Skip to main content
NCBI home page
Antimicrobial Agents and Chemotherapy logoLink to Antimicrobial Agents and Chemotherapy
. 2024 Feb 6;68(3):e01541-23. doi: 10.1128/aac.01541-23

Simulated drug disposition in critically ill patients to evaluate effective PK/PD targets for combating Pseudomonas aeruginosa resistance to meropenem

Xiaonan Zhang 1, Yan Wang 2, Sanwang Li 3, Feifan Xie 1,✉, Hanxi Yi 4,✉
Editor: James E Leggett5
PMCID: PMC10916391  PMID: 38319075

ABSTRACT

Bacterial infections, including those caused by Pseudomonas aeruginosa, often lead to sepsis, necessitating effective antibiotic treatment like carbapenems. The key pharmacokinetic/pharmacodynamic (PK/PD) index correlated to carbapenem efficacy is the fraction time of unbound plasma concentration above the minimum inhibitory concentration (MIC) of the pathogen (%fT > MIC). While multiple targets exist, determining the most effective one for critically ill patients remains a matter of debate. This study evaluated meropenem’s bactericidal potency and its ability to combat drug resistance in Pseudomonas aeruginosa under three representative PK/PD targets: 40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC. The hollow fiber infection model (HFIM) was constructed, validated, and subsequently inoculated with a substantial Pseudomonas aeruginosa load (1 × 108 CFU/mL). Different meropenem regimens were administered to achieve the specified PK/PD targets. At specified intervals, samples were collected from the HFIM system and subjected to centrifugation. The resulting supernatant was utilized to determine drug concentrations, while the precipitates were used to track changes in both total and drug-resistant bacterial populations over time by the spread plate method. The HFIM accurately reproduced meropenem’s pharmacokinetics in critically ill patients. All three PK/PD target groups exhibited a rapid bactericidal response within 6 h of the initial treatment. However, the 40% fT > MIC and 100% fT > MIC groups subsequently showed bacterial resurgence and resistance, whereas the 100% fT > 4× MIC group displayed sustained bactericidal activity with no evidence of drug resistance. The HFIM system revealed that maintaining 100% fT > 4× MIC offers a desirable microbiological response for critically ill patients, demonstrating strong bactericidal capacity and effective prevention of drug resistance.

KEYWORDS: PK/PD, Pseudomonas aeruginosa, sepsis, meropenem, hollow fiber infection model

INTRODUCTION

As a critical global healthcare concern, sepsis manifests as a life-threatening syndrome of organ dysfunction arising from a dysregulated host response to infection (1). It stands as the primary cause of mortality resulting from infections. Notably, hospital mortality rates for sepsis cases are around 27%, reaching even higher estimates of 42% for intensive care unit (ICU) patients undergoing sepsis treatment (2). Beyond its clinical implications, sepsis bears a substantial economic burden, with the average hospital-wide cost exceeding $32,000 per patient (3).

The pathophysiology of sepsis centers on the activation of a host inflammatory response due to infection (4). Upon entering the body, bacteria and toxins stimulate inflammatory cells, leading to the production and release of numerous inflammatory mediators, including TNF-α, IL-6, and chemokines (5). These mediators evoke either localized or systemic inflammatory reactions. Concurrently, immunosuppression becomes evident within the patient system. When the degree of immunosuppression surpasses the inflammatory response, it signifies the body’s immune reaction entering a state of decompensation (6). Within the context of sepsis, severe infections with a high bacterial load driven by immunosuppression further intensify the systemic inflammatory response and precipitate diverse pathological changes. This cascade worsens organ failure and, in some cases, leads to fatality (7). Consequently, infection constitutes a pivotal component in the progression of sepsis.

Infections stem from diverse sources including bacteria, fungi, viruses, and parasites, with bacterial infections being the most prevalent (8). Timely and appropriate administration of antibiotics, encompassing both the spectrum of antibiotic activity and therapeutic exposure, is a pivotal intervention in bacterial infection therapies (9). Carbapenems, such as meropenem, imipenem, and ertapenem, belong to the class of atypical β-lactam antibiotics (10). This category finds common employment in addressing bacterial infections within the ICU and is notably regarded as an empirical treatment for sepsis due to its wide-ranging antibacterial spectrum (including Pseudomonas aeruginosa) and robust stability against β-lactamases (11, 12).

Carbapenems are a type of time-dependent bactericidal agents. The pharmacokinetic/pharmacodynamic (PK/PD) index strongly associated with the bactericidal efficacy of carbapenems is the percentage of the time during which its unbound plasma concentration exceeds the minimum inhibitory concentration (MIC) of the pathogen (%fT > MIC) (13). The commonly used PK/PD targets for carbapenems include 40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC (14–17). The 40% fT > MIC target is often used for hospitalized patients with normal immune function (18). Yet, there is a suggestion that sepsis patients with immunosuppression might benefit from more stringent PK/PD targets. For instance, ensuring free plasma carbapenem concentrations exceed one to four times the pathogen MIC throughout the dosing interval (100% fT > MIC or 100% fT > 4× MIC) (19–21). However, robust data supporting the selection of appropriate PK/PD targets for critically ill patients are currently limited. In our earlier study, we showcased that meropenem treatment groups meeting aggressive PK/PD targets of 100% fT > MIC or 100% fT > 4× MIC exhibited superior survival outcomes compared to the conventional 40% fT > MIC target in an intra-abdominal sepsis rat model (22). However, there were no significant statistical differences in survival outcomes between the 100% fT > MIC and 100% fT > 4× MIC targets. Moreover, the microbiological reaction to diverse PK/PD targets remains uncertain because of erratic bacterial counts in blood specimens.

