Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate

Inadequate imipenem dosing in patients with decreased kidney function: A clinical pharmacokinetic study

OBJECTIVES

A number of population pharmacokinetic (popPK) models of imipenem in critically ill patients are available for dosing optimisation, but they represent only a narrow range of kidney functions. This study evaluates the target attainment of on-label regimens through popPK modelling and simulation in patients across different kidney functions.

METHODS

A popPK model was built based on two datasets from Switzerland (model development population, 151 patients, 322 concentrations) and externally validated on two datasets from the Czech Republic (19 patients, 111 concentrations) and Vietnam (43 patients, 85 concentrations). Monte Carlo simulations were performed to evaluate the probability of target attainment (PTA) from a minimum inhibitory concentration (MIC) of 0.125 to 32 mg/L. We estimated the cumulative fraction of response (CFR) against Pseudomonas aeruginosa (the epidemiological cut-off value was 4 mg/L) across a broad range of Cockcroft-Gault creatinine clearance values (CLCRCG 15-130 mL/min). Targets of 40% and 100%ƒT>MIC (percentage of dosing interval estimated free concentrations above MIC) were investigated.

RESULTS

Decreased kidney function (eGFRCKD-EPI <90mL/min) was observed in 70/151 patients (46.4%) within the model development population, and in 11/19 (57.9%) and 24/43 (55.8%) patients in the Czech Republic and Vietnam, respectively. CLCRCG significantly influenced the imipenem clearance described by a two-compartment model. For PTA, all regimens achieved 40% ƒT>MIC2mg/L. With a 100%ƒT>MIC target, 500 mg q6h (CLCRCG 30-60 mL/min) could only cover a MIC of up to 1 mg/L, irrespective of infusion time. For CFR, no dosing regimen could cover susceptible P. aeruginosa for 100%ƒT>MIC.

CONCLUSIONS

The highest on-label imipenem dosing regimens failed to attain 100% ƒT>MIC4mg/L in patients with decreased kidney function. Higher dosing may be necessary to cover MIC of 4mg/L. Future trials should explore their efficacy, toxicity, and the utility of model-informed precision dosing in this population.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity

BACKGROUND

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity.

METHODS

Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m 2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using Pmetrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset.

RESULTS

In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m 2 (range: 40-70.3 kg/m 2 ). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients).

CONCLUSIONS

Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Assessment of body mass-related covariates for rifampicin pharmacokinetics in healthy Caucasian volunteers

PURPOSE

Currently, body weight-based dosing of rifampicin is recommended. But lately, fat-free mass (FFM) was reported to be superior to body weight (BW). The present evaluation aimed to assess the influence of body mass-related covariates on rifampicin's pharmacokinetics (PK) parameters in more detail using non-linear mixed effects modeling (NLMEM).

METHODS

Twenty-four healthy Caucasian volunteers were enrolled in a bioequivalence study, each receiving a test and a reference tablet of 600 mg of rifampicin separated by a wash-out period of at least 9 days. Monolix version 2023R1 was used for NLMEM. Monte Carlo simulations (MCS) were performed to visualize the relationship of body size descriptors to the exposure to rifampicin.

RESULTS

A one-compartment model with nonlinear (Michaelis-Menten) elimination and zero-order absorption kinetics with a lag time best described the data. The covariate model including fat-free mass (FFM) on volume of distribution (V/F) and on maximum elimination rate (Vmax/F) lowered the objective function value (OFV) by 56.4. The second-best covariate model of sex on V/F and Vmax/F and BW on V/F reduced the OFV by 51.2. The decrease in unexplained inter-individual variability on Vmax/F in both covariate models was similar. For a given dose, MCS showed lower exposure to rifampicin with higher FFM and accordingly in males compared to females with the same BW and body height.

CONCLUSION

Our results indicate that beyond BW, body composition as reflected by FFM could also be relevant for optimized dosing of rifampicin. This assumption needs to be studied further in patients treated with rifampicin.

