Pooled Population Pharmacokinetic Analysis for Exploring Ciprofloxacin Pharmacokinetic Variability in Intensive Care Patients

Abstract

Background and Objective Previous pharmacokinetic (PK) studies of ciprofloxacin in intensive care (ICU) patients have shown large differences in estimated PK parameters, suggesting that further investigation is needed for this population. Hence, we performed a pooled population PK analysis of ciprofloxacin after intravenous administration using individual patient data from three studies. Additionally, we studied the PK differences between these studies through a post-hoc analysis. Methods Individual patient data from three studies (study 1, 2, and 3) were pooled. The pooled data set consisted of 1094 ciprofloxacin concentration-time data points from 140 ICU patients. Nonlinear mixed-effects modeling was used to develop a population PK model. Covariates were selected following a stepwise covariate modeling procedure. To analyze PK differences between the three original studies, random samples were drawn from the posterior distribution of individual PK parameters. These samples were used for a simulation study comparing PK exposure and the percentage of target attainment between patients of these studies. Results A two-compartment model with first-order elimination best described the data. Inter-individual variability was added to the clearance, central volume, and peripheral volume. Inter-occasion variability was added to clearance only. Body weight was added to all parameters allometrically. Estimated glomerular filtration rate on ciprofloxacin clearance was identified as the only covariate relationship resulting in a drop in inter-individual variability of clearance from 58.7 to 47.2%. In the post-hoc analysis, clearance showed the highest deviation between the three studies with a coefficient of variation of 14.3% for posterior mean and 24.1% for posterior inter-individual variability. The simulation study showed that following the same dose regimen of 400 mg three times daily, the area under the concentration-time curve of study 3 was the highest with a mean area under the concentration-time curve at 24 h of 58 mg•h/L compared with that of 47.7 mg•h/L for study 1 and 47.6 mg•h/L for study 2. Similar differences were also observed in the percentage of target attainment, defined as the ratio of area under the concentration-time curve at 24 h and the minimum inhibitory concentration. At the epidemiological cut-off minimum inhibitory concentration of Pseudomonas aeruginosa of 0.5 mg/L, percentage of target attainment was only 21%, 18%, and 38% for study 1, 2, and 3, respectively. Conclusions We developed a population PK model of ciprofloxacin in ICU patients using pooled data of individual patients from three studies. A simple ciprofloxacin dose recommendation for the entire ICU population remains challenging owing to the PK differences within ICU patients, hence dose individualization may be needed for the optimization of ciprofloxacin treatment.The members of the Dutch Antibiotic PK/PD are listed in acknowledgements section.

The effect of the acute phase of infection on absorption of and exposure to orally administered antibiotics in non-critically ill, hospitalized patients

“…When comparing our PPK modelling results with those of studies performed in critically ill 21 , 24 , 29 , 30 and burn patients, 31 CL CR was likewise associated with CL of both amoxicillin 29 , 31 and ciprofloxacin. 21 , 24 , 30 The IIV of CL was lower in our study population, suggesting that exposure is more predictable in non-critically ill patients, also in the acute phase of infection. Therefore, the acute phase of infection only had a marginal effect on the pharmacokinetics of the investigated antibiotics in non-critically ill patients.…”

Real-Time Monitoring of Antibiotics in the Critically Ill Using Biosensors

“…The mathematical models commonly used in MIPD software, for example, do not always capture the range of pharmacokinetics and pharmacodynamics seen across heterogeneous critically ill populations. Numerous studies have highlighted the difficulty that such models have in accurately predicting the pharmacokinetics in critically ill patients for multiple antibiotics, including vancomycin [5,6], amikacin [4], gentamicin, tobramycin [7], meropenem [8], ciprofloxacin [9], cefotaxime [10], and polymyxin [11]. This problem is exacerbated in critically ill patient groups, such as pediatrics or those undergoing renal replacement therapies, whose pharmacokinetic profiles are distinct from those of more typical patients.…”

Unraveling the Effects of Acute Inflammation on Pharmacokinetics: A Model-Based Analysis Focusing on Renal Glomerular Filtration Rate and Cytochrome P450 3A4-Mediated Metabolism

“…Acute severe inflammation can alter drug exposure [ 1 , 2 ] in patients and thereby potentially impact drug efficacy and toxicity, especially for drugs with narrow therapeutic windows, e.g., antimicrobials [ 3 ] and immunosuppressants [ 4 ]. Studies in critically ill or septic patients, where acute inflammation is often present, show large inter-individual variability [ 5 ] in pharmacokinetics, e.g., clearance and distribution volume [ 6 ]. Several clinical pharmacokinetics studies investigating patient populations suffering from acute inflammation by empirically employing population pharmacokinetics modeling approaches have found that increased levels of inflammatory biomarkers such as C-reactive protein (CRP) or interleukin-6 (IL-6) may contribute to the observed variability in pharmacokinetics [ 7 ].…”