Population Pharmacokinetics/Pharmacodynamics and Clinical Outcomes of Meropenem in Critically Ill Patients

ABSTRACT

Several pathophysiological changes can alter meropenem pharmacokinetics in critically ill patients, thereby increasing the risk of subtherapeutic concentrations and affecting therapeutic outcomes. This study aimed to characterize the population pharmacokinetic (PPK) parameters of meropenem, evaluate the relationship between the pharmacokinetic/pharmacodynamic index of meropenem and treatment outcomes, and evaluate the different dosage regimens that can achieve 40%, 75%, and 100% of the dosing interval for which the free plasma concentrations remain above the MIC of the pathogens (fT_>MIC) targets. Critically ill adult patients treated with meropenem were recruited for this study. Five blood samples were collected from each patient. PPK models were developed using a nonlinear mixed-effects modeling approach, and the final model was subsequently used for Monte Carlo simulations to determine the optimal dosage regimens. A total of 247 concentrations from 52 patients were available for analysis. The two-compartment model with linear elimination adequately described the data. The mean PPK parameters were clearance (CL) of 4.8 L/h, central volume of distribution (V_C) of 11.4 L, peripheral volume of distribution (V_P) of 14.6 L, and intercompartment clearance of 10.5 L/h. Creatinine clearance was a significant covariate affecting CL, while serum albumin level and shock status were factors influencing V_C and V_P, respectively. Although 75% of the drug-resistant infection patients had fT_>MIC values of >40%, approximately 83% of them did not survive the infection. Therefore, 40% fT_>MIC might not be sufficient for critically ill patients, and a higher target, such as 75 to 100% fT_>MIC, should be considered for optimizing therapy. A 75% fT_>MIC could be reached using approved doses administered via a 3-h infusion.

INTRODUCTION

Severe sepsis and septic shock are common conditions and prominent causes of morbidity, mortality, and economic burden to patients in intensive care units (ICUs) (1, 2). Early initiation of appropriate antimicrobial therapy has been demonstrated to be the most effective intervention for reducing mortality in these patients (3, 4).

Meropenem is a broad-spectrum carbapenem antibiotic with potent activity against Gram-negative bacilli, Gram-positive cocci, and anaerobic bacteria. It is a small hydrophilic molecule with a low level of protein binding (<2%) and is excreted mainly through the kidney with a short half-life (approximately 1 h) (5). This antibiotic agent is frequently used for the empirical treatment of sepsis and other severe nosocomial infections in ICUs. The antibacterial activity of meropenem depends on the percentage of the dosing interval for which the free plasma concentrations remain above the MIC of the pathogens (fT_>MIC) (6, 7). Generally, a fT_>MIC of at least 40 to 50% of the dosing interval is considered sufficient for carbapenems. However, a higher fT_>MIC target may be required in critically ill patients (8, 9). Optimizing the meropenem dosage regimen to achieve this target is a significant challenge for physicians attending to patients in ICUs. This is due to sepsis-related pathophysiological changes that may alter meropenem pharmacokinetics, including volume of distribution and total drug clearance, resulting in a highly variable plasma concentration.

Although the pharmacokinetic parameters of meropenem in critically ill patients have been widely studied, most studies have been conducted on small patient populations (10–15). The small cohort size may impact accuracy and is unlikely to capture all the variability of pharmacokinetic parameters in critically ill patients. Moreover, clinical data on the relationship between meropenem exposure and treatment outcomes is limited. Therefore, the objectives of this study were to characterize the population pharmacokinetic parameters of meropenem in critically ill patients, evaluate the relationship between meropenem exposure and clinical outcomes, and evaluate the different dosage regimens that can achieve 40%, 75%, and 100% fT_>MIC targets.

RESULTS

Patient characteristics.

Fifty-two critically ill patients were included in this study, 48 of whom were in the medical ICU, and the remaining four patients were in the surgical ICU. The median (range) age, weight, and creatinine clearance estimated by the Cockcroft-Gault equation (CL_CR-CG) were 63 years (29 to 91 years), 61.5 kg (27.8 to 86 kg), and 44.6 mL/min (6.2 to 161.1 mL/min), respectively. The majority of the patients had pneumonia (63%) and received meropenem doses ranging from 1 to 6 g/day. The patient characteristics are shown in Table 1.

TABLE 1.

Demographics and clinical characteristics of the study population

Characteristica	Patients (n = 52)b
Male	31 (59.6)
Age (yrs)	63 (48.0 to 74.0)
Body wt (kg)	61.5 (53.4 to 69.8)
Body mass index (kg/m2)	22.9 (20.6 to 25.4)
APACHE II score	20 (14 to 23)
SOFA score	8 (6 to 11)
Inotropic or vasopressor used	20 (38.5)
Mechanical ventilator	46 (88.5)
Sepsisc	51 (98.1)
Septic shockc	18 (34.6)
CLCR-CG (mL/min)	44.6 (24.2 to 80.7)
Serum lactate (mmol/L)	3.4 (1.7 to 5.7)
Serum albumin (g/dL)	2.4 (2.0 to 2.9)
Hypoalbuminemiad	28 (53.8)
Cumulative fluid balance (L)e	3.6 (1.7 to 5.9)
Primary infection site
Respiratory	33 (63.5)
Intraabdominal	8 (15.4)
Genitourinary	6 (11.5)
Others	5 (9.6)
Meropenem dosage regimens
LD 2 g, 1 g q8 h	24 (46.2)
LD 2 g, 1 g q12 h	10 (19.2)
LD 2 g, 0.5 g q12 h	6 (11.5)
Others	12 (23.1)

