Population pharmacokinetics and dosing optimization of mezlocillin in neonates and young infants.

OBJECTIVES: Mezlocillin is used in the treatment of neonatal infectious diseases. However, due to the absence of population pharmacokinetic studies in neonates and young infants, dosing regimens differ considerably in clinical practice. Hence, this study aimed to describe the pharmacokinetic characteristics of mezlocillin in neonates and young infants, and propose the optimal dosing regimen based on the population pharmacokinetic model of mezlocillin. METHODS: A prospective, open-label pharmacokinetic study of mezlocillin was carried out in newborns. Blood samples were collected using an opportunistic sampling method. HPLC was used to measure the plasma drug concentrations. A population pharmacokinetic model was developed using NONMEM software. RESULTS: Ninety-five blood samples from 48 neonates and young infants were included. The ranges of postmenstrual age and birth weight were 29-40 weeks and 1200-4000 g, respectively, including term and preterm infants. A two-compartment model with first-order elimination was developed to describe the population pharmacokinetics of mezlocillin. Postmenstrual age, current weight and serum creatinine concentration were the most important covariates. Monte Carlo simulation results indicated that the current dose of 50 mg/kg q12h resulted in 89.2% of patients achieving the therapeutic target, when the MIC of 4 mg/L was used as the breakpoint. When increasing the dosing frequency to q8h, a dose of 20 mg/kg resulted in 74.3% of patients achieving the therapeutic target. CONCLUSIONS: A population pharmacokinetic model of mezlocillin in neonates and young infants was established. Optimal dosing regimens based on this model were provided for use in neonatal infections.

The impact of public health emergencies on hotel demand - Estimation from a new foresight perspective on the COVID-19.

This paper proposes a new foresight approach to estimate the impact of public health emergencies on hotel demand. The forecasting-based influence evaluation consists of four modules: decomposing hotel demand before an emergency, matching each decomposed component to a forecasting model, combining the predictions as the expected demand after the emergency, and estimating the impact by comparing actual demand against that predicted. The method is applied to analyze the impact of COVID-19 on Macao's hotel industry. The empirical results show that: 1) the new approach accurately estimates COVID-19's impact on hotel demand; 2) the seasonal and industry development components contribute significantly to the estimate of expected demand; 3) COVID-19's impact is heterogeneous across hotel services.