Next Article in Journal
Silver and Gold Compounds as Antibiotics
Next Article in Special Issue
Application of Physiologically Based Pharmacokinetic Model to Delineate the Impact of Aging and Renal Impairment on Ceftazidime Clearance
Previous Article in Journal
Returned Rate and Changed Patterns of Systemic Antibiotic Use in Ambulatory Care in Hungary after the Pandemic—A Longitudinal Ecological Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Antibiotics
Volume 13
Issue 9
10.3390/antibiotics13090849
antibiotics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Precision Dosing of Meropenem in Adults with Normal Renal Function: Insights from a Population Pharmacokinetic and Monte Carlo Simulation Study

by
Yong Kyun Kim
1,
Gaeun Kang
2,
Dae Young Zang
3 and
Dong Hwan Lee
4,*
1
Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14066, Republic of Korea
2
Division of Clinical Pharmacology, Chonnam National University Hospital, Gfwangju 61469, Republic of Korea
3
Division of Hematology-Oncology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14066, Republic of Korea
4
Department of Clinical Pharmacology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14066, Republic of Korea
*
Author to whom correspondence should be addressed.
Antibiotics 2024, 13(9), 849; https://doi.org/10.3390/antibiotics13090849
Submission received: 2 August 2024 / Revised: 2 September 2024 / Accepted: 3 September 2024 / Published: 5 September 2024
(This article belongs to the Special Issue PK/PD Models for Optimizing Antibiotic Use and Avoiding Drug-Drug Interaction)
Browse Figures
Versions Notes

Abstract

:
This study aimed to develop a population pharmacokinetic (PK) model for meropenem in healthy adults and explore optimal dosing regimens for patients with normal renal function. PK samples were obtained from 12 healthy participants, which were analyzed using noncompartmental analysis and nonlinear mixed-effect modeling. The PK profiles of meropenem were characterized using a two-compartment model, and serum creatinine level was identified as a significant covariate affecting total clearance. Monte Carlo simulations were conducted using this model to inform dosing recommendations. The target index for meropenem efficacy was defined as the cumulative percentage over 24 h during which free (f) drug concentration exceeded the minimum inhibitory concentration (MIC) under steady state conditions (fT>MIC). These simulations indicated that the current dosage regimen of 1 g for 30 min infusions every 8 h achieved a 90% probability of target attainment (PTA) for 40%fT>MIC when the MIC was <2 mg/L. However, to achieve more stringent therapeutic targets, such as a 90%PTA for 100%fT>MIC or a 90%PTA for 100%fT>4MIC, higher doses administered as 3 h extended infusions or as continuous infusions may be necessary. These results highlight the need for model-informed precision dosing to enhance the efficacy of meropenem therapy across various MIC levels and therapeutic targets.

1. Introduction

Meropenem belongs to the carbapenem class of β-lactam antibiotics and is distinguished by its activity against a broad spectrum of Gram-positive and Gram-negative bacteria [1,2]. This broad-spectrum activity is important for managing complex infections in intensive care units (ICUs), particularly those caused by multidrug-resistant organisms, which is a challenge in these high-risk settings [3]. Carbapenems disrupt bacterial growth by interacting with penicillin-binding proteins, which are required for cell wall synthesis. Their structure confers resistance to degradation by β-lactamase enzymes, which is a common bacterial resistance mechanism [4]. Because of its resistance to hydrolysis, carbapenems are particularly valuable for treating severe hospital-acquired infections, including those from extended-spectrum beta-lactamase-producing Enterobacterales [3,4].
Although carbapenems are highly effective, their increased use is associated with an increase in resistance among Gram-negative bacteria to these drugs, which highlights the urgent need to administer them prudently to prevent further resistance. Carbapenems rank as the third most frequently administered antibiotics worldwide for community-acquired infections in ICUs. This represents 10.7% of such treatments, and they are the preferred antibiotic for managing nosocomial infections, accounting for 21.5% of all such cases [5]. This significant reliance on carbapenems emphasizes the need for continued efforts to refine their use and ensure their effectiveness against evolving drug-resistant bacteria. The emerging problem of antimicrobial resistance, which is considered a major global public health threat, contributed to approximately 1.27 million fatalities worldwide in 2019 [6]. This issue highlights the importance of optimizing antimicrobial dosing to avoid inadequate treatment outcomes and the development of drug resistance. The concept of “mutant prevention concentration” is fundamental in this context, as insufficient antimicrobial levels enable resistant bacterial populations to flourish [7].
Given the pivotal role of meropenem in the treatment of severe infections, it is critical to understand meropenem’s behavior in different patient populations. Meropenem is primarily excreted unaltered by the kidneys. Studies focusing on precision dosing of meropenem are often done in critically ill patients, who typically exhibit impaired renal function. Consequently, these findings may not be directly applicable to patients with normal or enhanced renal function. Some studies suggest that meropenem dosage should be increased for cases in which renal function is normal or enhanced [8,9,10]. The purpose of the present study was to analyze the population pharmacokinetic (PK) characteristics of meropenem in healthy adults. The data obtained from individuals without confounding factors, such as illness, not only provide standard PK parameters, but also contribute to the development of predictive models for precision dosing when used for subsequent patient-focused research. Another aim of this study was to use this population PK model to conduct Monte Carlo simulations and establish optimal dosing regimens for patients with normal renal function.

2. Results

2.1. Participants

Informed consent was obtained from 14 healthy volunteers; however, two individuals were excluded because of positive results from allergic skin tests. The demographic and clinical characteristics of the remaining participants (four females and eight males) are listed in Table 1. The laboratory characteristics displayed clinically insignificant values. Creatinine clearance (CLCR), which was calculated using the Cockcroft–Gault formula, exhibited values comparable to those estimated using the CKD-EPI formulas for glomerular filtration rate (eGFR). A body surface area (BSA) of 1.73 m2 was used to standardize the CLCR. The eGFR values were adjusted for BSA levels in each patient. No adverse events were reported among the participants administered meropenem.

2.2. Population Pharmacokinetic Analysis

Plasma samples (n = 84) were analyzed to characterize the PK profile of meropenem, which was best described by a two-compartment model. The objective function values (OFVs) for the one-, two-, and three-compartment base models were −43.231, −150.154, and −160.715, respectively. Although the three-compartment model exhibited lower OFVs, it was not selected as the structural model because the clearance was unacceptably low at 0.0138 L/h, and the volume of distribution for the second peripheral compartment was excessively high at 4404 L. In addition, the RSEs for all parameters could not be estimated. Therefore, the two-compartment model was considered the most appropriate structural base model (Table S1). The two-compartment model incorporated total clearance (CL), volume of distribution in the central compartment (V1), volume of distribution for the peripheral compartments (V2), and intercompartmental clearance between V1 and V2 (Q2), as listed in Table 2. Because of the high correlation (0.99) between the variabilities (ω2) of CL and V1, these parameters were coded to share a single η, which was formulated as follows: CL = TVCL × EXP[ETA(1)], and V1 = TVV1 × EXP[THETA(1) × ETA(1)], where TV represents the typical value. THETA(1) was estimated to be 1.59 in the base model (Table S1) and 1.53 in the final model (Table 2). The stepwise covariate selection process is detailed in Table S2. The final PK model, which achieved an OFV of −175.610, identified serum creatinine levels as a significant factor that influences CL (Table 2).
Figure 1 shows the goodness-of-fit plots for the final PK model. It also shows that the distribution of conditional weighted residuals and observed concentrations closely align with the x-axis and the unity line. This indicates minimal bias in the PK parameters and affirms the appropriateness of the model. The individual fit plots for meropenem are provided in Figure S1. Figure S2 shows the visual predictive checks (VPCs) for the meropenem PK model in which the 10th, 50th, and 90th quantiles of the observed concentrations mostly fall within the 95% confidence interval of the simulated data. This underscores the model’s robust predictive accuracy and reliable depiction of the observed concentrations.