^aCL_CR-CG, creatinine clearance estimated using Cockcroft-Gault formula based on ideal body weight; APACHE II, acute physiology and chronic health evaluation II; SOFA, sequential organ failure assessment score; LD, loading dose; q, every.

^bData are expressed as n (%) or median (interquartile range).

^cPatient diagnosed according to sepsis-3 criteria.

^dHypoalbuminemia was defined as a serum albumin level less than 2.5 g/dL.

^eThe sum of daily fluid balance from the first day of intensive care admission until plasma meropenem was measured.

Population pharmacokinetic (PK) modeling.

In total, 256 unbound meropenem concentrations were obtained from the 52 patients. Of these, 7 (3%) and 9 (3%) concentrations were below the lower limit of quantitation (LLOQ) and lower limit of detection (LOD), respectively. All detectable concentrations, including points below the LLOQ, were included as continuous data, while concentrations below the LOD were discarded. As a result, the remaining 247 concentrations ranging from 0.12 to 127.7 mg/L were analyzed.

A two-compartment model with first-order elimination best described the meropenem concentration-time profiles. The proportional error model adequately described the residual variability, whereas the combined proportional and additive error model did not perform significantly better. Interindividual variability (IIV) was implemented for all PK parameters. However, the IIV for intercompartmental clearance was small and was therefore fixed at zero. Regarding covariate analysis, implementing CL_CR-CG calculated using ideal body weight (IBW) on meropenem clearance (CL) provided the largest reduction in objective function value (OFV) compared to other renal function markers. Therefore, it was included in the final model. Serum albumin (ALB) and shock state significantly affected the volume of distribution in the central (V_C) and peripheral (V_P) compartments, respectively. The addition of ALB to V_C resulted in a decrease in the IIV of V_C to near zero; therefore, it was fixed to zero in the final model. When the effects of these covariates were combined, no other covariates showed significance. The backward deletion process did not remove the covariates. Therefore, these covariates were retained in the model. The population pharmacokinetic (PPK) parameters of the base and final models are summarized in Table 2. In the final model, the individual PK parameters were best described using the following equations:

$$\text{CL\hspace{0.17em}}\left(\text{L}/\text{h}\right)=\text{\hspace{0.17em}5}.\text{16\hspace{0.17em}}\times \text{\hspace{0.17em}(1\hspace{0.17em}}+\text{\hspace{0.17em}0.016\hspace{0.17em}}\times {\text{\hspace{0.17em}[CL}}_{\text{CR}-\text{CG}}-\text{\hspace{0.17em}5}0])$$

$${\mathrm{V}}_{\text{C}}\left(\text{L}\right)=\text{\hspace{0.17em}11}.\text{1\hspace{0.17em}}\times \text{\hspace{0.17em}(1\hspace{0.17em}}-\hspace{0.17em}0.\text{255\hspace{0.17em}}\times [\text{ALB}\hspace{0.17em}-\hspace{0.17em}\text{2}.\text{5}])$$

$${\mathrm{V}}_{\text{P}}(\text{L})=10.3+11.3(\text{if\hspace{0.17em}shock)}$$

TABLE 2.

Population pharmacokinetic parameters of meropenem from the base and final model

	Base model (OFV = 1,382.7)	Final modelb (OFV = 1,334.9)
Parametera	Estimate (% RSEa)	Estimate (% RSEa)	% Shra	Median (95% CIa) of bootstrap estimate
Fixed-effect parameters
CL (L/h)	4.8 (11.9)	5.16 (9.8)		5.19 (4.22 to 6.21)
θ1		0.016 (8.9)		0.016 (0.012 to 0.018)
VC (L)	11.4 (11.5)	11.1 (10.8)		11.1 (7.89 to 12.79)
θ2		−0.255 (30.5)		−0.258 (−0.418 to −0.103)
VP (L)	14.6 (12.0)	10.3 (31.7)		10.49 (7.35 to 14.72)
θ3		11.3 (43.1)		11.42 (3.54 to 22.57)
Q (L/h)	10.5 (30.4)	9.37 (38.4)		9.40 (6.06 to 22.57)
Interindividual variability (IIV, % CVa)
IIV on CL	86.1 (9.5)	60.5 (11.4)	1.0	59.4 (45.7 to 72.8)
IIV on VC	30.0 (38.9)	Fixed to 0
IIV on VP	63.7 (18.8)	47.7 (22.1)	36.3	45.7 (23.7 to 66.5)
IIV on Q	Fixed to 0
Residual variability (%)
Proportional	24.1 (11.0)	26.0 (8.3)	13	25.6 (21.1 to 29.7)

^a% RSE, percentage of relative standard error; % Shr, percentage of shrinkage; % CV, percentage of coefficient of variation; OFV, minimum objective function value; CI, confidence interval; CL, total clearance; V_C, central volume of distribution; V_P, the peripheral volume of distribution; Q, intercompartment clearance; CL_CR-CG, creatinine clearance estimated by Cockcroft-Gault equation; ALB, serum albumin (g/dL); Shock, a clinical state requiring vasopressors or inotropes to maintain a mean arterial pressure greater than 65 mm Hg.