The hollow fiber infection model (HFIM) serves as an in vitro system that offers a flexible environment for continuous high-density cell culture (23). The HFIM finds primary utility in diverse applications, encompassing the collection of cellular products (including exosomes, monoclonal antibodies, recombinant proteins, and other cell-secreted substances), cell co-culturing, and in vitro PK/PD studies (24). In comparison to the animal infection model used in our preclinical study, the in vitro HFIM presents numerous advantages, such as its user-friendly operation, robust controllability, easy multisampling capabilities, and long-period administration (e.g., 7–14 days). This adaptability renders it particularly well suited for PK/PD studies pertaining to bacterial resistance (23).

The goal of this study is to evaluate the bactericidal capacity and resistance suppression of the representative PK/PD targets (40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC) of meropenem against Pseudomonas aeruginosa with simulated drug disposition in critically ill patients using HFIM system.

MATERIALS AND METHODS

Bacterial strains and compounds

Pseudomonas aeruginosa ATCC 9027 and 27853 (Shanghai Bioresource Collection Center, Shanghai, China) were adopted for all the experiments. ATCC 9027 served as the wild-type strain utilized in the HFIM experiment, while ATCC 27853 was employed as the quality control strain for determining the MIC. The bacterial strains were stored in a −80°C freezer in a 40% glycerin solution until utilization. For experimentation, they were cultured and quantified in Mueller-Hinton broth (MHB) and on Mueller-Hinton agar (MHA) (Guangdong Huankai Microbial Sci. & Tech. Co., Ltd., Guangdong, China). Meropenem (Aladdin Biochemical Technology Co., Ltd., Shanghai, China) was dissolved in sterile ultrapure water to attain the desired concentrations. Then, the solution was subjected to filtration through a 0.22-micron filter for sterilization, making it suitable for use in the HFIM system.

Antimicrobial susceptibility test

Following the Clinical and Laboratory Standards Institute (CLSI) guidelines, meropenem’s MICs against the aforementioned microorganisms were determined using the agar dilution method. Microorganisms were incubated in MHB at 37°C until they reached the logarithmic growth phase. Afterward, these bacterial suspensions were further diluted with MHB to achieve an optical density of 0.08–0.13 at a wavelength of 625 nm, which corresponds to an approximate bacterial load of 1 × 108 CFU/mL. Further 10-fold dilution was performed by combining 100 µL of the bacterial suspension with 900 µL of MHB. Then, 2 µL of this diluted solution was added to MHA plates containing serial twofold dilutions of meropenem ranging from 0.002 to 32 mg/L. This resulted in a bacterial density of 1 × 104 CFU/spot. The inoculated plates were incubated at 37°C for 16–20 h. MIC was defined as the lowest drug concentration that completely inhibited visible bacterial growth. Meropenem MIC against ATCC 27853 was used as the quality control standard.

Setup and validation of the HFIM

The HFIM comprises several components: the diluent reservoir, syringe compartment, central reservoir, elimination reservoir, and the hollow fiber cartridge. Each component was interconnected using silicone hoses powered by peristaltic pumps, as depicted in Fig. 1. Two peristaltic pumps that connected the diluent reservoir and elimination reservoir to the central reservoir maintained equal flow rates to ensure a consistent volume of broth medium within the central reservoir, effectively replicating the dynamic process of in vivo drug concentration under various administration regimens. During administration, a syringe pump transferred the drug solution from the syringe compartment to the central reservoir. The central reservoir was linked to both sample ports of the hollow fiber cartridge. Through the action of a duet pump, the drug-containing broth medium was rapidly circulated between the central reservoir and the hollow fiber cartridge to keep uniform drug concentrations. The cartridge was equipped with numerous small semi-permeable hollow fiber capillaries, facilitating the unhindered passage of drugs, nutrients, and bacterial metabolites, while effectively entrapping bacteria. Bacterial growth exclusively occurred within the extracapillary space (ECS) of the cartridge. To foster conditions conducive to bacterial growth, the entire apparatus was housed within a temperature-controlled incubator set at 37°C (25).

Fig 1.

Fig 1

Open in a new tab

Schematic diagram of the HFIM system.

