Population pharmacokinetics of intravenous cefotaxime indicates that higher doses are required for critically ill children.

Background: Cefotaxime is frequently used in critically ill children, however pharmacokinetic (PK) studies to support adequate dosing in this patient population are limited.

Objectives: To characterize cefotaxime PK in critically ill children and evaluate exposures achieved by current and alternative dosing regimens.

Methods: Children (0-18 years) admitted to the paediatric ICU, receiving intravenous cefotaxime (100-150 mg/kg/day, interval 6-8 h) were included (Clinicaltrials.

Midazolam Infusion and Disease Severity Affect the Level of Sedation in Children: A Parametric Time-to-Event Analysis.

Aim: In critically ill mechanically ventilated children, midazolam is used first line for sedation, however its exact sedative effects have been difficult to quantify. In this analysis, we use parametric time-to-event (PTTE) analysis to quantify the effects of midazolam in critically ill children.

Methods: In the PTTE analysis, data was analyzed from a published study in mechanically ventilated children in which blinded midazolam or placebo infusions were administered during a sedation interruption phase until, based on COMFORT-B and NISS scores, patients became undersedated and unblinded midazolam was restarted.

Current Ceftriaxone Dose Recommendations are Adequate for Most Critically Ill Children: Results of a Population Pharmacokinetic Modeling and Simulation Study.

Background And Objective: Ceftriaxone is a cornerstone antibiotic for critically ill children with severe infections. Despite its widespread use, information on the pharmacokinetics of ceftriaxone is lacking in this population. We aimed to determine ceftriaxone pharmacokinetics in critically ill children and to propose ceftriaxone dosing guidelines resulting in adequate target attainment using population pharmacokinetic modeling and simulation.

Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling.

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome.

An HPLC-UV method for determining plasma dimethylacetamide concentrations in patients receiving intravenous busulfan.

Dimethylacetamide (DMA) is a solvent used in the preparation of intravenous busulfan, an alkylating agent used in blood or marrow transplantation. DMA may contribute to hepatic toxicity, so it is important to monitor its clearance. The aim of this study was to develop an HPLC-UV assay for measurement of DMA in human plasma.