^bThe final PK model parameters:

$\text{CL\hspace{0.17em}}\left(\text{L/h}\right)\text{\hspace{0.17em}=5.16\hspace{0.17em}×\hspace{0.17em}}\left({\text{1}+\theta }_{\text{1}}{\text{×\hspace{0.17em}[CL}}_{\text{CR}-\text{CG}}-\hspace{0.17em}\text{50}]\right)$

${\mathrm{V}}_{\text{C}}\left(\text{L}\right)\text{\hspace{0.17em}=11.1\hspace{0.17em}×\hspace{0.17em}}\left({1+\theta }_{\text{2}}\text{\hspace{0.17em}×\hspace{0.17em}[ALB\hspace{0.17em}}-\text{2.5]}\right)$

${\mathrm{V}}_{\text{P}}\left(\text{L}\right)\text{\hspace{0.17em}=10}{\text{.3\hspace{0.17em}}+\theta }_{\text{3}}\text{\hspace{0.17em}×\hspace{0.17em}SHOCK}$

Goodness-of-fit plots (Fig. 1) indicated that the fit of the final model was reasonably good, with no obvious biases. The prediction-corrected visual predictive check (pcVPC) plot (Fig. 2) also confirmed the adequate predictive performance of the model, as the 5th, 50th, and 95th percentiles of the observed data laid within the 95% confidence interval of the corresponding percentiles of the model prediction.

FIG 1.

Goodness-of-fit plots of the final pharmacokinetic model. Observed meropenem concentration versus population-predicted (A) and individual-predicted (B) concentration. Conditional weighted residuals versus population predicted (C) and time after dose (D). Solid lines represent the line of identity, and the dashed line is the locally weighted smoothing (LOESS) line to indicate trends.

FIG 2.

Prediction-corrected visual predictive check (pcVPC) of the final model. Open circles are observed concentrations. The solid line represents the 50th percentiles of the observation, and dashed lines represent the 2.5th and 97.5th percentiles of the observations. The shaded areas are the 95% confidence intervals around the 2.5th, 50th, and 97.5th of the simulated data.

Microbiological finding.

Of the 52 patients, a pathogen was identified in 31 patients (60%). The most commonly isolated microorganisms were Klebsiella pneumoniae (33%), Escherichia coli (21%), Pseudomonas aeruginosa (15%), and Acinetobacter baumannii (9%) (Table 3). The median MIC values of meropenem for Enterobacteriaceae, P. aeruginosa, and A. baumannii were 0.023, 0.125, and 32 mg/L, respectively.

TABLE 3.

Microbiologic characteristics (86 isolates from 31 patients) and meropenem susceptibility

Pathogena	No. of isolates	Meropenem MIC range (mg/L)a
Klebsiella pneumoniae	12	0.023
Klebsiella pneumoniae (ESBL)	14	0.032 to 0.5
Klebsiella pneumoniae (CRE) Klebsiella pneumoniae (CRE)	2 1	≥32 12
Escherichia coli	15	0.01 to 0.023
Escherichia coli (ESBL)	3	0.023
Pseudomonas aeruginosa	10	0.064 to 1.0
Pseudomonas aeruginosa (CR-GNB)	3	≥32
Acinetobacter baumannii	2	0.38
Acinetobacter baumannii (CR-GNB)	6	≥32
Burkholderia pseudomallei	3	0.75
Enterococcus faecium	6	≥32
Enterobacter cloacae	1	0.032
Enterobacter aerogenes	1	0.047
Providencia stuartii	3	0.023
Stenotrophomonas maltophilia	2	ND
Moraxella catarrhalis	1	ND
Bacillus spp.	1	ND

^aESBL, extended-spectrum β-lactamases; CR-GNB, carbapenem-resistant Gram-negative bacteria; CRE, carbapenem-resistant Enterobacteriaceae; ND, not determined.

PK/pharmacodynamic (PD) analysis and treatment outcomes.

Although 31 patients had documented bacterial infections, only 20 were included in the clinical outcome assessment. Patients whose specific MIC data of causative pathogens were unavailable (n = 7) and those with infections caused by intrinsically meropenem-resistant pathogens (n = 4) were excluded from the analysis. The baseline clinical characteristics are shown in Table 4.

TABLE 4.