The apparent volume of distribution (Vd) denotes the ratio of drug quantity (X) to plasma concentration (C) at the point of dynamic equilibrium in the body, expressed as Vd = X/C. Reference values of clearance (CLhuman) and volume of distribution (Vd, human) of meropenem in critically ill patients were 6.15 L/h and 17.4 L, respectively (26). The medium volume of the entire HFIM system (including the central reservoir, cartridge, and tubing) (Vd, HFIM model) for the HFIM was set at 200 mL, following the manufacturer’s guidance. The scaled dose of the HFIM (XHFIM) was calculated as Xhuman × Vd, HFIM model /Vd, human to ensure consistency between the drug equilibrium concentration in the HFIM and that in the human body. The elimination velocity of the HFIM (Qelimination), equivalent to the flow rate from the central reservoir to the elimination reservoir, was adjusted to match the actual drug elimination rate in humans. Specifically, Qelimination was determined as CLhuman /Vd, human × Vd, HFIM model, with a value of 1,178 µL/minute.

With the aforementioned parameters, a single dose of meropenem (1.72 mg) was infused over 0.5 h into the HFIM system to verify the consistency of simulated and observed concentrations. Approximately 500 µL of MHB was collected from the sampling ports of the central reservoir and cartridge at various time points up to 12 h post-drug administration. These samples were stored in a −80°C freezer and subsequently analyzed using a modified ultra-performance liquid chromatography with photodiode array method originally developed for plasma samples (27).

Determination of meropenem dosing regimens in the HFIM

The MIC of meropenem against Pseudomonas aeruginosa ATCC 9027 was determined to be 0.125 mg/L. PK simulations were conducted to identify appropriate dosing regimens of meropenem for HFIM experiments, aligning with representative PK/PD targets (40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC). The meropenem regimens selected for HFIM experiments were 0.14 mg q12h (achieving 40% fT > MIC), 1.72 mg q12h (attaining 100% fT > MIC), and 6.32 mg q12h (meeting 100% fT > 4× MIC), all with a fixed infusion duration of 0.5 h. The simulated concentration-time profiles for these regimens are depicted in Fig. 2.

Fig 2.

Fig 2

Open in a new tab

The simulated concentration-time curves and illustrative attainment of PK/PD targets for different meropenem dosing regimens of 0.14 mg q12h (40% fT > MIC), 1.72 mg q12h (100% fT > MIC), and 6.32 mg q12h (100% fT > 4× MIC) for a treatment duration of 240 h. The dots represent the observed concentrations, and the Y-axis is shown as a base 2 logarithmic scale.

Bactericidal capacity and suppression of drug resistance studies

Pseudomonas aeruginosa ATCC 9027 was revived, cultured, and diluted to a concentration of 1 × 108 CFU/mL in sterile MHB. A 20 mL bacterial solution was injected into the left sample port of the cartridge, while an empty sterile syringe was connected to the right port. The solution was carefully flushed back and forth until consistent turbidity was observed in both syringes. The entire bacterial solution was then injected into the cartridge.

Three treatment groups (0.14 mg q12h, 1.72 mg q12h, and 6.32 mg q12h) and one control group, each consisting of two parallel replicates, were planned. The control group received an equivalent volume of sterile saline solution instead of the meropenem solution. The experiments lasted for 240 h. Broth samples (250 µL) for drug measurement were collected from the cartridge’s sampling port after the initial dosing (from 0.167 to 12 h) and just before each subsequent dosing. In the 0.14 mg dose group, trough concentrations were not measured due to sensitivity issues. Instead, samples were collected at 5 h post-dosing for subsequent doses. These samples were preserved in a −80°C freezer until drug measurement. For bacterial counting, 250 µL of broth samples was collected from the cartridge before drug dosing, at 6, 12, 24, 36, 48 h, and every 24 h until the end of the experiments. These samples underwent centrifugation at 15,770 × g for 10 minutes. After discarding the supernatant, the sediment was resuspended with an equal amount of sterile saline to minimize any potential residual bactericidal effect of meropenem. The resuspension solution was then diluted 10-fold using sterile saline. Subsequently, 100 µL of the appropriately diluted resuspension solution was evenly spread on the surfaces of blank plates (for counting total bacteria) and medicated plates (containing 3× MIC of meropenem, for counting drug-resistant bacteria) for incubation, following standard colony-counting principles.

RESULTS

In vitro susceptibility testing

The MIC values for meropenem against Pseudomonas aeruginosa ATCC 9027 and ATCC 27853 were determined to be 0.125 and 0.5 µg/mL, respectively, meeting the quality control criteria of CLSI standards.

Setup and validation of the HFIM

The simulated and observed concentration-time profiles following a single dose of 1.72 mg meropenem in the HFIM system are depicted in Fig. 3. Notably, there was excellent concordance between the measured concentrations in the ECS and central reservoir with the theoretical simulations. The observed peak concentration and area under the curve fell within the range of 80%–120% of the theoretical values. This robust agreement attests to the HFIM model’s accurate reproduction of meropenem’s pharmacokinetics in critically ill patients, affirming the successful construction of the in vitro HFIM.

Fig 3.

Fig 3

Open in a new tab

The observed (central reservoir and extracapillary space) versus simulated concentrations after a single intravenous dose of 1.72 mg meropenem over 0.5 h in the HFIM system.