High adsorption capacity of hemoperfusion on imipenem in critically ill patients with septic shock: a case report

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by an excessive host response to infection, manifested by elevated levels of inflammatory cytokines. At present, the use of hemoperfusion to remove inflammatory cytokines from the bloodstream has been expanding. Meanwhile, the pharmacokinetics and pharmacodynamics characteristics of antibiotics in critically ill patients may be impacted by hemoperfusion.

CASE PRESENTATION

The patient was a 69-year-old male with poorly controlled type 2 diabetes. When admitted to the ICU, Multiple Organ Dysfunction Syndrome (MODS) appeared within 48 h, and he was suspected of septic shock due to acute granulocytopenia and significantly increased procalcitonin. Broad-spectrum antibiotics imipenem was administered according to Sepsis 3.0 bundle and hemoperfusion lasting 4 h with a neutron-macroporous resin device (HA-380, Jafron, China) five times was conducted to lower the extremely high value of serum inflammatory factors. Blood samples were collected to measure imipenem plasma concentration to investigate the effect of hemoperfusion quantitatively. This study showed that 4 h of hemoperfusion had a good adsorption ability on inflammatory factors and could remove about 75.2% of imipenem.

CONCLUSIONS

This case demonstrated the high adsorption capacity of hemoperfusion on imipenem in critically ill patients. It implies a timely imipenem supplement is required, especially before hemoperfusion.

Population pharmacokinetics and dosing optimisation of imipenem in critically ill patients

OBJECTIVE

The objective of this study was to explore factors that affect the clearance of imipenem in critically ill patients and to provide a dosing regimen for such patients.

METHODS

A prospective open-label study enrolled 51 critically ill patients with sepsis. Patients were between the ages of 18 and 96. Blood samples were collected in duplicate before (0 hour) and at 0.5, 1, 1.5, 2, 3, 4, 6, and 8 hours after imipenem administration. The plasma imipenem concentration was determined by the high-performance liquid chromatography-ultraviolet detection (HPLC-UV) method. A population pharmacokinetic (PPK) model was developed using nonlinear mixed-effects modelling methods to identify covariates. Monte Carlo simulations were performed using the final PPK model to explore the effect of different dosing regimens on the probability of target attainment (PTA).

RESULTS

The imipenem concentration data were best described by a two-compartment model. Creatinine clearance (CrCl, mL/min) was a covariate that affected central clearance (CLc). Patients were divided into four subgroups based on different CrCl rates. Monte Carlo simulations were performed to assess the PTA differences between empirical dosing regimens (0.5 g every 6 hours (q6h), 0.5 g every 8 hours (q8h), 0.5 g every 12 hours (q12h), 1 g every 6 hours (q6h), 1 g every 8 hours (q8h), and 1 g every 12 hours (q12h)) and to determine the target achievement rate covariate.

CONCLUSION

This study identified covariates for CLc, and the proposed final model can be used to guide clinicians administering imipenem in this particular patient population.

Parametric and Nonparametric Population Pharmacokinetic Models to Assess Probability of Target Attainment of Imipenem Concentrations in Critically Ill Patients

Population pharmacokinetic modeling and simulation (M&S) are used to improve antibiotic dosing. Little is known about the differences in parametric and nonparametric M&S. Our objectives were to compare (1) the external validation of parametric and nonparametric models of imipenem in critically ill patients and (2) the probability of target attainment (PTA) calculations using simulations of both models. The M&S software used was NONMEM 7.2 (parametric) and Pmetrics 1.5.2 (nonparametric). The external predictive performance of both models was adequate for eGFRs ≥ 78 mL/min but insufficient for lower eGFRs, indicating that the models (developed using a population with eGFR ≥ 60 mL/min) could not be extrapolated to lower eGFRs. Simulations were performed for three dosing regimens and three eGFRs (90, 120, 150 mL/min). Fifty percent of the PTA results were similar for both models, while for the other 50% the nonparametric model resulted in lower MICs. This was explained by a higher estimated between-subject variability of the nonparametric model. Simulations indicated that 1000 mg q6h is suitable to reach MICs of 2 mg/L for eGFRs of 90-120 mL/min. For MICs of 4 mg/L and for higher eGFRs, dosing recommendations are missing due to largely different PTA values per model. The consequences of the different modeling approaches in clinical practice should be further investigated.

