic alternations were frequently present in most cancer types. The enrichment analysis results prompted us to investigate the changes in the tumor immune microenvironment further. We discovered that the expression of TFAP2A was significantly associated with the expression of immune checkpoint genes, immune subtypes, ESTIMATE scores, tumor-infiltrating immune cells, and the possible role of TFAP2A in predicting immunotherapy efficacy. In addition, high TFAP2A expression significantly correlated with several ICP genes, and promoted the expression of PD-L1 on mRNA and protein levels through regulating its expression at the transcriptional level. TFAP2A protein level was upregulated in fresh colon tumor tissue samples compared to that in the adjacent normal tissues, which essentially positively correlated with the expression of PD-L1. CONCLUSIONS: Our study suggests that targeting TFAP2A may provide a novel and effective strategy for cancer treatment.