Bactericidal capacity and suppression of drug resistance studies

The three treatment groups (0.14 mg q12h, 1.72 mg q12h, and 6.32 mg q12h) achieving different PK/PD targets (40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC) exhibited diverse concentration-time profiles. The observed concentrations closely aligned with the simulations consistently over the entire study period for the 100% fT > 4× MIC group. In contrast, for the 40% fT > MIC and 100% fT > MIC groups, this alignment was observed specifically within the initial 3–5 days (Fig. 2). The concentrations of meropenem within the 40% fT > MIC (5 h post-dosing) and 100% fT > MIC (trough level) target groups became undetectable after the third and fifth day, respectively, falling below the lower limit of quantitation.

The dynamics of total and resistant populations of Pseudomonas aeruginosa in both the control group and PK/PD target groups during continuous administration are depicted in Fig. 4. In the control group, total and resistant bacterial concentrations remained constant at 1 × 109 and 1 × 103 CFU/mL, respectively, throughout the observation period. All PK/PD target groups exhibited a rapid bactericidal effect within 6 h following the first administration. However, the 40% fT > MIC and 100% fT > MIC groups experienced a resurgence in both total and resistant bacterial concentrations after 6 and 12 h, respectively. Total bacterial concentrations in these groups increased to levels equivalent to those of the control group by 24 and 96 h, respectively. Similarly, resistant bacterial concentrations in these groups exceeded those of the control group after 12 and 36 h, respectively. These findings indicate that the bacterial populations in the 40% fT > MIC and 100% fT > MIC target groups exhibited initial inhibition followed by proliferation, leading to the emergence of drug-resistant subpopulations under drug pressure. Additionally, the antibacterial effect of the 100% fT > MIC target group exceeded that of the 40% fT > MIC target group, indicating superior bactericidal efficacy. However, both regimens failed to prevent the development of drug resistance with prolonged administration. In contrast, the 100% fT > 4× MIC group demonstrated continuous inhibition of bacterial growth, maintaining total bacterial concentrations between 1 × 102 and 1 × 104 CFU/mL throughout the administration period. That is, the target group of 100% fT > 4× MIC successfully suppressed the emergence of resistant subpopulations while concurrently demonstrating enhanced bactericidal efficacy.

Fig 4.

Fig 4

Open in a new tab

The dynamics of total (A) and drug-resistant (B) Pseudomonas aeruginosa ATCC 9027 populations in the control group and three treatment groups attaining different PK/PD targets.

DISCUSSION

Bacterial resistance consistently presents a formidable challenge in antibiotic use. The indiscriminate application of antibiotics has intensified the severity of bacterial resistance in clinical settings (28). This study leveraged the HFIM to scrutinize meropenem’s efficacy in killing bacteria and inhibiting resistance during extended administration against Pseudomonas aeruginosa, focusing on three prevalent PK/PD targets (40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC).

The HFIM was chosen for its ability to accurately simulate drug pharmacokinetics within the body, despite its limitation in replicating the innate immune response (29). However, given our focus on critically ill sepsis patients with compromised immune functions, the absence of immune functionality in the HFIM allowed for a more accurate representation of these patients’ specific conditions. During the experiments, we deliberately introduced a 1 × 108 CFU/mL inoculum of Pseudomonas aeruginosa into the HFIM, mirroring the intensified infection state observed in clinical sepsis patients. This intentional choice also elevated the chances of spontaneous resistant mutant bacteria emergence in Pseudomonas aeruginosa, facilitating subsequent investigations into bacterial resistance (30). Our results revealed that the blank control group exhibited concentrations of spontaneous resistant mutant bacteria at 1 × 103 CFU/mL with a mutation frequency of 1 × 10−6, effectively replicating the baseline of resistant mutant subpopulations in sepsis patients (31). The experiments spanned a 10-day administration period, aligning with the authentic treatment timeline of meropenem in sepsis patients. This duration allowed for a comprehensive exploration of both the bactericidal effectiveness and the capacity to curtail the proliferation of spontaneous resistant mutant bacteria across each PK/PD target regimen.

