Characterization of Maternal and Neonatal Pharmacokinetic Behavior of Ceftazidime

Abstract

Ceftazidime is a parenteral cephalosporin frequently used in pregnant women for treatment of urinary tract or intrauterine infections. Despite its regular use in pregnant women, ceftazidime disposition in both mother and fetus is not well understood, and a pharmacokinetic (PK) model that allows characterization and simulation of both maternal and preterm neonatal ceftazidime disposition is not available. In this study, 10 pregnant women with suspected or proven intrauterine infections in the late second and early third trimester were treated with 1 g of ceftazidime intravenously every 6 hours. During ceftazidime treatment, one maternal and umbilical cord blood sample was taken at delivery to quantify ceftazidime concentrations in the mother and preterm neonate. Data showed that ceftazidime concentrations in the mother were comparable to those observed in the neonate. Based on these data, a PK model was developed to describe maternal disposition, ceftazidime distribution over the placenta, and elimination in the neonate. The maternal substructure of the model was parameterized according to a previously reported ceftazidime model with minor adjustments to account for pregnancy-related effects on renal elimination of ceftazidime. The expanded population PK model with an additional neonatal compartment was fitted to measured drug concentrations in the neonate. The neonatal elimination rate constant at delivery was close to that estimated for the mother. The presented results show that ceftazidime readily crosses the placenta and indicate that perinatal PK behavior of ceftazidime in preterm neonates can be expected to be similar to those observed in their mothers.

The Use of Pregnancy Physiologically Based Pharmacokinetic Modeling for Renally Cleared Drugs

“…The simulated mean plasma concentration–time profile after both single and multiple dose administration of 1 g of ceftazidime in pregnant women in their third trimester were compared with the mean clinical data published by Dallmann et al 14 . The mean concentrations are in general within the simulated 5th and 95th percentile interval (Figure 2b).…”

“…The simulated mean plasma concentration-time profile after both single and multiple dose administration of 1 g of ceftazidime in pregnant women in their third trimester were compared with the mean clinical data published by Dallmann et al 14 The mean concentrations are in general within the simulated 5th and 95th percentile interval (Figure 2b). Simulations show a predicted 27% decrease in AUC in the third trimester of pregnancy, which is similar to the decrease in observed steady-state concentration reported in the literature 13 (Table 2).…”

An Application of a Physiologically Based Pharmacokinetic Approach to Predict Ceftazidime Pharmacokinetics in a Pregnant Population

“…The slight underprediction of ceftazidime exposure in the simulated subjects compared with Dallmann’s study [ 43 ] is probably because the observations stemmed from pregnant subjects under mechanical ventilation. A previous study found that mechanical ventilation decreases ceftazidime renal clearance and increases its volume of distribution [ 49 ].…”

Leveraging Predictive Pharmacometrics-Based Algorithms to Enhance Perinatal Care—Application to Neonatal Jaundice

“…As perinatal medicine is undergoing a fundamental change transforming towards a modern data-driven patient-oriented approach new tools that will predict the dynamics of biomarkers for an individual fetus or newborn will become increasingly important in maternal, neonatal and perinatal care. We developed PMX-based algorithms to optimize and individualize dosing of therapeutics in the field of perinatal medicine ( Wilbaux et al, 2016a ; Wilbaux et al, 2016b ; Koch et al, 2017 ; Nekka et al, 2017 ; van Donge et al, 2018 ; Dallmann et al, 2019 ; van Donge et al, 2019 ; Wilbaux et al, 2019 ; van Donge et al, 2020a ; van Donge et al, 2020b ; Koch et al, 2020c ; Dao et al, 2020 ; Samiee-Zafarghandy et al, 2022 ). It is time to go beyond classical pharmacological applications and develop algorithms condensing the wealth of clinical data and physiology knowledge into predictive tools coping with the dynamics of biomarkers for an individual fetus or neonate.…”