Comparison of baseline characteristics between clinical success and failure groups

	Clinical outcomeb		Survival during ICU admissionb
Characteristica	Success (n = 13)	Failure (n = 7)	Alive (n = 14)	Dead (n = 6)
Male	7 (53.8)	3 (42.9)	7 (50)	3 (50)
Age (yrs)	63.3 (55.2 to 76.5)	65.1 (63 to 71.6)	61.8 (46.4 to 75.7)	67.3 (64.0 to 72.8)
Body wt (kg)	62 (51 to 68)	60 (59 to 67.2)	59.5 (50.8 to 67.6)	62.5 (60 to 68.2)
APACHE IIc	20 (19 to 22)	28 (16.5 to 29.5)	20 (17.5 to 21.8)	28 (20.5 to 30.3)
SOFAc	9 (6 to 11)	10 (8 to 12)	8 (6.0 to 10.8)	10 (8.5 to 13.0)
Inotropic or vasopressor used	10 (76.9)	7 (100)	11 (78.6)	6 (100)
Mechanical ventilator	11 (84.6)	7 (100)	12 (85.7)	6 (100)
Sepsisd	13 (100)	7 (100)	14 (100)	6 (100)
Septic shockd	6 (46.2)	4 (57.1)	6 (42.9)	4 (66.7)
CLCR-CG (mL/min)	45.5 (38.6 to 84.5)	69.2 (30.5 to 82.1)	46.7 (39 to 84.3)	52.9 (27.4 to 77.6)
Serum lactate (mmol/L)	2.3 (1.6 to 6.1)	4 (1.8 to 7.1)	2.3 (1.3 to 5.6)	4.1 (2.5 to 8.5)
Hypoalbuminemia	9 (69.2)	6 (85.7)	10 (71.4)	5 (83.3)
Primary infection site
Respiratory	6 (46.1)	7 (100)	8 (57.1)	5 (83.3)
Genitourinary	3 (23.1)	0	3 (21.4)	0
Intraabdominal	2 (15.4)	0	1 (7.1)	1 (16.7)
Others	2 (15.4)	0	2 (14.3)	0
MIC (mg/L)	0.032 (0.023 to 32)e	32 (12 to 32)e	0.07 (0.023 to 32)e	32 (12 to 32)e
Meropenem monotherapy	12 (92.3)	2 (28.6)	13 (92.9)	1 (16.7)
Concomitant antimicrobials
Colistin	1 (7.7)	3 (42.8)	1 (7.1)	4 (66.7)
Vancomycin	0	1 (14.3)	0	1 (16.7)
Other	0	1 (14.3)	0	0

^aThe information was from the first day of infection.

^bData are expressed as n (%) or median (interquartile range), unless otherwise stated.

^cEvaluation on the first day of ICU admission.

^dPatient diagnosed according to sepsis-3 criteria.

^eMedian (minimum-maximum).

Overall, 90% (18/20) of patients achieved a PK/PD target of 40% fT_>MIC. For higher thresholds of 75% and 100% fT_>MIC target, only 70% (14/20) and 55% (11/20) of the patients achieved these targets. The average fT_>MIC and the proportion of patients reaching the fT_>MIC targets were higher in the clinical success group than in the failure group. Similar findings were observed in the survivor group. The results of the clinical outcome analyses are presented in Table 5.

TABLE 5.

Pharmacokinetic/pharmacodynamic indices and clinical outcomes of 20 critically ill patients

Parameter	Overall (n = 20)a	Clinical outcome			Survival during ICU admission
		Success (n = 13)a	Failure (n = 7)a	P value	Alive (n = 14)a	Dead (n = 6)a	P value
MIC (mg/L)	0.023 to 32	0.032 (0.023 to 32)	32 (12 to 32)	0.001	0.07 (0.023 to 32)	32 (12 to 32)	0.003
No. resistant pathogen (%)	8 (40)	1 (7.7)	7 (100)	0.001	2 (14.3)	6 (100)	0.001
%fT>MIC	100 (6.3 to 100)	100 (50 to 100)	81.3 (6.3 to 100)	0.017	100 (6.3 to 100)	85.6 (8.8 to 100)	0.279
fT>MIC ≥ 40%	18 (90)	13 (100)	5 (71.4)	0.111	13 (92.9)	5 (83.3)	0.521
fT>MIC ≥ 75%	14 (70)	10 (76.9)	4 (57.1)	0.613	10 (71.4)	4 (66.7)	1.000
fT>MIC = 100%	11 (55)	10 (76.9)	1 (14.3)	0.017	10 (71.4)	1 (16.7)	0.05

^aData are presented as median (minimum-maximum) or n (%).

Dosing optimization.

The simulation results indicated that all current standard dosage regimens provided a higher probability of achieving traditional 40% fT_>MIC target than 90% against pathogens, with MIC values ranging from 0.0625 to 2 mg/L. When considering the more aggressive target of 75% fT_>MIC, the standard doses could be achieved at an optimal probability of target attainment (PTA) for isolates with MIC values of ≤2 mg/L in patients with CL_CR-CG values of >25 mL/min when administered by extended infusion over 3 h (Fig. 3). For 100% fT_>MIC target, a larger dose is required to reach an optimal PTA. The meropenem regimens needed to achieve optimal PTA for 40% fT_>MIC, 75% fT_>MIC, and 100% fT_>MIC targets are reported in Table 6.