The pharmacokinetic results from the different PK/PD target regimens illustrated that the measured concentrations within the 100% fT > 4× MIC group perfectly matched the anticipated pharmacokinetic curve. However, in the 40% fT > MIC and 100% fT > MIC groups, meropenem concentrations (5 h post-dosing for 40% fT > MIC group and trough level for 100% fT > MIC group) fell below the lower limit of quantitation after the third and fifth day, respectively. The observed meropenem concentrations falling below the lower limit of quantitation are hypothesized to correlate with the proliferation of resistant mutant bacteria. These mutations prompt the production of enzymes that confer resistance to the drug, consequently leading to meropenem degradation. This is supported by the evident transformation of bacterial populations in the 40% fT > MIC and 100% fT > MIC target groups into drug-resistant mutant bacteria within 24 and 96 h, respectively. This transition leads to the emergence of enzymes capable of degrading the drug. Further in vitro susceptibility testing on the sampled bacteria at 120 and 240 h revealed an increase in MIC to 2 µg/mL for both the 40% and 100% fT > MIC target groups, compared to the control (MIC at 0.125 µg/mL). Resistant Pseudomonas aeruginosa may produce metallo-β-lactamases (MBLs), Ambler class A β-lactamases, or Ambler class D β-lactamases, which are able to hydrolyze carbapenems including meropenem (28). To explore the potential resistant mechanisms for the studied Pseudomonas aeruginosa, a phenotypic detection method employing combined-disc tests of meropenem alone and with phenylboronic acid (PBA) or ethylenediaminetetraacetic acid (EDTA) or both PBA and EDTA was evaluated for the detection of carbapenemase production and the differentiation of Ambler class A β-lactamases and MBL (32). Our results revealed that resistant Pseudomonas aeruginosa did not generate MBLs or class A carbapenemases. As a result, we hypothesized that the resistant Pseudomonas aeruginosa might produce class D carbapenemases, which cannot be detected by the aforementioned phenotypic detection method. The resistant enzyme-induced drug degradation was also observed in an HFIM study of sulbactam, where sulbactam concentrations within the cartridge were lower than predicted, attributed to the accumulation of β-lactamase, leading to drug degradation (33).

The pharmacodynamic findings showcased a strong bactericidal effect (>3 × log10 CFU/mL) within 6 h following initial administration for each specific PK/PD target regimen. However, as the dosing duration extended, noticeable variations emerged among the distinct PK/PD target groups regarding their capacity to restrain the proliferation of drug-resistant bacteria. Our study revealed that achieving a 100% fT > 4× MIC target is crucial to effectively suppress the development of drug resistance to meropenem against Pseudomonas aeruginosa. This finding emphasizes the significance of 100% fT > 4–6× MIC in mitigating bacterial resistance to β-lactam antibiotics, particularly carbapenems, as evidenced by the in vitro static time-killing experiment (34). Earlier HFIM studies on meropenem primarily focused on the bactericidal effects of the combined therapy of meropenem with other antibiotics (e.g., tobramycin, amikacin, and ciprofloxacin) (35–39). There is one relevant HFIM study that identified a target of 100% fT >6 × MIC for meropenem monotherapy to suppress the emergence of drug-resistant subpopulations in Pseudomonas aeruginosa (17). In contrast to previous studies, our work stands out as the first attempt to directly compare the bactericidal efficacy and the ability to suppress drug resistance across various clinical PK/PD targets (40% fT >MIC, 100% fT >MIC, and 100% fT >4 × MIC) of meropenem when used as a monotherapy. This was achieved by simulating meropenem pharmacokinetics in critically ill sepsis patients with differently designed meropenem dosing regimens in HFIM.

In a clinical context, for critically ill patients, achieving the PK/PD targets of 40% fT > MIC and 100% fT > MIC was feasible with meropenem dosages ranging from 0.5 to 1 g administered every 8 h, using a 30–60-minute infusion (40, 41). Furthermore, the target of 100% fT > 4× MIC could be attained with a meropenem dosage of 2 g every 8 h, administered through an extended infusion lasting 3 h (26). While our study demonstrated superior microbiological response with the 100% fT > 4× MIC target compared to 40% fT > MIC and 100% fT > MIC targets, this improvement may not directly translate to clinical benefits. For instance, a randomized clinical trial assessing the composite outcome of mortality and the emergence of pandrug-resistant or extensively drug-resistant bacteria at day 28 among critically ill sepsis patients showed no significant differences between intermittent and continuous administration of meropenem (42).

We acknowledge the potential differences between ATCC strains and clinical isolates of Pseudomonas aeruginosa. The choice of the sensitive ATCC 9027 wild-type strain in our study was aimed at facilitating the development of meropenem dosing regimens to assess the bactericidal efficacy and resistance suppression of representative PK/PD targets (40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC). Future investigations might explore the utilization of clinical isolates of Pseudomonas aeruginosa for comparative analysis. Additionally, we chose 6 h as the initial sampling time point for our experiment, considering the challenges of dense sampling and bacteria measurement in the early hours. However, we acknowledge the value of assessing the initial bactericidal effect by collecting samples between 1 and 2 h post-bacteria injection for a more comprehensive understanding.

Conclusion

In summary, our HFIM experiments, simulating meropenem pharmacokinetics in critically ill sepsis patients, highlighted that achieving a target of 100% fT > 4× MIC is notably more effective for meropenem against Pseudomonas aeruginosa. This target exhibited superior capabilities in preventing the emergence of drug-resistant subpopulations compared to 40% fT > MIC and 100% fT > MIC.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (grant numbers: 82073940 and 82373965) and the Science and Technology Innovation Program of Hunan Province (grant number: 2022RC1229).

X.Z. and Y.W. performed the study, analyzed the data, and prepared the first draft of the manuscript. S.L. reviewed the manuscript. F.X. and H.Y. conceived the idea, reviewed, and approved the final version of the manuscript.

Contributor Information

Feifan Xie, Email: feifan.xie@csu.edu.cn.

Hanxi Yi, Email: hanxiyi2022@csu.edu.cn.