2.3. Comparing Noncompartmental Analysis and Population Pharmacokinetics Results

Table 3 lists the findings for the descriptive statistical analysis of the PK parameters for each subject, which were derived from the NCA and population PK analysis. Following the intravenous administration of 500 mg of meropenem, the average (coefficient of variation, CV%) was 40.2 (30.1%) mg/L for Cmax and 40.4 (22.7%) mg·h/L for AUCinf. Comparing the results analyzed using NCA and population PK through the Wilcoxon signed-rank test, the half-lives t1/2λz and t1/2β were not statistically different (p = 0.0712). However, the CLNCA and CL and the VssNCA and Vss showed statistically significant differences (p = 0.048 and 0.002, respectively).

2.4. Dosage Simulation

Figure 2 shows the probability of target attainment (PTA) for the empirical therapy using the current dosage regimen (1 g every 8 h) in patients with normal renal function as determined in the first simulation. This reflects the distribution of meropenem MICs for Pseudomonas aeruginosa as reported by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in 2024 [11]. The target index for meropenem was defined as the cumulative percentage over 24 h during which the free (f) drug concentration exceeded the minimum inhibitory concentration (MIC) under steady state conditions (fT>MIC). The regimen achieved a 90%PTA for 40%fT>MIC when the MIC was below 2 mg/L; however, it failed to reach a 90%PTA for 40%fT>4MIC when the MIC exceeded 1 mg/L. In addition, when the targets were set at 100%fT>MIC or 100%fT>4MIC, the current regimen did not achieve a 90%PTA at any MIC level.
In the second simulation, the optimal dosage regimens were evaluated to achieve a PTA ≥ 90% at 40%fT>MIC, 40%fT>4MIC, 100%fT>MIC, and 100%fT>4MIC (Figure 3). Because 2 g is four times the dose of 0.5 g, the PTA for fT>4MIC at a dose of 2 g overlapped with the PTA for fT>MIC at a dose of 0.5 g. When 1 g of meropenem was infused over 0.5 h every 12 h, the PTA for 40%fT>MIC exceeded 90% when the MIC was at or below 0.5 mg/L. Extending the infusion time to 3 h resulted in a PTA > 90% when the MIC was 2 mg/L and >80% when the MIC was 4 mg/L. For a 1.5 g dose of meropenem infused over 0.5 h every 8 h, the PTA for 40%fT>4MIC exceeded 90% when the MIC was at or below 1 mg/L. Increasing the infusion duration to 3 h enabled the PTA to exceed 90%, even when the MIC was 2 mg/L. When 2 g of meropenem was infused over 0.5 h every 6 h, the PTA for 100%fT>MIC exceeded 90% when the MIC was at or below 0.5 mg/L. Extending the infusion time to 3 h maintained a PTA > 90%, even at an MIC of 2 mg/L; however, even with a 3 h infusion interval of 2 g meropenem every 6 h, the PTA for 100%fT>4MIC did not exceed 90% when the MIC was above 0.5 mg/L.
In the final simulation, the optimal dose regimens for continuous infusion were evaluated to achieve a PTA ≥ 90% for both 100%fT>MIC and 100%fT>4MIC (Figure 4). When 2 g of meropenem was administered as a continuous infusion, the PTA for 100%ft>4MIC exceeded 90% when the MIC was 1 mg/L, and the PTA for 100% ft>MIC also exceeded 90% when the MIC was 4 mg/L. When 8 g of meropenem was administered as a continuous infusion, the PTA for 100%ft>4MIC exceeded 90% when the MIC was 4 mg/L.