FIG 3.

Probability of target attainment (PTA) for meropenem regimens achieving 75% fT_>MIC target during 24 to 48 h after administration. Five groups were categorized according to creatinine clearance calculated by the Cockcroft and Gault equation (CL_CR-CG) as <10 mL/min (A), 10 to 25 mL/min (B), >25 to 50 mL/min (C), >50 to 90 mL/min (D), and >90 to 130 mL/min (E). (F) A histogram showing international MIC distribution of meropenem for Pseudomonas aeruginosa and Acinetobacter baumannii from the EUCAST database (October 2021) is shown. The horizontal dashed line denotes the PTA of 90%; LD, loading dose.

TABLE 6.

Meropenem dosing recommendation against pathogens with MIC values of ≤2 mg/L

CLCR-CG (mL/min)a	fT>MIC targeta
	40% fT>MIC	75% fT>MIC	100% fT>MIC
90.1 to 130	LD 2 g, 1 g q8h (II)b	LD 2 g, 1 g q6h (EI)	LD 2 g, 2 g q6h (EI)
50.1 to 90	LD 2 g, 1 g q8h (II)b	LD 2 g, 1 g q8h (EI)	LD 2 g, 2 g q6h (EI)
25.1 to 50	LD 2 g, 1 g q12h (II)b	LD 2 g, 1 g q12h (EI)	LD 2 g, 1 g q6h (EI)
10 to 25	LD 1 g, 0.5 g q12h (II)b	LD 1 g, 0.5 g q12h (II)b	LD 1g, 0.5 g q6h (II)
<10	LD 1 g, 0.5 g q24h (II)b	LD 1 g, 0.5 g q24h (II)b	LD 1 g, 0.5 g q8h (II)

^a%fT_>MIC, percentage of dosing interval where unbound meropenem concentration is above the MIC; CL_CR-CG, creatinine clearance estimated using standard Cockcroft-Gault formula based on ideal body weight; LD, loading dose; II, intermittent infusion (0.5 h); EI, extended infusion (3 h).

^bCurrent standard regimen according to drug label information.

DISCUSSION

The current study characterized PK properties and provided the dosage regimens of meropenem needed to achieve 40%, 75%, and 100% fT_>MIC targets in a critically ill population. A two-compartment model with first-order elimination adequately described the PK profiles of meropenem. The ICU patient population in this study was remarkably diverse in terms of clinical and biological characteristics, which contributed to the high IIV of the PK parameters in the model. The population mean CL in the current analysis was approximately 4.75 L/h, similar to that obtained in previous PK studies in which the PK of meropenem was quantified in patients with moderate to severe renal impairment (2.1 to 7.7 L/h) (12, 15–18). Consistent with the expectation that a large proportion of meropenem would be renally excreted, renal function was the main factor influencing meropenem CL. CL_CR-CG showed a better ability to predict meropenem CL than creatinine clearance estimated by the Jelliffe equation (CL_CR-JEL) or estimated glomerular filtration rate (GFR) using the four- and six-variable modification of diet in renal disease study equation (GFR_MDRD) or the chronic kidney disease epidemiology collaboration equation (GFR_EPI). The superiority of CL_CR-CG has been described in previous studies (16, 19). The average volume of distribution at steady state (V_SS = V_C + V_P) in the current study was 22.4 L, within the range of that previously reported in critically ill patients (range, 20.7 to 37.9 L). Hypotension had a significant effect on the V_P of meropenem. In patients with hypotension, the V_P of meropenem was increased by approximately 50%. In response to hypotension, a large volume of fluid resuscitation and inotropic agents are frequently prescribed to restore the mean arterial pressure. Intravenous fluid administration led to a significant increase in total body water, which substantially increased the volume of distribution (V_D). Low serum albumin levels were also found to increase the V_C significantly. When serum albumin levels decreased from 3.5 to 2.5 and 1.5 g/dL, the V_C of meropenem increased by 34% and 68%, respectively. An increase in V_D caused by hypoalbuminemia has been documented in several studies, particularly in highly protein-bound antibiotics (18, 20–23). Meropenem is a low protein binding antibiotic; therefore, it is unlikely that hypoalbuminemia would have a significant influence on meropenem V_D due to a change in drug binding. One possible explanation for this association is that it might be the pathophysiological consequence of hypoalbuminemia. By reducing intravascular oncotic pressure, hypoalbuminemia promotes fluid extravasation and tissue edema formation. Moreover, the loss of intravascular volume owing to hypoalbuminemia necessitates a large volume of fluid therapy to maintain tissue perfusion. Edema formation and aggressive fluid therapy contribute to an increase in the V_D of antibiotics. The hydrophilic nature of meropenem renders it sensitive to this phenomenon.

