External evaluation of the predictive performance of published population pharmacokinetic models of linezolid in adult patients.

OBJECTIVES: Several linezolid population pharmacokinetic (popPK) models have been established to facilitate optimal therapy; however, their extrapolated predictive performance to other clinical sites is unknown. This study aimed to externally evaluate the predictive performance of published pharmacokinetic models of linezolid in adult patients. METHODS: For the evaluation dataset, 150 samples were collected from 70 adult patients (72.9% of which were critically ill) treated with linezolid at our center. Twenty-five published popPK models were identified from PubMed and Embase. Model predictability was evaluated using prediction-based, simulation-based, and Bayesian forecasting-based approaches to assess model predictability. RESULTS: Prediction-based diagnostics found that the prediction error within ±30% (F30) was less than 40% in all models, indicating unsatisfactory predictability. The simulation-based prediction- and variability-corrected visual predictive check and normalized prediction distribution error test indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting with one or two prior observations significantly improved the models' predictive performance. CONCLUSION: The published linezolid popPK models showed insufficient predictive ability. Therefore, their sole use is not recommended, and incorporating therapeutic drug monitoring of linezolid in clinical applications is necessary.

Expression, Significance, and Correlation of Histone Deacetylase 1/RE-1 Silencing Transcription Factor and Neuronal Markers in Glioma.

BACKGROUND: Inducing the differentiation of glioma cells into neuron-like cells may be an effective strategy to combat glioma. The histone deacetylase 1/RE-1 silencing transcription factor (HDAC1/REST) complex regulates the expression of multiple neuronal genes. In this study, we analyzed the presence and significance of this regulatory effect in glioma based on bioinformatics methods. METHODS: The Human Protein Atlas database was used to obtain immunohistochemical staining images. The Gene Expression Profiling Interactive Analysis and Chinese Glioma Genome Atlas databases were used to analyze the expression of HDAC1/REST and neuronal markers in glioma, their effects on survival, and the association between HDAC1/REST and the expression of neuronal markers and stem cell markers. The differentially expressed genes between the high and low HDAC1/REST groups were explored. The Database for Annotation, Visualization and Integrated Discovery database was used for gene ontology and kyoto encyclopedia of genes and genomes pathway enrichment analysis. RESULTS: The results showed that the expression of HDAC1 and REST increased with the grade of glioma, while the expression of neuronal markers decreased with the grade of glioma. High expression of HDAC1/REST and low expression of neuronal markers were associated with poor prognosis. HDAC1/REST expression was negatively correlated with the expression of neuronal markers, and positively correlated with the expression of neural stem cell markers. The genes up-regulated in the high HDAC1/REST group were mainly related to extracellular matrix and inflammation, and the down-regulated genes were mainly related to synapsis. CONCLUSIONS: This study suggested that HDAC1/REST may be involved in maintaining the malignant phenotype of glioma cells and the stem cell status of glioma stem cells by inhibiting the expression of neuronal markers, which promote the progression of glioma.

Predicting Glioma Cell Differentiation-inducing Drugs Using a Drug Repositioning Strategy.

BACKGROUND: Currently, there are no effective differentiation-inducing agents for gliomas. Drug repositioning is a time-saving, low-risk, and low-cost drug development strategy. In this study, drugs that could induce the differentiation of glioma cells were searched by using a drug repositioning strategy. METHODS: Data mining was used to screen for differentially expressed genes (DEGs). The STRING 11.0 database was used for enrichment analysis. The Connectivity Map database was used for drug screening. The ChEMBL and STITCH databases were used to search for drug targets. The SwissDock database was used for molecular docking. RESULTS: A total of 45 DEGs were identified. The biological processes in which the DEGs were enriched mainly involved nervous system development and the regulation of biological processes. The enriched molecular functions mainly involved transcription-related molecular binding. The enriched cellular components mainly involved membrane-bound organelles and cellular protrusions. The enriched local network clusters mainly involved autophagy, the retinoic acid signalling pathway, and DNA methylation. The drug screening results showed that the drug with the highest score was acenocoumarol. A total of 12 acenocoumarol targets were obtained, among which histone deacetylase 1 (HDAC1) was the target with the highest degree value; the lowest ΔG value for acenocoumarol docked with HDAC1 was -7.52 kcal/mol, which was between those of the HDAC1 inhibitors romidepsin and vorinostat. CONCLUSION: Acenocoumarol may be a potential differentiation-inducing agent for glioma cells.

Parametric population pharmacokinetics of linezolid: A systematic review.

AIMS: Linezolid is often used for the infections caused by drug-resistant Gram-positive bacteria. Recent studies suggest that large between-subject variability (BSV) and within-subject variability could alter drug pharmacokinetics (PK) during linezolid therapy due to pathophysiological changes. This review synthesized information on linezolid population PK studies and summarized the significant covariates that influence linezolid PK. METHODS: A literature search was performed using PubMed, Web of Science and Embase from their inception to 30 September 2021. Published studies were included if they contained data analysing linezolid PK parameters in humans using a population approach with a nonlinear mixed-effects model. RESULTS: Twenty-five studies conducted in adults and five in paediatrics were included. One- and two-compartment models were the commonly used structural models for linezolid. Body size (weight, lean body weight and body surface area), creatinine clearance (CLcr) and age significantly influenced linezolid PK. The median clearance (CL) values (ranges) in infants (0.128 L/h/kg [0.121-0.135]] and children (0.107 L/h/kg [0.088-0.151]] were higher than in adults (0.098 L/h/kg [0.044-0.237]]. For patients with severe renal impairment (CLcr ≤ 30 mL/min), the CL was 37.2% (15.2-55.3%) lower than in patients with normal renal function. CONCLUSION: The optimal linezolid dosage should be adjusted based on the patient's body size, renal function and age. More studies are needed to explore the exact mechanism of linezolid elimination and evaluate the PK characteristics in paediatric patients.