3. Discussion

The primary objective in the realm of medical treatment is to restore patients from a diseased to a healthy state. Although some conditions may be irreversible or chronic, others are temporary or reversible, such as renal function. Research on a model-informed precision dosing of antibiotics often focuses on patients with decreased or severely impaired renal function, which sometimes overlooks those with enhanced renal activity. This can result in a form of reverse discrimination in which individuals with normal renal function are inadvertently neglected. This is more likely to be the case with drugs that have been in use for a long time. The rationale for initiating clinical trials with healthy individuals during the drug development process is the ability to establish the effects of the body on a drug and of a drug on the body, without the confounding factors that may be present in patients. This standard data may serve as a benchmark for clinical trials in patients. The methodologies used in developing PK models for precision dosing should also reflect this approach. Models developed exclusively for specific patient groups often show limited predictability. The present study was conducted to establish a model for meropenem with excellent predictive ability using PK data from healthy individuals. Although the ultimate goal was to integrate this model with patient data to enhance therapeutic outcomes, the findings from healthy adults alone have significant value. To date, there has been no population PK study conducted on healthy Korean individuals for meropenem, which indicates the uniqueness and importance of this endeavor.
In the present study, the PK profiles of meropenem were characterized using a two-compartment model in which serum creatinine was a significant covariate affecting clearance (CL). Specifically, CL decreased from 14.3 L/h at a serum creatinine level of 0.6 mg/dL to 11.7 L/h at 1.0 mg/dL. The population PK analysis exhibited typical values for total clearance and the steady state volume of distribution (Vss, V1 + V2) of 12.5 L/h and 12.32 L, respectively (Table 2). The NCA indicated that the mean values of CLNCA and VssNCA, based on a body weight of 70 kg, were 14.07 L/h and 15.44 L, respectively. Moreover, the mean post-hoc estimates for CL and Vss from the population PK analysis, which were also based on a body weight of 70 kg, were 13.79 L/h and 13.79 L, respectively (Table 3). These findings are comparable to those of PK studies conducted in healthy adults (Table 4). In these studies, the average age ranged from 23.6 to 35 years, average weight from 69 to 83 kg, CL from 11.2 to 19.7 L/h, and Vss from 11.7 to 20.6 L. Therefore, our data, when combined with patient data, may serve as a robust baseline for building models with excellent predictive performance.
Li et al. compared the predictive performance of existing meropenem PK models and found that the 18 models were developed using critically ill patients, which exhibited a wide range of CL and Vss values, from 2.18 to 16.7 L/h and 8.30 to 51.2 L, respectively [19]. Many of these studies involved patients on life-support, such as renal replacement therapy or ECMO, which contributed to the variability of the PK parameters. More significantly, the critical condition of these patients is often accompanied by various pathophysiological changes. These changes, whether the result of infection or noninfectious pathological conditions, can affect CL and Vss [20]. For example, increased cardiac output can result in increased clearance, whereas third spacing altered protein binding, or both, can result in an increased volume of distribution. Similarly, renal and hepatic dysfunction may result in increased distribution and decreased clearance [20]. Gijsen et al. performed a population PK analysis focusing solely on ICU patients with normal or enhanced renal function, excluding those with an eGFR < 70 mL/min/1.73m2 as calculated by the CKD-EPI equation. CL and Vss were estimated to be 13.7 L/h and 37.9 L, respectively [21]. These results indicate that in critically ill patients, Vss may be significantly higher, whereas clearance rates are comparable to those in healthy individuals. The idea of augmented renal clearance (ARC) was proposed based on studies of patients who experienced subtherapeutic antimicrobial concentrations when administered standard doses. This suggests a criterion of a CLCR of 130 mL/min/1.73 m2 or higher [22], which has since been broadly accepted based on follow-up studies and consensus [23]. Although numerous studies recommend increasing meropenem doses in patients with ARC [8,9,24,25,26], there have been studies establishing a prospective population pharmacokinetic model specifically for this cohort on meropenem or that provide dosing recommendations based on a PK model.
In the present study, Monte Carlo simulations were used to evaluate optimal dosing regimens for meropenem in adults with normal renal function. Beta-lactams, which are known for their time-dependent killing properties, have a PK/PD index in which the fT>MIC correlates with therapeutic outcomes. Various in vitro studies, animal experiments, and clinical trials have demonstrated that for beta-lactams, both fT>MIC and fT>4MIC are related to rates of bactericidal effect, bacteriological eradication, or clinical cure [27,28,29]. For meropenem, the proposed ranges for fT>MIC or fT>4MIC vary from 40% to 100%. In the present study, 40%fT>MIC, 40%fT>4MIC, 100%fT>MIC, and 100%fT>4MIC were designated as the efficacy targets to conduct the simulations [30,31,32,33,34,35]. In the present study, when pathogens had an MIC of 1, administering 2 g of meropenem every 6 h as a 3 h extended infusion only achieved a 30% PTA for 100%fT>4MIC (Figure 3); however, a continuous infusion of 8 g surpassed a 90% PTA for 100%fT>4MIC (Figure 4). This suggests that patients with normal or augmented renal function may require increased dosages or extended infusion times, which is consistent with several other studies. Hou et al. conducted simulations using pharmacokinetic parameters previously reported for patients with ARC [36] in which the clearance and volume of distribution were set at 19.2 ± 4.1 L/h and 25.35 ± 4.9 L, respectively. The results indicated that for a pathogen with an MIC of 1, administering 3 g every 6 h with an infusion time of either 0.5 or 3 h achieved a PTA of 86.6% for the 70%fT>4MIC target [9]. Wang et al. also performed simulations using PK parameters derived from the NCA. They set the clearance (7.7 ± 1.8 L/h) and volume of distribution (22.6 ± 5.1 L) from a subgroup with normal CLCR. They observed that for targeting 100%fT>4MIC with an MIC of 1 mg/L, only the regimen of 1.0 g every 6 h infused over 3 or 6 h achieved a PTA greater than 90% [10]. Furthermore, Sistanizad and colleagues reviewed antibiotic studies on ARC patients and recommended administering 2 g of meropenem every 6–8 h because prolonged infusion decreased the risk of subtherapeutic exposure [37].
There are several limitations to the present study. First, a variety of meaningful covariates cannot be detected with the small sample size of 12 participants. Despite testing various renal function indicators, such as CLCR and eGFR, only serum creatinine levels showed a significant impact on CL in our model. The narrow range of renal function observed in our cohort of healthy adults may have further constrained the inclusion of other renal function indicators as meaningful covariates. However, this study is significant because it is the first to apply NCA and nonlinear mixed-effect modeling to evaluate the PK of meropenem in healthy Korean adults. In addition, it is expected to significantly assist with model development for our subsequent studies involving patients. Second, because the study was conducted in healthy individuals, it was not possible to assess the microbiological or clinical outcomes of meropenem treatment. Nevertheless, the simulations using PK parameters provided dosing recommendations that may be beneficial for patients. Third, the simulation results suggest dosages much higher compared with those typically used, indicating a need for validation in clinical studies.
In conclusion, this study provides important insights into the population PK of meropenem in healthy adults. It establishes a foundation for more accurate dosing guidelines tailored to patients with normal renal function. Through extensive Monte Carlo simulations, we demonstrated that current dosing strategies may require adjustment to optimize the therapeutic outcomes, particularly in patients with normal renal function. Our findings advocate for model-informed precision dosing regimens to ensure effective antimicrobial therapy, which will reduce the emergence of resistance and improve patient care.

4. Materials and Methods

4.1. Participants

This prospective study was conducted in January 2023 at the Clinical Trial Center of the Hallym University Sacred Heart Hospital. The selection of participants involved the following inclusion criteria:
(1) Adults between 19 and 55 years old at the time of screening.
(2) Individuals free from congenital, chronic conditions, and pathological signs during medical evaluations.
(3) Candidates considered eligible through comprehensive health assessments, which encompass medical histories, vital signs, physical exams, blood tests for hematology and biochemistry, urinalysis, and other diagnostic tests.
The exclusion criteria were:
(1) Participants with clinically significant conditions or histories of disorders in systems, such as gastrointestinal, cardiovascular, respiratory, endocrine, hepatobiliary, hematologic-oncologic, musculoskeletal, renal, neurological, psychiatric, immunological, urological, ophthalmological, otolaryngological, or genetic.
(2) Participants with past conditions known to influence the PK of medications, including liver or kidney impairment.
(3) Participants with known allergies to meropenem or previous adverse reactions to it.
(4) Participants testing positive for Hepatitis B surface antigen, hepatitis C virus antibodies, HIV antigen or antibodies, or syphilis.
(5) Women who are pregnant, breastfeeding, or may become pregnant.

4.2. Study Design

Study participants were administered 500 mg of meropenem intravenously over 30 min, which was prepared in 100 mL of saline solution. Venous blood (6 mL) was collected into tubes containing EDTA at specific time points to assess the PK characteristics. Sample collections occurred just before infusion (0 h), shortly after the dose (0.5 h), and at 0.75, 1, 2, 3, and 6 h after the infusion commenced. A validated high-performance liquid chromatography-tandem mass spectrometry assay was used to measure plasma levels of meropenem [38].