James E. Leggett, Providence Portland Med Ctr, Portland, USA

REFERENCES

  • 1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche J-D, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent J-L, Angus DC. 2016. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315:801. doi: 10.1001/jama.2016.0287 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Fleischmann-Struzek C, Mellhammar L, Rose N, Cassini A, Rudd KE, Schlattmann P, Allegranzi B, Reinhart K. 2020. Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis. Intensive Care Med 46:1552–1562. doi: 10.1007/s00134-020-06151-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Arefian H, Heublein S, Scherag A, Brunkhorst FM, Younis MZ, Moerer O, Fischer D, Hartmann M. 2017. Hospital-related cost of sepsis: a systematic review. J Infect 74:107–117. doi: 10.1016/j.jinf.2016.11.006 [DOI] [PubMed] [Google Scholar]
  • 4. Arina P, Singer M. 2021. Pathophysiology of sepsis. Curr Opin Anaesthesiol 34:77–84. doi: 10.1097/ACO.0000000000000963 [DOI] [PubMed] [Google Scholar]
  • 5. Doganyigit Z, Eroglu E, Akyuz E. 2022. Inflammatory mediators of cytokines and chemokines in sepsis: from bench to bedside. Hum Exp Toxicol 41:9603271221078871. doi: 10.1177/09603271221078871 [DOI] [PubMed] [Google Scholar]
  • 6. Salomão R, Ferreira BL, Salomão MC, Santos SS, Azevedo LCP, Brunialti MKC. 2019. Sepsis: evolving concepts and challenges. Braz J Med Biol Res 52:e8595. doi: 10.1590/1414-431X20198595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Torres LK, Pickkers P, van der Poll T. 2022. Sepsis-induced immunosuppression. Annu Rev Physiol 84:157–181. doi: 10.1146/annurev-physiol-061121-040214 [DOI] [PubMed] [Google Scholar]
  • 8. Zhang H, Zhang Y, Wu J, Li Y, Zhou X, Li X, Chen H, Guo M, Chen S, Sun F, Mao R, Qiu C, Zhu Z, Ai J, Zhang W. 2020. Risks and features of secondary infections in severe and critical ill COVID-19 patients. Emerg Microbes Infect 9:1958–1964. doi: 10.1080/22221751.2020.1812437 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Abdul-Aziz MH, Brady K, Cotta MO, Roberts JA. 2022. Therapeutic drug monitoring of antibiotics: defining the therapeutic range. Ther Drug Monit 44:19–31. doi: 10.1097/FTD.0000000000000940 [DOI] [PubMed] [Google Scholar]
  • 10. Gupta R, Al-Kharji NMSA, Alqurafi MA, Nguyen TQ, Chai W, Quan P, Malhotra R, Simcox BS, Mortimer P, Brammer Basta LA, Rohde KH, Buynak JD. 2021. Atypically modified carbapenem antibiotics display improved antimycobacterial activity in the absence of β-lactamase inhibitors. ACS Infect Dis 7:2425–2436. doi: 10.1021/acsinfecdis.1c00185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Barbier F, Pommier C, Essaied W, Garrouste-Orgeas M, Schwebel C, Ruckly S, Dumenil A-S, Lemiale V, Mourvillier B, Clec’h C, Darmon M, Laurent V, Marcotte G, Lucet J-C, Souweine B, Zahar J-R, Timsit J-F. 2016. Colonization and infection with extended-spectrum β-lactamase-producing Enterobacteriaceae in ICU patients: what impact on outcomes and carbapenem exposure? J Antimicrob Chemother 71:1088–1097. doi: 10.1093/jac/dkv423 [DOI] [PubMed] [Google Scholar]
  • 12. El-Gamal MI, Brahim I, Hisham N, Aladdin R, Mohammed H, Bahaaeldin A. 2017. Recent updates of carbapenem antibiotics. Eur J Med Chem 131:185–195. doi: 10.1016/j.ejmech.2017.03.022 [DOI] [PubMed] [Google Scholar]
  • 13. Craig WA. 1998. Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin Infect Dis 26:1–10; doi: 10.1086/516284 [DOI] [PubMed] [Google Scholar]
  • 14. Ikawa K, Morikawa N, Ikeda K, Ohge H, Sueda T. 2008. Development of breakpoints of carbapenems for intraabdominal infections based on pharmacokinetics and pharmacodynamics in peritoneal fluid. J Infect Chemother 14:330–332. doi: 10.1007/s10156-008-0624-1 [DOI] [PubMed] [Google Scholar]
  • 15. Heffernan AJ, Sime FB, Lipman J, Roberts JA. 2018. Individualising therapy to minimize bacterial multidrug resistance. Drugs 78:621–641. doi: 10.1007/s40265-018-0891-9 [DOI] [PubMed] [Google Scholar]
  • 16. Alshaer MH, Maranchick N, Alexander KM, Manigaba K, Shoulders BR, Felton TW, Mathew SK, Peloquin CA. 2023. Beta-lactam target attainment and associated outcomes in patients with bloodstream infections. Int J Antimicrob Agents 61:106727. doi: 10.1016/j.ijantimicag.2023.106727 [DOI] [PubMed] [Google Scholar]