Carbapenems exhibit time-dependent antibacterial activity; that is, their antibacterial activity is best correlated with fT_>MIC. Generally, it has been suggested that the fT_>MIC of carbapenems should be at least 40 to 50% for an optimal antibactericidal effect (7). In this study, all patients with drug-susceptible infections had fT_>MIC values greater than 40%, and clinical response was observed in all patients. Seventy-five percent of the patients with resistant organisms also achieved this conservative target; however, approximately 83% did not survive the infection. This suggests that a target of 40% fT_>MIC might not be sufficient for critically ill patients. When fT_>MIC reached 75% and 100%, the clinical success rates increased to 71% and 91%, respectively. The proportion of patients who achieved 75% fT_>MIC or 100% fT_>MIC was higher in the clinical success group than in the failure group. There were seven patients with no specific MIC values for causative pathogens. When using the Clinical and Laboratory Standards Institute breakpoint (24) to calculate fT_>MIC for these patients, the finding was consistent with our primary approach; the percentage of patients achieving 75% fT_>MIC and 100% fT_>MIC remained higher in the clinical success group than in the failure group (82.4% versus 40 to 70%). However, these observations might have been biased by the unbalanced MIC distribution between the groups. In our study, patients in the treatment failure group were infected by pathogens with higher MIC values than those in the success group (12 to 32 mg/L versus 0.023 to 32 mg/L). Therefore, the treatment failure and mortality rates were expected to be higher in patients infected with resistant organisms, regardless of PK/PD achievement. Notably, one patient in the clinical success group was infected with multidrug-resistant Pseudomonas aeruginosa (MIC of 32 mg/L). The successful treatment of this patient was achieved by using a combination therapy of colistin and the highest licensed dose of meropenem, which was able to provide an individual fT_>MIC above the 40% target. This indicates that MIC value is not the only factor determining prognosis. Other factors, such as disease severity, other combination antibiotics, and PK/PD achievement, might also affect treatment outcome. Unfortunately, we did not have enough data to control for other important variables and were limited by the study design. Nevertheless, considering the high mortality rate even when 40% fT_>MIC was achieved and the fact that higher exposure was associated with improved outcomes (8, 25), maintaining a higher threshold of 75 to 100% fT_>MIC should be considered for optimizing therapy in critically ill patients.

The simulation results demonstrated that the prolonged infusion time of meropenem to 3 h and shortened dosing interval provided a greater likelihood of achieving PK/PD targets than the 30-min intermittent infusion. The advantage of the extended infusion strategy has already been documented in other studies (10, 12, 26). However, the extended administration strategy did not significantly improve PTA in patients with severe renal impairment in the current analysis, particularly when CL_CR-CG was less than 10 mL/min. A similar trend was observed in previous studies (27, 28). The elimination of meropenem is a first-order process, and it is renally excreted. In our population with reserved renal function, the elimination half-life (t_{1/2, β}) was approximately 1 h. Therefore, a short regular dosing interval and prolonged infusion duration were observed to be the best strategies for maximizing fT>MIC. When CL_CR-CG declined to less than 10 mL/min, the t_{1/2, β} of meropenem was prolonged to 4.3 to 7.4 h. Extending the infusion time to 3 h, which is less than the t_{1/2, β}, generated an only 20 to 25% higher trough concentration and resulted in a slightly higher PK/PD target achievement rate (86.9% versus 88.6%). However, when the infusion duration was increased to 24 h (continuous infusion) in the corresponding dose, the PTA increased from 88.6% to 99.4%. Therefore, we inferred that the infusion duration should be longer than the t_{1/2, β} of meropenem to maximize the advantage of an extended infusion strategy.

Based on the PTA results for achieving the 75% fT_>MIC target, the current standard intermittent infusion of meropenem dosages are only sufficient to be used empirically for covering susceptible pathogens (MIC ≤ 2 mg/L) in patients with severe renal impairment (CL_CR-CG < 25 mL/min). For mild-to-moderate renal function, an extended infusion strategy of the standard dosage regimen is required to achieve optimal PTA. When targeting 100% fT_>MIC, a daily dose up to 2- to 3-fold the current standard dose is required for covering susceptible pathogens. The drug concentration at the site of infection is also a crucial determinant for obtaining a good clinical outcome. For pneumonia, which was the common infection in our study, epithelial lining fluid penetration (ELF) was the most clinically relevant compartment to estimate intrapulmonary drug concentrations. Studies performed in critically ill patients reported that the meropenem concentration in ELF was much lower than that in the plasma, and the ELF penetration ratio ranged from 0.2 to 0.3 (29–31). The standard intermittent infusion of meropenem doses (1 to 2 g every 8 h) failed to achieve 40 to 50% fT_>MIC in ELF against pathogens with MIC values ≤2 mg/L (30, 32). The use of extended infusion has been shown to increase the penetration ratio of meropenem. However, even an extended infusion of the highest licensed meropenem dose fails to achieve 40% fT_>MIC in ELF (30). Although continuous infusion appeared to increase meropenem exposure in ELF compared with extended infusion, it might not be sufficient to cover all intermediate organisms causing nosocomial pneumonia in critically ill patients (29). Considering our simulation results and this ELF penetration issue, meropenem should be administered by extended infusion in all ICU patients to achieve a 75% fT_>MIC target in the plasma and increase meropenem exposure in ELF.