4.3. Population Pharmacokinetic Analysis

The PK parameters for meropenem were analyzed by nonlinear mixed-effects models using the NONMEM software package (version 7.5, ICON Clinical Research LLC, North Wales, PA, USA). Parameter estimation incorporated both fixed and random effects using the first-order conditional estimation with interaction method. This facilitated the modeling of interactions between interindividual variability (IIV) in PK parameters and residual variability (RV) in measured concentrations. The PK analysis of meropenem used one-, two-, and three-compartment models, which were established with the NONMEM PK model library subroutines ADVAN1 TRANS2, ADVAN3 TRANS4, and ADVAN11 TRANS4, respectively. Except for zero-order infusion processes, each model followed first-order kinetics. The models described each PK parameter using the equation θi = θ × exp(ηi), where θ represents the typical value, θi the individual parameter value, and ηi encapsulates the IIV, which was assumed to be normally distributed, having a mean of zero and a variance of ω2. RV was assumed to follow a normal distribution, having a mean of zero and a variance of σ2. Evaluations of RV considered additive, proportional, and combined additive–proportional error models. Model selection and evaluation were based on changes in NONMEM OFVs, parameter estimate precision indicated by relative standard errors, goodness-of-fit plots, VPC, and bootstrap analyses. Significant improvements in the model structures were evident when reductions in OFV exceeded thresholds of 3.84 (for one degree of freedom χ2 test) or 5.99 (for two degrees of freedom), indicating statistical significance at p < 0.05. Model validation involved generating plots of CWRES against time and population predictions (PREDs), and plots comparisons of actual data points against PREDs and individual predictions. VPC aligned the observed meropenem concentrations within 80% prediction intervals from 1000 model simulations. Variability in the predictions of the final model was analyzed using median and 95% confidence intervals from 2000 bootstrap samples.
Significant covariates affecting the PK parameters were identified through a stepwise forward selection and backward elimination process, with inclusion and exclusion thresholds set at p < 0.01 (ΔOFV < −6.635) and p < 0.001 (ΔOFV > 10.83), respectively. Covariate analysis included demographic (gender, age, weight, height, body mass index, BSA) and biochemical parameters (serum protein, albumin, creatinine, cystatin C levels), as well as the effect of renal clearance on meropenem elimination using the Cockcroft–Gault, MDRD, and CKD-EPI formulas. The Perl-speaks-NONMEM (PSN, version 5.3.1, https://uupharmacometrics.github.io/PsN/, accessed on 13 April 2023) tool was used for covariate identification, VPC execution, and nonparametric bootstrapping to evaluate model stability. Post-analysis processing and graphical representation of the outcomes were performed in the R programming environment (version 4.4.0, https://www.r-project.org/, accessed on 6 June 2024).

4.4. Noncompartmental Analysis

Noncompartmental analysis (NCA) was used to assess the plasma concentration-time profiles of meropenem and employed the R programming language and the NonCompart package. The evaluated PK parameters included maximum observed concentration (Cmax), time of last measurable concentration (Tlast), and concentration corresponding to Tlast (Clast). Additional parameters included the area under the concentration-time curve from the start of the dose to the last quantifiable concentration (AUClast) and from the start of dosing to infinity (AUCinf). The AUMC from time zero to Tlast (AUMClast) and extrapolated to infinity (AUMCinf), mean residence time to infinity (MRTinf), total body clearance (CLNCA), volume of distribution as determined by NCA (VzNCA), steady state volume of distribution (VssNCA), and terminal elimination half-life (t1/2λz) were also calculated. Cmax, Tlast, and Clast were directly determined from the observed data. The trapezoidal method with linear up and log down was used for the calculation of the AUClast and AUMClast. AUCinf was estimated by adding the ratio of Clast to the terminal elimination rate constant (λz) to AUClast, which was obtained by log-linear regression of the terminal phase plasma concentrations. AUMCinf was calculated using the formula AUMClast + (Tlast × Clast)/λz + Clast/λz2. The formula for MRTinf was AUMCinf/AUCinf minus half the infusion time. CLNCA was determined as the dose divided by AUCinf, VzNCA as CLNCA divided by λz, VssNCA as the product of MRTinf and CLNCA, and t1/2λz as the natural logarithm of two divided by λz.

4.5. Dosage Simulation

To develop dosage recommendations for meropenem in patients with normal renal function, Monte Carlo simulations were carried out based on the final PK model. A total of 10,000 individual PK parameters for virtual patients were generated, assuming a log-normal distribution for each parameter or covariate. MIC values ranging from 0.06 to 16 mg/L were randomly assigned and reflected the distribution of meropenem MICs for P. aeruginosa as reported by the EUCAST. Among 59,460 cases, the distribution of MIC values was as follows: 0.06 mg/L for 4.43% (2634 cases), 0.125 mg/L for 10.14% (6028 cases), 0.25 mg/L for 18.29% (10,875 cases), 0.5 mg/L for 20.15% (11,982 cases), 1 mg/L for 14.81% (8807 cases), 2 mg/L for 8.68% (5161 cases), 4 mg/L for 6.35% (3776 cases), 8 mg/L for 5.46% (3247 cases), and 16 mg/L for 6.64% (3950 cases).
Steady state concentration-time profiles were generated for the virtual patients to assess the PTA. The therapeutic targets included 40%fT>MIC, 40%fT>4MIC, 100%fT>MIC, and 100%fT>4MIC, with a dosing regimen considered optimal if the PTA was 90% or higher. The fraction of unbound drug (f) was fixed at 0.98. An initial simulation assessed the adequacy of the current dosing regimen for patients with normal renal function, specifically for those with a CLCR > 50 mL/min, using 1 g of meropenem administered every 8 h (q8h). This regimen was consistent with the recommended dosage for treating infections caused by P. aeruginosa, which also specifies a dose of 1 gram every 8 h. Subsequent simulations were conducted to establish meropenem dosage recommendations for patients with normal renal function. These analyses calculated the PTA for 10,000 patients at four dosing levels (0.6, 1, 1.5, and 2 g), two infusion durations (0.5 and 3 h), and three dosing intervals (6, 8, and 12 h). Further simulations were carried out to refine dosing recommendations for meropenem in continuous infusion settings for patients with normal renal function. This final simulation evaluated the PTA for extremely high dosages (e.g., 2, 4, 6, and 8 g), which were administered as continuous infusions. The analysis aimed to determine the PTA for achieving 100%fT>MIC and 100%fT>4MIC.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/antibiotics13090849/s1, Figure S1. Individual fit plots for meropenem (a) normal scale, (b) semi-log scale: closed circles, observed concentrations; solid line, individual predicted concentrations; dotted line, population predicted concentrations. Figure S2. Visual predictive check from simulated concentrations of 1,000 virtual datasets of meropenem: closed circles, observed concentrations; solid lines, 10th, 50th, and 90th percentiles of observations; dashed lines, 10th, 50th, and 90th percentiles of simulated concentrations and shaded areas, 95% confidence intervals for the 10th, 50th, and 90th percentiles of the simulated concentrations. Table S1. Parameter estimates for the base final pharmacokinetic model of meropenem in 12 healthy adult participants. Table S2. Stepwise covariate selection process: forward selection (p-value = 0.01, OFV difference < −6.635, degree of freedom = 1) and backward elimination (p-value = 0.001, OFV difference < 10.83, degree of freedom = 1).