  • 17. Tam VH, Schilling AN, Neshat S, Poole K, Melnick DA, Coyle EA. 2005. Optimization of meropenem minimum concentration/MIC ratio to suppress in vitro resistance of Pseudomonas aeruginosa. Antimicrob Agents Chemother 49:4920–4927. doi: 10.1128/AAC.49.12.4920-4927.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Mattoes HM, Kuti JL, Drusano GL, Nicolau DP. 2004. Optimizing antimicrobial pharmacodynamics: dosage strategies for meropenem. Clin Ther 26:1187–1198. doi: 10.1016/s0149-2918(04)80001-8 [DOI] [PubMed] [Google Scholar]
  • 19. Li C, Du X, Kuti JL, Nicolau DP. 2007. Clinical pharmacodynamics of meropenem in patients with lower respiratory tract infections. Antimicrob Agents Chemother 51:1725–1730. doi: 10.1128/AAC.00294-06 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Roberts JA, Paul SK, Akova M, Bassetti M, De Waele JJ, Dimopoulos G, Kaukonen K-M, Koulenti D, Martin C, Montravers P, Rello J, Rhodes A, Starr T, Wallis SC, Lipman J, DALI Study . 2014. DALI: defining antibiotic levels in intensive care unit patients: are current β-lactam antibiotic doses sufficient for critically ill patients? Clin Infect Dis 58:1072–1083. doi: 10.1093/cid/ciu027 [DOI] [PubMed] [Google Scholar]
  • 21. Dinh TD, Nguyen HN, Le BH, Nguyen TTT, Nguyen HTL. 2022. Population-based pharmacokinetics and dose optimization of imipenem in Vietnamese critically-ill patients. Infect Drug Resist 15:4575–4583. doi: 10.2147/IDR.S373348 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Wang Y, Liu L, Wu Q, Yin Q, Xie F. 2022. Defining exposure predictors of meropenem that are associated with improved survival for severe bacterial infection: a preclinical PK/PD study in sepsis rat model. Antibiotics 11:1660. doi: 10.3390/antibiotics11111660 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Sadouki Z, McHugh TD, Aarnoutse R, Ortiz Canseco J, Darlow C, Hope W, van Ingen J, Longshaw C, Manissero D, Mead A, Pelligand L, Phee L, Readman J, Ruth MM, Standing JF, Stone N, Wey EQ, Kloprogge F. 2021. Application of the hollow fibre infection model (HFIM) in antimicrobial development: a systematic review and recommendations of reporting. J Antimicrob Chemother 76:2252–2259. doi: 10.1093/jac/dkab160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. McSharry JJ, Drusano GL. 2011. Antiviral pharmacodynamics in hollow fibre bioreactors. Antivir Chem Chemother 21:183–192. doi: 10.3851/IMP1770 [DOI] [PubMed] [Google Scholar]
  • 25. Cadwell JJS. 2018. The hollow fiber infection model: principles and practice. Adv Antibiotics Antibodies 07. doi: 10.4172/2327-5073-C2-036 [DOI] [Google Scholar]
  • 26. Zhao YC, Zou Y, Xiao YW, Wang F, Zhang BK, Xiang DX, Yu F, Luo H, Sandaradura I, Yan M. 2022. Does prolonged infusion time really improve the efficacy of meropenem therapy? A prospective study in critically ill patients. Infect Dis Ther 11:201–216. doi: 10.1007/s40121-021-00551-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Xie F, Liu L, Wang Y, Peng Y, Li S. 2022. An UPLC-PDA assay for simultaneous determination of seven antibiotics in human plasma. J Pharm Biomed Anal 210:114558. doi: 10.1016/j.jpba.2021.114558 [DOI] [PubMed] [Google Scholar]
  • 28. Botelho J, Grosso F, Peixe L. 2019. Antibiotic resistance in Pseudomonas aeruginosa - mechanisms, epidemiology and evolution. Drug Resist Updat 44:100640. doi: 10.1016/j.drup.2019.07.002 [DOI] [PubMed] [Google Scholar]
  • 29. Ruth MM, Raaijmakers J, van den Hombergh E, Aarnoutse R, Svensson EM, Susanto BO, Simonsson USH, Wertheim H, Hoefsloot W, van Ingen J. 2022. Standard therapy of Mycobacterium avium complex pulmonary disease shows limited efficacy in an open source hollow fibre system that simulates human plasma and epithelial lining fluid pharmacokinetics. Clin Microbiol Infect 28:448. doi: 10.1016/j.cmi.2021.07.015 [DOI] [PubMed] [Google Scholar]
  • 30. Li X, Wang L, Zhang XJ, Yang Y, Gong WT, Xu B, Zhu YQ, Liu W. 2014. Evaluation of meropenem regimens suppressing emergence of resistance in Acinetobacter baumannii with human simulated exposure in an in vitro intravenous-infusion hollow-fiber infection model. Antimicrob Agents Chemother 58:6773–6781. doi: 10.1128/AAC.03505-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Drusano GL, Neely MN, Yamada WM, Duncanson B, Brown D, Maynard M, Vicchiarelli M, Louie A. 2018. The combination of fosfomycin plus meropenem is synergistic for Pseudomonas aeruginosa PAO1 in a hollow-fiber infection model. Antimicrob Agents Chemother 62:e01682-18. doi: 10.1128/AAC.01682-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Tsakris A, Poulou A, Pournaras S, Voulgari E, Vrioni G, Themeli-Digalaki K, Petropoulou D, Sofianou D. 2010. A simple phenotypic method for the differentiation of metallo-beta-lactamases and class A KPC carbapenemases in Enterobacteriaceae clinical isolates. J Antimicrob Chemother 65:1664–1671. doi: 10.1093/jac/dkq210 [DOI] [PubMed] [Google Scholar]