Parametric and Nonparametric Methods in Population Pharmacokinetics: Experts' Discussion on Use, Strengths, and Limitations

Population pharmacokinetics consists of analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring and model-informed precision dosing. There are 2 main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians, and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion article may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area.

Pragmatic options for dose optimization of ceftazidime/avibactam with aztreonam in complex patients

BACKGROUND

Avibactam is a β-lactamase inhibitor that is combined with aztreonam against Enterobacterales co-expressing serine- and metallo-β-lactamases (MBL). Optimal dosing of aztreonam with avibactam is not well-defined in critically ill patients and contingent on ceftazidime/avibactam product labelling.

OBJECTIVES

To identify a pragmatic dosing strategy for aztreonam with avibactam to maximize the probability of target attainment (PTA).

METHODS

We conducted a prospective observational pharmacokinetic study. Five blood samples were collected around the fourth dose of aztreonam or ceftazidime/avibactam and assayed for all three drugs. Population pharmacokinetic (PK) analysis coupled with Monte Carlo simulations were used to create a dosing nomogram for aztreonam and ceftazidime/avibactam based on drug-specific pharmacodynamic (PD) targets.

RESULTS

A total of 41 participants (59% male) median age of 75 years (IQR 63-79 years) were enrolled. They were critically ill (46%) with multiple comorbidities and complications including burns (20%). Population PK analysis identified higher volume of distribution and lower clearance (CL) compared with typical value expectations for aztreonam and ceftazidime/avibactam. Estimated glomerular filtration (eGFR) rate using the CKD-EPI equation predicted CL for all three drugs. The need for high doses of aztreonam and ceftazidime/avibactam above those in the existing product labels are not predicted by this analysis with the exception of ceftazidime/avibactam for patients with eGFR of 6-15 mL/min, in whom suboptimal PTA of ≤71% is predicted.

CONCLUSIONS

Pragmatic and lower daily-dose options are predicted for aztreonam and ceftazidime/avibactam when the eGFR is <90 mL/min. These options should be tested prospectively.

Population Pharmacokinetic Modeling and Simulations of Imipenem in Burn Patients With and Without Continuous Venovenous Hemofiltration in the Military Health System

Continuous venovenous hemofiltration (CVVH) is a life‐sustaining procedure in patients with severe burns and acute kidney injury. Physiologic changes from burn injury and use of CVVH may alter imipenem pharmacokinetics (PK). We aimed to compare imipenem clearance (CL) in burn patients with and without CVVH, determine the effect of burn on imipenem volume of distribution (CVVH, n = 12; no CVVH, n = 11), in combination with previously published models. Model qualification was performed with standard diagnostics and comparing predicted PK parameters/time‐concentration profiles with those in the existing literature. Monte Carlo simulations were conducted to evaluate the probability of target attainment. A 2‐compartment model best described the data. Utilizing albumin as a covariate on volume parameters and leveraging the clearance model from prior literature, our model predicted imipenem central volume and CL within a 10% margin of error across healthy, renally impaired, and burn populations. We provide direct comparison of imipenem CL in burn patients with and without CVVH. Notably, there was no significant difference. Large imipenem V_d in patients with severe burns is likely explained by increased capillary permeability, for which serum albumin may be a reasonable surrogate. Dosing 500 mg every 6 hours is adequate for burn patients on renally dosed CVVH; however, suspicion of augmented renal clearance or patients placed on CVVH without renal impairment may necessitate dosing of 1000 mg every 6 hours.