Author Contributions

Conceptualization, Y.K.K. and D.H.L.; methodology, Y.K.K., G.K. and D.H.L.; software, D.H.L.; validation, Y.K.K., G.K. and D.H.L.; formal analysis, Y.K.K., G.K. and D.H.L.; investigation, Y.K.K. and D.H.L.; resources, D.Y.Z.; data curation, Y.K.K., G.K. and D.H.L.; writing—original draft preparation, Y.K.K., G.K. and D.H.L.; writing—review and editing, Y.K.K., G.K., D.Y.Z. and D.H.L.; visualization, Y.K.K. and D.H.L.; supervision, D.H.L.; project administration, D.H.L.; funding acquisition, D.H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the First Research in Lifetime R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2020R1F1A1077190).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Hallym University Sacred Heart Hospital (IRB No. 2022-08-006 and date of approval 25 October 2022).

Informed Consent Statement

Written informed consent was obtained from all subjects participating in the study, including consent for publication.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Craig, W.A. The pharmacology of meropenem, a new carbapenem antibiotic. Clin. Infect. Dis. 1997, 24 (Suppl S2), S266–S275. [Google Scholar] [CrossRef] [PubMed]
  2. Fish, D.N.; Singletary, T.J. Meropenem, a new carbapenem antibiotic. Pharmacotherapy 1997, 17, 644–669. [Google Scholar] [CrossRef] [PubMed]
  3. Patrier, J.; Timsit, J.F. Carbapenem use in critically ill patients. Curr. Opin. Infect. Dis. 2020, 33, 86–91. [Google Scholar] [CrossRef]
  4. Nicolau, D.P. Carbapenems: A potent class of antibiotics. Expert. Opin. Pharmacother. 2008, 9, 23–37. [Google Scholar] [CrossRef] [PubMed]
  5. Versporten, A.; Zarb, P.; Caniaux, I.; Gros, M.F.; Drapier, N.; Miller, M.; Jarlier, V.; Nathwani, D.; Goossens, H.; Koraqi, A.; et al. Antimicrobial consumption and resistance in adult hospital inpatients in 53 countries: Results of an internet-based global point prevalence survey. Lancet Glob. Health 2018, 6, e619–e629. [Google Scholar] [CrossRef] [PubMed]
  6. Antimicrobial Resistance, C. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet 2022, 399, 629–655. [Google Scholar] [CrossRef]
  7. Roberts, J.A.; Kruger, P.; Paterson, D.L.; Lipman, J. Antibiotic resistance—What’s dosing got to do with it? Crit. Care Med. 2008, 36, 2433–2440. [Google Scholar] [CrossRef]
  8. Angelini, J.; Giuliano, S.; Flammini, S.; Pagotto, A.; Lo Re, F.; Tascini, C.; Baraldo, M. Meropenem PK/PD Variability and Renal Function: “We Go Together”. Pharmaceutics 2023, 15, 2238. [Google Scholar] [CrossRef]
  9. Hou, J.; Zhang, M.; Ma, S.Q.; Cong, R.N.; Li, J.F. Application of Monte Carlo simulation to optimise the dosage regimen of meropenem in patients with augmented renal clearance for Pseudomonas aeruginosa infection. Heliyon 2024, 10, e32600. [Google Scholar] [CrossRef]
  10. Wang, Y.; Li, H.; Wang, D.; Li, Y.; Shen, Y.; Fu, Y.; Li, Y.; Gao, M.; Zhang, D. Changes of PK/PD of Meropenem in patients with abdominal septic shock and exploration of clinical rational administration plan: A prospective exploratory study. Sci. Rep. 2024, 14, 10173. [Google Scholar] [CrossRef]
  11. European Committee on Antimicrobial Susceptibility Testing. Antimicrobial Wild Type Distributions of Microorganisms. Available online: https://mic.eucast.org/ (accessed on 18 June 2024).
  12. Bax, R.P.; Bastain, W.; Featherstone, A.; Wilkinson, D.M.; Hutchison, M.; Haworth, S.J. The pharmacokinetics of meropenem in volunteers. J. Antimicrob. Chemother. 1989, 24, 311–320. [Google Scholar] [CrossRef] [PubMed]
  13. Wise, R.; Logan, M.; Cooper, M.; Ashby, J.P.; Andrews, J.M. Meropenem pharmacokinetics and penetration into an inflammatory exudate. Antimicrob. Agents Chemother. 1990, 34, 1515–1517. [Google Scholar] [CrossRef] [PubMed]
  14. Burman, L.A.; Nilsson-Ehle, I.; Hutchison, M.; Haworth, S.J.; Norrby, S.R. Pharmacokinetics of meropenem and its metabolite ICI 213,689 in healthy subjects with known renal metabolism of imipenem. J. Antimicrob. Chemother. 1991, 27, 219–224. [Google Scholar] [CrossRef] [PubMed]
  15. Nilsson-Ehle, I.; Hutchison, M.; Haworth, S.J.; Norrby, S.R. Pharmacokinetics of meropenem compared to imipenem-cilastatin in young, healthy males. Eur. J. Clin. Microbiol. Infect. Dis. 1991, 10, 85–88. [Google Scholar] [CrossRef] [PubMed]
  16. Christensson, B.A.; Nilsson-Ehle, I.; Hutchison, M.; Haworth, S.J.; Oqvist, B.; Norrby, S.R. Pharmacokinetics of meropenem in subjects with various degrees of renal impairment. Antimicrob. Agents Chemother. 1992, 36, 1532–1537. [Google Scholar] [CrossRef]
  17. Leroy, A.; Fillastre, J.P.; Borsa-Lebas, F.; Etienne, I.; Humbert, G. Pharmacokinetics of meropenem (ICI 194,660) and its metabolite (ICI 213,689) in healthy subjects and in patients with renal impairment. Antimicrob. Agents Chemother. 1992, 36, 2794–2798. [Google Scholar] [CrossRef]
  18. Ljungberg, B.; Nilsson-Ehle, I. Pharmacokinetics of meropenem and its metabolite in young and elderly healthy men. Antimicrob. Agents Chemother. 1992, 36, 1437–1440. [Google Scholar] [CrossRef]
  19. Li, L.; Sassen, S.D.T.; Ewoldt, T.M.J.; Abdulla, A.; Hunfeld, N.G.M.; Muller, A.E.; de Winter, B.C.M.; Endeman, H.; Koch, B.C.P. Meropenem Model-Informed Precision Dosing in the Treatment of Critically Ill Patients: Can We Use It? Antibiotics 2023, 12, 383. [Google Scholar] [CrossRef]