The strength of the current study is that both the drug exposure and meropenem MIC were determined; therefore, relationships between pharmacodynamic parameters and clinical outcomes could be evaluated. Although the present study has some strengths, it also has several limitations. First, because most of the plasma concentrations were measured at a steady state and there was limited blood sampling during the distribution phase, the incorporation of IIV into V_C and V_P resulted in a moderate level of ETA shrinkage. Second, this study was not designed to assess the PK/PD index and clinical outcomes as the primary objectives. Only 20 patients were available for pharmacodynamic analysis, and only 8 patients were infected by resistant pathogens. The small sample size and imbalance in the clinical demographics of the groups did not allow us to draw a definite conclusion about the optimal cutoff PK/PD index of meropenem in critically ill patients. Future controlled studies with larger population sizes are required to elucidate the appropriate pharmacodynamic cutoffs for meropenem in critically ill patients.

Conclusions.

The PPK model presented in this study contributes to a better understanding of the pharmacokinetics of meropenem in critically ill patients. Renal function was significantly associated with meropenem CL, while hypotension and low serum albumin levels increased the V_D of meropenem. Achieving 40% fT_>MIC might not be sufficient for critically ill patients, and a higher target, such as 75 to 100% fT_>MIC, should be considered for optimizing therapy. A 75% fT_>MIC could be reached using approved doses administered by 3-h infusion; however, twice the licensed dose was necessary to reach the 100% fT_>MIC target.

MATERIALS AND METHODS

Study population and design.

This was a prospective, single-center, observational pharmacokinetic study conducted at Songklanagarind Hospital, an academic hospital and the largest tertiary care center in southern Thailand. The inclusion criteria were (i) age of ≥18 years, (ii) confirmed or suspected bacterial infection, (iii) intravenous meropenem therapy, and (iv) hospitalization in a medical or surgical ICU. The exclusion criteria were (i) requirement of renal replacement therapy, (ii) pregnancy or breastfeeding, and (iii) a history of meropenem hypersensitivity.

The study protocol was approved by the Human Research Ethics Committee of the Faculty of Medicine, Prince of Songkla University (REC 61-061-14-1). Written informed consent was obtained from the patients or their legally authorized representatives before enrollment.

Meropenem administration and blood sampling.

Meropenem doses prescribed according to the attending physician’s decision were administered intravenously over 30 min using a venous catheter. After 24 h of therapy, 3 mL of blood was obtained via an indwelling arterial catheter from the arm that was not used for the intravenous infusion. A total of 5 blood samples per patient were randomly collected at the following sampling windows: shortly before meropenem administration and 0 to 0.5, 0.6 to 2.5, 2.6 to 4.0, and 4.1 to 12 h after meropenem administration. All blood samples were collected in heparinized tubes, immediately placed in an ice bath, and rapidly separated by centrifugation (2,000 × g at 4°C for 10 min) within 15 min of collection. Plasma supernatants were then preserved at −80°C until they were assayed in 4 weeks.

Unbound plasma meropenem assay.

Unbound plasma meropenem concentrations were measured using reverse-phase high-performance liquid chromatography (HPLC) based on a validated assay reported by Ozkan et al. (33). The unbound fraction of meropenem was extracted by transferring 500 μL of the sample to an ultrafiltration device (Nanosep 10K device, Pall Corp., Northborough, MA) and centrifuged at 13,000 rpm at 4°C for 30 min. Fifty microliters of the filtrates was injected into the HPLC system for analysis and chromatographically separated with a reversed-phase column (μBondapak C₁₈ column, 3.9 × 300 mm; Waters Associates). The mobile phase consisted of 15 mM KH₂PO₄, acetonitrile, and methanol (94:4:2 [vol/vol/vol], adjusted to a pH of 4.6), which flowed through the column at a rate of 1 mL/min. Transitional masses were monitored using a photodiode array detector (Waters, 2996; Waters Associates, Milford, MA) at a wavelength of 296 nm. The chromatograms were evaluated and integrated using a Waters 746 data module (Waters Associates). The LOD and LLOQ were 0.15 and 0.5 μg/mL, respectively. The intra- and interassay coefficients of variation were consistently less than 5%. The accuracy values ranged from 102.91% to 108.08%, and the recovery values ranged from 103.37% to 117.85%. Three meropenem concentrations (2, 32, and 128 μg/mL) with five replicates were used for this validation.

Microbiology.

All antimicrobial susceptibility tests were conducted at the Microbiology Laboratory at Songklanagarind Hospital, Hat Yai, Thailand. MIC values for meropenem were evaluated using the Epsilometer test methodology (Liofilchem MIC test strips, Envimed, Thailand) for each patient in whom the microorganism was identified.

Outcome assessment.

The primary treatment outcome was the clinical response, which was classified as either success or failure. Clinical success was defined as the resolution or improvement of infection-related clinical signs and symptoms at the end of meropenem therapy with no need to add or change the antibacterial therapy. Clinical failure was defined as the persistence or worsening of any clinical signs or symptoms of infection, the emergence of any new clinical signs or symptoms of infection, or the need for additional systemic antibacterial medication at the end of meropenem therapy. Subsequent deescalation to other narrow-spectrum antibiotics, according to microbiological results, was considered a standard of care and did not imply treatment failure. The secondary treatment outcome was ICU mortality.

