1 Introduction
Ciprofloxacin is a commonly used antibiotic for treating infections in intensive care unit (ICU) patients given its broad spectrum of action against pathogenic bacteria including most Gram-negative species [
1‐
3]. However, adequate dosing of ciprofloxacin remains a major challenge in these patients. The ICU patient population exhibits high pharmacokinetic (PK) variability between patients and over the time course of disease and therapy owing to the large and variable physiological changes associated with the type and severity of illness as well as therapy [
4]. For example, the clearance (CL) of antibiotics may be decreased by acute kidney injury whereas the volume of distribution may be increased by capillary leakage and administration of intravenous fluids [
5]. Thus, prescribing appropriate antibiotic doses for ICU patients is challenging for clinicians. This needs to be addressed as underdosing may give rise to treatment failure in at least some ICU patients but also to long-term increased antibiotic resistance [
6].
Population PK models are increasingly being used to optimize and clinically guide antibiotic dose regimens. This is also the case for ciprofloxacin. Several studies have been performed characterizing ciprofloxacin population pharmacokinetics in ICU patients [
7‐
13]. In theory, given sufficient data that includes all relevant model covariates, a well-developed model should be capable of accurately describing the entire patient population of the training data, i.e., the data used for the model development. However, considerable deviation can be observed in the reported population PK parameter estimates [
6,
8‐
12,
14,
15]. Such results do not only highlight the high PK variability of ciprofloxacin in the ICU patient population, but also suggest that an important part of the variability may still be unexplained in the ICU patient population. As population PK models are often used to recommend dosing regimens, the use of a model that is not able to adequately describe the majority of patients to be treated is likely to lead to poor PK predictions and thus selection of suboptimal dose regimens. Therefore, it is important to further study the pharmacokinetics of ciprofloxacin in a larger population of ICU patients. To this end, we aimed to combine individual patient data from three previous studies to develop a pooled population PK model of ciprofloxacin representative for a large ICU population and to investigate the PK differences between studies.
4 Discussion
In this study, we performed a pooled population PK analysis of ciprofloxacin in a large cohort of ICU patients describing the data of three studies. As we developed the model based on a pooled data set consisting of individual patient data from multiple studies representing a larger sample of the ICU population, the estimated IIVs were overall higher compared with the original studies of study 1 and study 2 [
7,
8]. The fact that substantial unexplained IIV and IOV were still present in the final model with only one covariate identified, despite the large size of the data set, indicates that ciprofloxacin pharmacokinetics is not yet comprehensively understood in the ICU patient population and remains difficult to predict on the individual level. The observed PK differences between the three studies in the post hoc analysis also illustrated that there are likely still factors to be identified that can explain the PK variability between studies and individuals.
Apart from the allometric scaling of body weight, the only covariate association identified was eGFR on CL. Expectedly, renal function-related variables should be strong predictors of CL, as ciprofloxacin is weakly bound to protein and the majority of the ciprofloxacin molecules in serum is eliminated through the kidneys [
31,
32]. Notably, the original report of study 1 did not identify any covariate associations. Mathematically, the likelihood of a candidate covariate model is a composite of the likelihoods as contributed by the data of each study. In the covariate selection procedure, the data of study 1 might mask a weak or modest covariate effect that was more abundantly present in the data of study 2 and 3. It is worth mentioning that we purposely did not evaluate “study” as a covariate. This variable provides rather limited insight into how patients differ in the three studies, which should in principle be explained by differences in patients’ actual characteristics. Moreover, “study” being a covariate would have drastically diminished the model’s external applicability because such a model would be impossible to externally validate and use in any other clinical setting.
While the final model could describe the data of the three studies as a whole, we revealed that there was still some PK variability unexplained between studies (Table
3, Fig.
3). The largest variability was observed in CL for both the posterior mean and the posterior IIV. This may be attributable to the studied patient population. Study 3 included patients with severe sepsis and septic shock. Consequently, a high percentage of septic patients were included in that study, with 55% of patients fulfilling the criteria for septic shock. Patients with sepsis have an increased risk of elevated drug exposure owing to the derangement of renal and hepatic functions on which the elimination of ciprofloxacin mainly relies [
33]. This may be indicated by the estimated posterior mean of CL,
V1, and
V2, which were all smaller for the patients of study 3 compared with the patients of study 1 and 2 (Table
3). In addition, as we previously mentioned, there is a deviation in the reported PK parameters in the published studies [
6,
8‐
12,
14,
15]. The parameters’ estimates of our model differ from previously published models to varying extents as well. For instance, the most clinically relevant parameter CL is associated with a difference of up to 38.1% from the values of previously published two-compartment models, while for
V1,
Q, and
V2 the differences may be as high as more than 200%. This could be due to the differences in the characteristics of the studied populations, for example, in underlying condition or disease severity. In comparison to a study in which a relatively similar population was studied, our results are in agreement with theirs, resulting in a difference in CL of below 5% [
12]. However, we still observed large differences in PK parameters despite the studied patient population of our study being comparable to that of the published patient population [
13]. Such a difference may be because of the differences that exist between the investigated populations that were not identified as a result of limitations in the collected data on patient characteristics and thus covariates analyzed in the respective studies, which we further discuss below. This is perhaps partially evidenced by the unreduced IIV of this study as well, despite a larger data set used for the analysis. It is also conceivable that the parameterization of a model can have an impact on the estimates of PK parameters. Nevertheless, the large remaining IIV suggests an insufficient knowledge on the ciprofloxacin pharmacokinetics in ICU patients because of the highly variable nature of this population. It is thus pivotal for clinical professionals to validate and perhaps calibrate an external model before the implementation in a clinical setting.