  20. Roberts, J.A.; Abdul-Aziz, M.H.; Lipman, J.; Mouton, J.W.; Vinks, A.A.; Felton, T.W.; Hope, W.W.; Farkas, A.; Neely, M.N.; Schentag, J.J.; et al. Individualised antibiotic dosing for patients who are critically ill: Challenges and potential solutions. Lancet Infect. Dis. 2014, 14, 498–509. [Google Scholar] [CrossRef]
  21. Gijsen, M.; Elkayal, O.; Annaert, P.; Van Daele, R.; Meersseman, P.; Debaveye, Y.; Wauters, J.; Dreesen, E.; Spriet, I. Meropenem Target Attainment and Population Pharmacokinetics in Critically Ill Septic Patients with Preserved or Increased Renal Function. Infect. Drug Resist. 2022, 15, 53–62. [Google Scholar] [CrossRef]
  22. Udy, A.A.; Varghese, J.M.; Altukroni, M.; Briscoe, S.; McWhinney, B.C.; Ungerer, J.P.; Lipman, J.; Roberts, J.A. Subtherapeutic initial beta-lactam concentrations in select critically ill patients: Association between augmented renal clearance and low trough drug concentrations. Chest 2012, 142, 30–39. [Google Scholar] [CrossRef] [PubMed]
  23. Bilbao-Meseguer, I.; Rodriguez-Gascon, A.; Barrasa, H.; Isla, A.; Solinis, M.A. Augmented Renal Clearance in Critically Ill Patients: A Systematic Review. Clin. Pharmacokinet. 2018, 57, 1107–1121. [Google Scholar] [CrossRef] [PubMed]
  24. Tamatsukuri, T.; Ohbayashi, M.; Kohyama, N.; Kobayashi, Y.; Yamamoto, T.; Fukuda, K.; Nakamura, S.; Miyake, Y.; Dohi, K.; Kogo, M. The exploration of population pharmacokinetic model for meropenem in augmented renal clearance and investigation of optimum setting of dose. J. Infect. Chemother. 2018, 24, 834–840. [Google Scholar] [CrossRef]
  25. Carrie, C.; Chadefaux, G.; Sauvage, N.; de Courson, H.; Petit, L.; Nouette-Gaulain, K.; Pereira, B.; Biais, M. Increased beta-Lactams dosing regimens improve clinical outcome in critically ill patients with augmented renal clearance treated for a first episode of hospital or ventilator-acquired pneumonia: A before and after study. Crit. Care 2019, 23, 379. [Google Scholar] [CrossRef] [PubMed]
  26. Agyeman, A.A.; Rogers, K.E.; Tait, J.R.; Bergen, P.J.; Kirkpatrick, C.M.; Wallis, S.C.; Bulitta, J.B.; Paterson, D.L.; Lipman, J.; Nation, R.L.; et al. Evaluation of Meropenem-Ciprofloxacin Combination Dosage Regimens for the Pharmacokinetics of Critically Ill Patients With Augmented Renal Clearance. Clin. Pharmacol. Ther. 2021, 109, 1104–1115. [Google Scholar] [CrossRef]
  27. Craig, W.A. Pharmacokinetic/pharmacodynamic parameters: Rationale for antibacterial dosing of mice and men. Clin. Infect. Dis. 1998, 26, 1–12. [Google Scholar] [CrossRef]
  28. Drusano, G.L. Antimicrobial pharmacodynamics: Critical interactions of ‘bug and drug’. Nat. Rev. Microbiol. 2004, 2, 289–300. [Google Scholar] [CrossRef]
  29. Delattre, I.K.; Taccone, F.S.; Jacobs, F.; Hites, M.; Dugernier, T.; Spapen, H.; Laterre, P.F.; Wallemacq, P.E.; Van Bambeke, F.; Tulkens, P.M. Optimizing beta-lactams treatment in critically-ill patients using pharmacokinetics/pharmacodynamics targets: Are first conventional doses effective? Expert. Rev. Anti Infect. Ther. 2017, 15, 677–688. [Google Scholar] [CrossRef]
  30. Roberts, J.A.; Paul, S.K.; Akova, M.; Bassetti, M.; De Waele, J.J.; Dimopoulos, G.; Kaukonen, K.M.; Koulenti, D.; Martin, C.; Montravers, P.; et al. DALI: Defining antibiotic levels in intensive care unit patients: Are current beta-lactam antibiotic doses sufficient for critically ill patients? Clin. Infect. Dis. 2014, 58, 1072–1083. [Google Scholar] [CrossRef]
  31. Del Bono, V.; Giacobbe, D.R.; Marchese, A.; Parisini, A.; Fucile, C.; Coppo, E.; Marini, V.; Arena, A.; Molin, A.; Martelli, A.; et al. Meropenem for treating KPC-producing Klebsiella pneumoniae bloodstream infections: Should we get to the PK/PD root of the paradox? Virulence 2017, 8, 66–73. [Google Scholar] [CrossRef]
  32. Kothekar, A.T.; Divatia, J.V.; Myatra, S.N.; Patil, A.; Nookala Krishnamurthy, M.; Maheshwarappa, H.M.; Siddiqui, S.S.; Gurjar, M.; Biswas, S.; Gota, V. Clinical pharmacokinetics of 3-h extended infusion of meropenem in adult patients with severe sepsis and septic shock: Implications for empirical therapy against Gram-negative bacteria. Ann. Intensive Care 2020, 10, 4. [Google Scholar] [CrossRef]
  33. Scharf, C.; Liebchen, U.; Paal, M.; Taubert, M.; Vogeser, M.; Irlbeck, M.; Zoller, M.; Schroeder, I. The higher the better? Defining the optimal beta-lactam target for critically ill patients to reach infection resolution and improve outcome. J. Intensive Care 2020, 8, 86. [Google Scholar] [CrossRef]
  34. Boonpeng, A.; Jaruratanasirikul, S.; Jullangkoon, M.; Samaeng, M.; Wattanavijitkul, T.; Bhurayanontachai, R.; Pattharachayakul, S. Population Pharmacokinetics/Pharmacodynamics and Clinical Outcomes of Meropenem in Critically Ill Patients. Antimicrob. Agents Chemother. 2022, 66, e0084522. [Google Scholar] [CrossRef] [PubMed]
  35. Lewis, S.J.; Mueller, B.A. Antibiotic dosing recommendations in critically ill patients receiving new innovative kidney replacement therapy. BMC Nephrol. 2024, 25, 73. [Google Scholar] [CrossRef] [PubMed]
  36. Razzazzadeh, S.; Darazam, I.A.; Hajiesmaeili, M.; Salamzadeh, J.; Mahboubi, A.; Sadeghnezhad, E.; Sahraei, Z. Investigation of pharmacokinetic and clinical outcomes of various meropenem regimens in patients with ventilator-associated pneumonia and augmented renal clearance. Eur. J. Clin. Pharmacol. 2022, 78, 823–829. [Google Scholar] [CrossRef]