Optimizing Aminoglycoside Dosing Regimens for Critically Ill Pediatric Patients with Augmented Renal Clearance: a Convergence of Parametric and Nonparametric Population Approaches

Augmented renal clearance (ARC) can occur in critically ill pediatric patients receiving aminoglycosides such as gentamicin and tobramycin, yet optimal dosing strategies for ARC are undefined. We evaluated the probability of achieving efficacious or toxic exposures in pediatrics. Parallel population modeling of concentration strategies were pursued using Pmetrics v1.5.2 (nonparametric) and Monolix v2019R2 (parametric). Bayesian exposures were used to classify ARC based on total clearance (CL). The effects of serum creatinine (SCR), creatinine clearance (CRCL), total body weight (TBW), postnatal age (PNA), and ARC were explored as covariates. The probabilities of target attainment (PTA) (i.e., maximum concentration [C _max]/MIC, area under the concentration-time curve [AUC]/MIC) and of toxic exposure (PTE) (i.e., minimum concentration [C _min] > 2 μg/ml) were calculated according to PNA and ARC. A total of 123 patients (1 to 21 years old, 56% female) contributed 304 concentrations. A two-compartment model was superior to a one-compartment model in both approaches. Bayesian posterior predicted concentrations from the nonparametric base model fit the data well (R ² = 0.96) and classified 34 patients as having ARC (28%). Both the nonparametric and parametric approaches resulted in allometrically scaling of TBW on volume (V) and clearance (CL). ARC modified CL and central V. CRCL and a maturation function modified CL. ARC was associated with a 1.49- versus 1.66-fold increase in CL and a 1.56- versus 1.66-fold increase in the central V (nonparametric versus parametric). A high dose of 12 mg/kg of body weight/day was required to achieve adequate PTA when MICs were 1 to 2 μg/ml; ARC lowered achievable MICs. When PNA was <2 years, PTE was increased. Aminoglycoside monotherapy should be avoided in critically ill pediatric patients with ARC when MICs exceed 1 μg/ml, as optimal exposures are unachievable with standard dosing.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Pharmacokinetics and Monte Carlo Dosing Simulations of Imipenem in Critically Ill Patients with Life-Threatening Severe Infections During Support with Extracorporeal Membrane Oxygenation

Background

Extracorporeal membrane oxygenation (ECMO), a cardiopulmonary bypass device, has been found to increase the profound pathophysiological changes associated with life-threatening severe infections in patients with multiple comorbidities, which results in alterations of pharmacokinetic patterns for antibiotics.

Objectives

The aims of this study were (1) to determine the pharmacokinetics of imipenem and (2) to assess the probability of target attainment (PTA) for imipenem in critically ill patients with life-threatening severe infections during support with ECMO.

Methods

The pharmacokinetic studies were carried out following administration of 0.5 g of imipenem every 6 h on the 4th dose of drug administration in 10 patients and a Monte Carlo simulation was performed to determine the PTA of achieving 40% exposure time during which the plasma drug concentrations remained above minimum inhibitory concentration (T > _MIC) and 80% T > _MIC.

Results

The median values of volume of distribution and total clearance (CL) of imipenem in these patients were 13.98 L and 9.78 L/h, respectively. A high PTA (≥ 90%) for a target of 80% with a MIC of 4 μg/mL in patients with CL_CR 60–120 mL/min and flow rate of ECMO circuit 3–5.5 L/min was observed when imipenem was administered by a 4-h infusion of 1 g every 6 h.

Conclusions

A high dosage regimen such as 1 g every 6 h of imipenem may be required to achieve pharmacodynamic targets against less susceptible pathogens in this patient population.

ClinicalTrial.gov Identifier

NCT03776305, date of registration: 11 December 2018.

Electronic supplementary material

The online version of this article (10.1007/s13318-020-00643-3) contains supplementary material, which is available to authorized users.

What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient’s clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.