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.

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 for using a drug repositioning strategy. Method: 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.