According to the simulation results, given the same dose regimen, the AUC
24 and the PTA of ciprofloxacin differed between studies but mostly between study 3 and the other two (Fig.
4). When the MIC was equal to or greater than 0.125 mg/L, the PTA started to differentiate between studies. Such differences could be clinically relevant at an MIC of 0.25 mg/L. Because the PTA at a MIC of 0.5 mg/L was low for patients in the three studies, the need for a ciprofloxacin dose regimen higher than 400 mg t.i.d. is indicated for higher PTA if microorganisms are to be treated with such an expected MIC. This was in accordance with the findings of the original studies of both study 1 and 2 [
7,
8]. However, a breakpoint of 0.5 mg/L is only applicable when treating
P. aeruginosa caused infection for which a higher dose is required (≥ 1200 mg/day). Nevertheless, it may be difficult to determine a generally applicable dose regimen owing to the large unexplained existent PK variability between studies. Meanwhile, dosing strategies probably need to be tailored per treatment center likely because of the different subtypes of patients. This highlights that in order to adequately capture the PK variability between ICU patients, we may need better instead of larger datasets including a more diverse and detailed set of covariates. The primary reason for ICU admission may be associated with the pharmacokinetics of ciprofloxacin where, for example, trauma may be related to augmented renal CL while cardiosurgical patients might often have poor organ function. The underlying condition such as comorbidity scores and life expectancy reflecting the health status of a patient can probably also help to explain a fraction of PK variability. Local treatment policies may have an impact on the PK variability as well, for example, the intensity and duration of ventilation, types and doses of fluid resuscitation including total parenteral nutrition, and the doses and duration of the use of inotropes and their changes over time. A number of these factors have been previously identified as covariates for other antibiotics in ICU patients [
34‐
37]. In addition, the immune response, for example, C-reactive protein, has also been shown to influence pharmacokinetics [
38]. Such covariates are undoubtedly of interest to be collected and tested in future studies; however, these were unfortunately not available in the current pooled data analysis, which is an important limitation of this study.
Published models of ciprofloxacin are to a large extent similar in terms of identified covariates [
8,
11‐
15]. The most commonly found are body weight and renal function. We also observed this in other commonly used antibiotics such as vancomycin and meropenem [
39,
40]. Undoubtedly, the body weight and renal function reflect a large portion of the PK variability. From a clinical perspective, however, there are likely additional factors that are explanatory for the PK variability between ICU patients, such as those mentioned earlier. We showed in this study that despite a larger dataset, the resulting model is not necessarily more elucidative, which may also explain the differences in the reported PK parameters of published studies and this study, as there may be differences in the distribution of yet unknown but potentially influential covariates between studies. This raises our concern on the covariates identifiability through conventional PK modeling approaches. As the traditional compartmental models represent the biological system with a high level of abstraction at the cost of omitting much detail, we may not be able to identify the covariates that are carried in granular clinical data. Dosing may alternatively be optimized through model-based therapeutic drug monitoring where individual dose advice can be produced using the individual PK parameters.
Acknowledgements
We thank Ronald Driessen for kindly helping us with data extraction.
The Dutch Antibiotic PK/PD Collaborators: Luca F. Roggeveen, Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. Lucas M. Fleuren, Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. Nicole G. M. Hunfeld, Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands. Tim M.J. Ewoldt, Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands. Anouk E. Muller, Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Medical Microbiology, Haaglanden Medical Center, The Hague, The Netherlands. Annemieke Dijkstra, Department of Intensive Care, Maasstad Hospital, Rotterdam, The Netherlands. Dylan W. de Lange, Department of Intensive Care and National Poisons Information Center, UMC Utrecht, Utrecht, The Netherlands. Emilie Gieling, Department of Pharmacy, Radboud UMC, Nijmegen, The Netherlands; Department of Pharmacy, UMC Utrecht, Utrecht, The Netherlands. Peter Pickkers, Department of Intensive Care, Radboud UMC, Nijmegen, The Netherlands. Jaap ten Oever, Radboud Center for Infectious Diseases, Radboud UMC, Nijmegen, The Netherlands.