  37. Sistanizad, M.; Hassanpour, R.; Pourheidar, E. Are Antibiotics Appropriately Dosed in Critically Ill Patients with Augmented Renal Clearance? A Narrative Review. Int. J. Clin. Pract. 2022, 2022, 1867674. [Google Scholar] [CrossRef] [PubMed]
  38. Lee, D.H.; Kim, H.S.; Park, S.; Kim, H.I.; Lee, S.H.; Kim, Y.K. Population Pharmacokinetics of Meropenem in Critically Ill Korean Patients and Effects of Extracorporeal Membrane Oxygenation. Pharmaceutics 2021, 13, 1861. [Google Scholar] [CrossRef]
Antibiotics 13 00849 g001
Figure 1. Goodness-of-fit plots for the final pharmacokinetic model of meropenem: (a) conditional weighted residuals (CWRES) vs. time, (b) CWRES vs. population predicted concentration (PRED), (c) observed concentration vs. PRED, and (d) observed concentration vs. individual predicted concentration (IPRED). Smooth curves are shown by the dashed lines.
Figure 1. Goodness-of-fit plots for the final pharmacokinetic model of meropenem: (a) conditional weighted residuals (CWRES) vs. time, (b) CWRES vs. population predicted concentration (PRED), (c) observed concentration vs. PRED, and (d) observed concentration vs. individual predicted concentration (IPRED). Smooth curves are shown by the dashed lines.
Antibiotics 13 00849 g001
Antibiotics 13 00849 g002
Figure 2. Probability of target attainment for empirical meropenem therapy (1 g q8h) in patients with normal renal function, which highlights MIC distribution for P. aeruginosa.
Figure 2. Probability of target attainment for empirical meropenem therapy (1 g q8h) in patients with normal renal function, which highlights MIC distribution for P. aeruginosa.
Antibiotics 13 00849 g002
Antibiotics 13 00849 g003
Figure 3. PTA for intermittent infusion of meropenem in adults with normal renal function. The analysis was performed at four dose levels (500, 1000, 1500, and 2000 mg) and three dosing intervals (6, 8, and 12 h).
Figure 3. PTA for intermittent infusion of meropenem in adults with normal renal function. The analysis was performed at four dose levels (500, 1000, 1500, and 2000 mg) and three dosing intervals (6, 8, and 12 h).
Antibiotics 13 00849 g003
Antibiotics 13 00849 g004
Figure 4. PTA for continuous meropenem infusions in adults with normal renal function at daily meropenem doses ranging from 2000 to 8000 mg.
Figure 4. PTA for continuous meropenem infusions in adults with normal renal function at daily meropenem doses ranging from 2000 to 8000 mg.
Antibiotics 13 00849 g004
Table 1. Subject characteristics.
Table 1. Subject characteristics.
ParametersMean (CV%)Median (IQR)
Demographic characteristics
Age, years36.8 (19.9%)36.0 (31.5–39.3)
Height, cm168 (4.29%)168 (163–173)
Weight, kg65.7 (20.8%)61.7 (56.7–73.3)
Body surface area, m21.74 (11.6%)1.71 (1.61–1.88)
Body mass index, kg/m223.0 (13.9%)21.5 (21.4–24.0)
Laboratory characteristics
Protein, g/dL7.48 (4.64%)7.45 (7.28–7.63)
Albumin, g/dL4.88 (4.35%)4.80 (4.78–5.03)
Cystatin C, mg/dL0.790 (15.9%)0.765 (0.705–0.873)
Creatinine, mg/dL0.863 (19.0%)0.860 (0.738–1.02)
Total bilirubin (mg/dL)0.558 (62.2%)0.470 (0.328–0.720)
Blood urea nitrogen, mg/dL15.1 (30.0%)14.1 (12.1–17.9)
Alanine aminotransferase, U/L21.8 (85.3%)17.0 (10.8–23.3)
Aspartate aminotransferase, U/L25.0 (38.7%)21.0 (19.8–27.5)
Gamma-glutamyl transferase, U/L27.3 (68.1%)17.0 (14.5–38.3)
Renal functions
CLCR, CG (mL/min) a105 (21.2%)105 (84.3–118)
CLCR, normalized CG (mL/min/1.73m2) b93.8 (16.4%)93.2 (80.5–106)
eGFR, MDRD (mL/min/1.73m2) c108 (13.9%)108 (96.6–116)
eGFR, CKD-EPICR (mL/min/1.73m2) d111 (15.4%)110 (102–120)
eGFR, CKD-EPICC (mL/min/1.73m2) e112 (14.9%)111 (102–122)
eGFR, CKD-EPICR-CC (mL/min/1.73m2) f93.3 (14.4%)93.1 (82.9–98.8)
eGFR, adjusted MDRD (mL/min) g107 (11.5%)111 (98.0–116)
eGFR, adjusted CKD-EPICR (mL/min) g110 (12.6%)116 (102–121)
eGFR, adjusted CKD-EPICC (mL/min) g112 (12.3%)116 (100–120)
eGFR, adjusted CKD-EPICR-CC(mL/min/1.73m2) g101 (15.8%)100 (90.7–112)
CV, coefficient of variation; IQR, interquartile range; CLCR, creatinine clearance; CG, Cockcroft–Gault equation; eGFR, estimated glomerular filtration rate; MDRD, modification of diet in renal disease; CKD-EPI, chronic kidney disease epidemiology collaboration; CR, creatinine; CC, cystatin C; min, the minimum of (CR or CC)/number and 1; max, the maximum of (CR or CC)/number and 1. a CLCR, CG = (140−Age) × weight/CR × 72 (× 0.85 if female). b CLCR, CG (normalized) = CLCR, CG / BSA × 1.73 m2. c eGFR = 175 × CR−1.154 × Age−0.203 (× 0.742 if female) d eGFR (female) = 142 × min (CR/0.7,1)−0.241 × max (CR/0.7,1)−1.200 × 0.9938Age × 1.012 eGFR (male) = 142 × min (CR/0.9,1)−0.302 × max (CR/0.9,1)−1.200 × 0.9938Age. e eGFR (female) = 133 × min (CC/0.7,1)−0.499 × max (CC/0.7,1)−1.328 × 0.9962Age × 0.932 [if female] f eGFR (female) = 135 × min (CR/0.7,1)−0.219 × max (CR/0.7,1)−0.544 × min (CC/0.8,1)0.323 × max (CC/0.8,1)−0.778 × 0.9961Age × 0.963 eGFR (male) = 135 × min (CR/0.9,1)−0.144 × max (CR/0.9,1)−0.544 × min (CC/0.8,1)0.323 × max (CC/0.8,1)−0.778 × 0.9961Age. g The adjusted eGFR by MDRD and CKD-EPI equations are eGFR (adjusted) = eGFR (MDRD or CKD-EPI)/1.73 m2 × BSA.
Table 2. Parameter estimates and bootstrap medians (95% confidence intervals) for the final pharmacokinetic model of meropenem in 12 healthy adult participants.
Table 2. Parameter estimates and bootstrap medians (95% confidence intervals) for the final pharmacokinetic model of meropenem in 12 healthy adult participants.