  • 33. Tanudra MA. 2018. Leveraging the dynamic in vitro hollow fiber infection model in determining the optimal clinical dose of β-lactam and β-lactamase inhibitor combinations. Master Thesis. Harvard University, United States, Massachusetts. https://dash.harvard.edu/bitstream/handle/1/42004045/TANUDRA-DOCUMENT-2018.pdf?sequence=1. [Google Scholar]
  • 34. Manduru M, Mihm LB, White RL, Friedrich LV, Flume PA, Bosso JA. 1997. In vitro pharmacodynamics of ceftazidime against Pseudomonas aeruginosa isolates from cystic fibrosis patients. Antimicrob Agents Chemother 41:2053–2056. doi: 10.1128/AAC.41.9.2053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Rees VE, Yadav R, Rogers KE, Bulitta JB, Wirth V, Oliver A, Boyce JD, Peleg AY, Nation RL, Landersdorfer CB. 2018. Meropenem combined with ciprofloxacin combats hypermutable Pseudomonas aeruginosa from respiratory infections of cystic fibrosis patients. Antimicrob Agents Chemother 62:e01150-18. doi: 10.1128/AAC.01150-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Landersdorfer CB, Rees VE, Yadav R, Rogers KE, Kim TH, Bergen PJ, Cheah SE, Boyce JD, Peleg AY, Oliver A, Shin BS, Nation RL, Bulitta JB. 2018. Optimization of a meropenem-tobramycin combination dosage regimen against hypermutable and nonhypermutable Pseudomonas aeruginosa via mechanism-based modeling and the hollow-fiber infection model. Antimicrob Agents Chemother 62:e02055-17. doi: 10.1128/AAC.02055-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Agyeman AA, Rogers KE, Tait JR, Bergen PJ, Kirkpatrick CM, Wallis SC, Bulitta JB, Paterson DL, Lipman J, Nation RL, Roberts JA, Landersdorfer CB. 2021. Evaluation of meropenem-ciprofloxacin combination dosage regimens for the pharmacokinetics of critically ill patients with augmented renal clearance. Clin Pharmacol Ther 109:1104–1115. doi: 10.1002/cpt.2191 [DOI] [PubMed] [Google Scholar]
  • 38. Yadav R, Bergen PJ, Rogers KE, Kirkpatrick CMJ, Wallis SC, Huang Y, Bulitta JB, Paterson DL, Lipman J, Nation RL, Roberts JA, Landersdorfer CB. 2019. Meropenem-tobramycin combination regimens combat carbapenem-resistant Pseudomonas aeruginosa in the hollow-fiber infection model simulating augmented renal clearance in critically ill patients. Antimicrob Agents Chemother 64:e01679-19. doi: 10.1128/AAC.01679-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Avent ML, McCarthy KL, Sime FB, Naicker S, Heffernan AJ, Wallis SC, Paterson DL, Roberts JA. 2022. Evaluating mono- and combination therapy of meropenem and amikacin against Pseudomonas aeruginosa bacteremia in the hollow-fiber infection model. Microbiol Spectr 10:e0052522. doi: 10.1128/spectrum.00525-22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Minichmayr IK, Kappetein S, Brill MJE, Friberg LE. 2022. Model-informed translation of in vitro effects of short-, prolonged- and continuous-infusion meropenem against Pseudomonas aeruginosa to clinical settings. Antibiotics (Basel) 11:1036. doi: 10.3390/antibiotics11081036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Onichimowski D, Będźkowska A, Ziółkowski H, Jaroszewski J, Borys M, Czuczwar M, Wiczling P. 2020. Population pharmacokinetics of standard-dose meropenem in critically ill patients on continuous renal replacement therapy: a prospective observational trial. Pharmacol Rep 72:719–729. doi: 10.1007/s43440-020-00104-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Monti G, Bradić N, Marzaroli M, Konkayev A, Fominskiy E, Kotani Y, Likhvantsev VV, Momesso E, Nogtev P, Lobreglio R, et al. 2023. Continuous vs intermittent meropenem administration in critically ill patients with sepsis: the MERCY randomized clinical trial. JAMA 330:141. doi: 10.1001/jama.2023.10598 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Antimicrobial Agents and Chemotherapy are provided here courtesy of American Society for Microbiology (ASM)

RESOURCES