Plasma and cerebrospinal fluid population pharmacokinetic modeling and simulation of meropenem after intravenous and intrathecal administration in postoperative neurosurgical patients

Abstract

Combined intravenous and local intrathecal administration of meropenem in patients after craniotomy is widely used to treat intracranial infections. However, the optimal dosing regimen of meropenem has not been investigated, posing a risk to treatment efficacy. We aimed to identify significant factors associated with inter-individual variability in cerebrospinal fluid (CSF) pharmacokinetics of meropenem and to evaluate potential intravenous and intrathecal meropenem dosing regimens for the treatment of patients with intracranial infections. After the diagnosis of intracranial infection, 15 patients with an indwelling drain tube received intravenous and intrathecal administration of meropenem. Blood and cerebrospinal fluid (CSF) samples were obtained at the scheduled time to measure meropenem concentration. Plasma and CSF concentration-time data were fit simultaneously using a nonlinear mixed-effects modeling approach. A 3-compartmental model was selected to characterize the in vivo behavior of meropenem. Through population modeling, multiple covariates were tested about their impact on the meropenem pharmacokinetics. Considering CSF outflow via drain tube leading to a drug loss, the drug clearance in CSF (CLCSF) was added to describe this drug loss. The covariate selection indicated that the drainage volume (mL/d) had a significant positive correlation with CLCSF. Bootstrap and visual predictive check suggested a robust and reliable pharmacokinetic model was structured. The established final population model was useful to apply with simulation to identify meropenem dosing regimens for the treatment of patients with intracranial infections. With the goal of CSF concentrations exceeding the minimum inhibitory concentration during the therapy, we created a simple to use dosage regimen table to guide clinicians with drug dosing.