Population pharmacokinetic analysis.

The PPK model of meropenem was built using NONMEM software version 7.4.3 (Icon Development Solution, Ellicott City, MD, USA) along with Perl-Speaks-NONMEM version 5.2.6 (Uppsala University, Uppsala, Sweden) and Pirana software version 3.0.0 (Certara, NJ, USA). Graphical processing of the data and NONMEM output were performed in R version 3.6.0 and R Studio version 1.2.1335 (R Foundation for Statistical Computing, Vienna, Austria). First-order conditional estimation with eta-epsilon interaction (FOCE-I) was used for parameter estimation throughout the analysis.

One-, two-, and three-compartment models with first-order elimination were evaluated to determine the optimal fit for the meropenem concentration-time data. The interindividual variability (IIV) of each parameter was modeled following an exponential error model, and the covariance terms were estimated for any interindividual error terms displaying significant correlations. The residual variability was modeled by considering additive, proportional, or combined additive and proportional error models.

After the appropriate structural model was established, several covariates of clinical interest were evaluated for their impact on PK parameters. These covariates were age, sex, actual body weight (BW), ideal body weight (IBW), adjusted body weight (ABW) (34), lean body weight (LBW) (35), body mass index, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, creatinine clearance estimated by the Cockcroft-Gault equation (CL_CR-CG) and by the Jelliffe equation (CL_CR-JEL) (36), estimated glomerular filtration rate (GFR) using the four- and six-variable modification of diet in renal disease study equation (GFR_MDRD) and the chronic kidney disease epidemiology collaboration equation (GFR_EPI), acute kidney injury, mechanical ventilation support, serum albumin, fluid balance, shock status (a clinical state requiring vasopressors or inotropes to maintain a mean arterial pressure greater than 65 mm Hg), and septic shock (based on the sepsis-3 criteria) (37). The CL_CR-CG values were calculated using various weight measures (BW, IBW, ABW, and LBW) and compared for their predictive performance on PK parameters. The renal function markers normalized to a body surface area (BSA) of 1.73 m² (units in mL/min/1.73 m²) and removal of BSA (units in mL/min) were also tested. Covariates were retained in the model if they led to significant improvement of model fit, as evaluated by a decrease in objective function value (OFV) of 3.84 (P < 0.05 for 1 degree of freedom [df]) for forward addition and an increase of OFV by 6.64 (P < 0.01 for 1 df) for a backward deletion step. Continuous covariates were scaled to the mean values.

OFV, Akaike’s information criterion (AIC), parameter precision, and visual inspection of various goodness-of-fit plots were considered for model selection. A nonparametric bootstrap (n = 2,000) was performed to evaluate the robustness of the final model and obtain the confidence intervals of all parameter estimates. The predictive performance of the final model was examined using a pcVPC that compared the 5th, 50th, and 95th percentiles of the observed and simulated concentrations (n = 2,000).

Calculation of individual pharmacokinetic/pharmacodynamic (PK/PD) indices.

The individual PK parameters from the final PPK model were used to calculate the PK/PD index. The percent fT_>MIC of meropenem was determined for each patient whose MIC value was available. If patients were infected with more than one strain/pathogen, the pathogen with the highest MIC was chosen for the calculation of the percent fT_>MIC. Subsequently, the number of patients who achieved a fT_>MIC above 40%, 75%, and 100% was counted and compared according to clinical outcomes.

The probability of target attainment (PTA).

Monte Carlo simulations (n = 5,000) were performed using the NONMEM software version 7.4 to assess the PTA of various meropenem dosage regimens. The final PPK model and significant covariates were used to generate unbound concentration-time profiles over the first 24 to 48 h of the treatment course. The simulated scenarios were divided into five groups according to renal function (CL_CR-CG < 10, 10 to 25, 25.1 to 50, 50.1 to 90, and 90.1 to 130 mL/min). CL_CR-CG was considered to follow a uniform distribution in each range. Serum albumin level and shock status were also included in the model. Regarding the original data set, serum albumin and shock status were simulated using mean and standard deviations of 2.5 ± 0.65 g/dL and 35%, respectively. From the simulated concentration-time profiles, fT_>MIC was determined for each virtual patient over a range of doubling MIC values, from 0.0156 to 128 mg/L. Then, the PTA was calculated as the percentage of patients who achieved 40% fT_>MIC, 75% fT_>MIC, and 100% fT_>MIC targets. Regimens with PTAs of at least 90% were considered optimal.

Statistical analysis.

All statistical analyses were performed using Stata software version 14.0 (StataCorp LP, College Station, TX, USA). Unless stated otherwise, continuous data are presented as median (interquartile ranges) and number (percentage) for categorical data. Continuous normally distributed data and nonnormally distributed data were analyzed by unpaired t test and Mann-Whitney U test, respectively. Categorical data were evaluated either by chi-square or Fisher’s exact test, as appropriate. A two-sided P value of <0.05 was considered statistically significant in all analyses.