ParameterEstimateRSE (%) [Shrinkage, %]Bootstrap Median (95% CI)
Structural model
CL = θ1 × (CR/0.86)θ2
θ1 (L/h)12.47.8712.3 (10.8–14.7)
θ2−0.39219.2−0.378 (−0.579–−0.115)
V1 (L)8.2612.58.31 (6.56–11.0)
Q (L/h)5.2216.15.05 (3.34–7.33)
V2 (L)4.0611.14.01 (3.00–5.07)
Interindividual variability
CL (%)26.230.4 [1.82]25.4 (7.8–38.3)
V1 a1.534.801.53 (1.07–2.43)
Q (%) b14.4[49.1]
V2 (%) b17.9[11.2]
Residual variability
Proportional error (%)10.920.410.4 (6.50–14.0)
RSE, relative standard error; CI, confidence interval; CL, total clearance; V1, central volume of distribution; V2, volume of distribution for the first peripheral compartment; Q, intercompartmental clearance between V1 and V2; CR, serum creatinine level; a, the estimate indicates that the interindividual variability (IIV) of V1 is 1.53 times the IIV of CL; b, fixed.
Table 3. Descriptive statistics for the individual pharmacokinetic parameters from noncompartmental and population PK analysis.
Table 3. Descriptive statistics for the individual pharmacokinetic parameters from noncompartmental and population PK analysis.
ParametersUnitMean (CV%)Median (IQR)
NCA results
Cmaxmg/L40.2 (30.1%)43.3 (34.2–46.3)
Clasth0.393 (71.4%)0.289 (0.231–0.484)
Tlastmg/L5.83 (7.56%)6.00 (6.00–6.00)
AUClastmg·h/L39.8 (22.7%)41.4 (35.9–44.9)
AUCinfmg·h/L40.4 (22.8%)42.3 (36.2–45.2)
AUMClastmg·h2/L49.8 (22.1%)51.0 (42.9–54.3)
AUMCinfmg·h2/L53.9 (24.1%)54.2 (46.2–58.9)
MRTinfh1.09 (13.3%)1.07 (0.989–1.22)
CLNCAL/h/kg0.201 (21.1%)0.193 (0.174–0.218)
VzZNCAL/kg0.280 (25.7%)0.260 (0.236–0.296)
VssNCAL/kg0.219 (25.8%)0.210 (0.187–0.228)
t1/2λz h0.967 (15.3%)0.908 (0.870–1.08)
Population PK results
CLL/h/kg0.197 (20.8%)0.189 (0.169–0.215)
VCL/kg0.134 (37.7%)0.118 (0.108–0.137)
VssL/kg0.197 (25.4%)0.184 (0.165–0.209)
AUCmg·h/L41.3 (21.7%)43.5 (37.1–45.2)
t1/2αh0.260 (26.9%)0.229 (0.214–0.277)
t1/2βh0.985 (14.5%)0.923 (0.893–1.10)
CV, coefficient of variation; IQR, interquartile range; Cmax, maximum observed plasma concentration; Tlast, time of last measurable concentration; Clast, concentration corresponding to Tlast; AUClast, area under the plasma concentration-time curve (AUC) from the start of the dose to the last quantifiable concentration; AUCinf, AUC from the start of dosing to infinity; AUMClast, area under the first moment curve (AUMC) from 0 h to the Tlast; AUMCinf, AUMC extrapolated to infinity, based on the last observed concentration; MRTinf, mean residence time from 0 h to infinite; CLNCA, total body clearance determined by NCA; VzNCA, volume of distribution (Vd) determined by NCA; VssNCA, steady state Vd determined by NCA; t1/2λz, terminal elimination half-life. Formulas: AUCinf, AUClast + Clast/λz; AUMCinf, AUMClast + (Tlast × Clast)/λz + Clast/λz2; MRTinf, AUMCinf/AUCinf − infusion time/2; CLNCA, dose/AUCinf; VzNCA, CLNCA/λz; VssNCA, MRTinf × CLNCA; t1/2λz, ln (2)/λz; CL, total clearance; VC, central volume of distribution; Vss, steady state volume of distribution; AUC, dose/CL; t1/2α, distribution phase half-life; t1/2β, elimination phase half-life
Table 4. Summary of the demographic characteristics and pharmacokinetic properties of meropenem in healthy participants.
Table 4. Summary of the demographic characteristics and pharmacokinetic properties of meropenem in healthy participants.
StudynAge (Years)Weight (kg)Height (cm)BSA (m2)CL (L/h)Vss (L)
Bax et al. [12]1226 (19–45)74 (68–87)179 (170–184)1.92 * 16.7 (1.2)19.1 (1.6)
Wise et al. [13]623.6 (23–31)69.9 (63–80)180 (169–187)1.89 * 15.2 (3.09)20.6 (5.90)
Burman et al. [14]635 (30–40)83 (68–93)––16.6 (0.6)20.4 (0.7)
Nilsson-Ehle et al. [15]833 (22–38)74 (66–86)––11.3 (1.86)12.5 (1.50)
Christensson et al. [16]634 (13.4)79 (8.4)–1.96 (0.09)11.2 (1.68)14.7 (0.21)
Leroy et al. [17]633.8 (9.0)66.9 (12.4)––19.7 (5.7)27.4 (7.0)
Ljunberg et al. [18]828 (5.2)69 (7.7)–1.88 (7.3)11.7 (1.7)11.7 (1.2)
The values are mean (range or SD). BSA, body surface area; CL, total clearance; Vss, total volume of distribution at steady state; *, calculated based on the average body weight and height using the de Bois method. (BSA = 0.007185 × height0.725 × weight0.425).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kim, Y.K.; Kang, G.; Zang, D.Y.; Lee, D.H. Precision Dosing of Meropenem in Adults with Normal Renal Function: Insights from a Population Pharmacokinetic and Monte Carlo Simulation Study. Antibiotics 2024, 13, 849. https://doi.org/10.3390/antibiotics13090849

AMA Style

Kim YK, Kang G, Zang DY, Lee DH. Precision Dosing of Meropenem in Adults with Normal Renal Function: Insights from a Population Pharmacokinetic and Monte Carlo Simulation Study. Antibiotics. 2024; 13(9):849. https://doi.org/10.3390/antibiotics13090849

Chicago/Turabian Style

Kim, Yong Kyun, Gaeun Kang, Dae Young Zang, and Dong Hwan Lee. 2024. "Precision Dosing of Meropenem in Adults with Normal Renal Function: Insights from a Population Pharmacokinetic and Monte Carlo Simulation Study" Antibiotics 13, no. 9: 849. https://doi.org/10.3390/antibiotics13090849

APA Style

Kim, Y. K., Kang, G., Zang, D. Y., & Lee, D. H. (2024). Precision Dosing of Meropenem in Adults with Normal Renal Function: Insights from a Population Pharmacokinetic and Monte Carlo Simulation Study. Antibiotics, 13(9), 849. https://doi.org/10.3390/antibiotics13090849

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop