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Model-informed precision dosing (MIPD) might be used to optimize antibiotic treatment. Procalcitonin (PCT) is a biomarker for severity of infection and response to antibiotic treatment. The aim of this study was to assess the impact of MIPD on the course of PCT and to investigate the association of PCT with pharmacodynamic target (PDT) attainment in critically ill patients. This is a secondary analysis of the DOLPHIN trial, a multicentre, open-label, randomised controlled trial. Patients with a PCT value available at day 1 (T1), day 3 (T3), or day 5 (T5) after randomisation were included. The primary outcome was the absolute difference in PCT concentration at T1, T3, and T5 between the MIPD and the standard dosing group. In total, 662 PCT concentrations from 351 critically ill patients were analysed. There was no statistically significant difference in PCT concentration between the trial arms at T1, T3, or T5. The median PCT concentration was highest in patients who exceeded 10× PDT at T1 [13.15 ng/mL (IQR 5.43–22.75)]. In 28-day non-survivors and in patients that exceeded PDT at T1, PCT decreased significantly between T1 and T3, but plateaued between T3 and T5. PCT concentrations were not significantly different between patients receiving antibiotic treatment with or without MIPD guidance. The potential of PCT to guide antibiotic dosing merits further investigation. [ABSTRACT FROM AUTHOR]

Background Standard antibiotic dosing is not suitable for critically ill patients, due to altered pharmacokinetics (PK) in these patients. Knowledge of protein binding is important for optimizing antibiotic exposure because only the unbound fraction is pharmacologically active. If unbound fractions can be predicted, minimal sampling techniques and less costly methods can be routinely used. Methods Data from the DOLPHIN trial, a prospective randomized clinical trial that included critically ill patients, were used. Total and unbound ceftriaxone concentrations were determined using a validated UPLC-MS/MS method. A non-linear saturable binding model was made using 75% of the trough concentrations and validated on the remaining data. Our model and previously published models were tested for their performance for subtherapeutic (<1 mg/L) and high (>10 mg/L) unbound concentrations. Results In total, 113 patients were sampled [Acute Physiology And Chronic Health Evaluation version 4 (APACHE IV) score 71 (IQR 55–87), albumin 28 g/L (IQR 24–32)]. This resulted in 439 samples (trough = 224, peak = 215). Unbound fractions were significantly different between samples taken at trough and peak times [10.9% (IQR 7.9–16.4) versus 19.7% (IQR 12.9–26.6), P  < 0.0001], which was not explained by concentration differences. Our model and most literature models showed good sensitivity and low specificity to determine high and subtherapeutic ceftriaxone trough concentrations using only the total ceftriaxone and albumin concentrations. Conclusions Ceftriaxone protein binding is not concentration related in critically ill patients. Existing models show good ability to predict high concentrations, but low specificity in predicting subtherapeutic concentrations. [ABSTRACT FROM AUTHOR]

Aims: To describe the pharmacokinetics (PK) of cefotaxime as pre‐emptive treatment in critically ill adult patients, including covariates and to determine the probability of target attainment (PTA) of different dosage regimens for Enterobacterales and Staphylococcus aureus. Methods: Five samples were drawn during 1 dosage interval in critically ill patients treated with cefotaxime 1 g q6h or q4h. PK parameters were estimated using NONMEM (v7.4.2). The percentage of patients reaching 100% fT>MICECOFF was used to compare different dosage regimens for Enterobacterales and S. aureus. Results: This study included 92 patients (437 samples). The best structural model was a 2‐compartment model with a combined error, interindividual variability on clearance, central volume and intercompartmental clearance. Correlations between interindividual variability were included. Clearance increased with higher estimated glomerular filtration rate (eGFR; creatinine clearance) and albumin concentration. For Enterobacterales, 1 g q8h reached 95% PTA and continuous infusion (CI) of 4 g 24 h−1 100% PTA at the highest eGFR and albumin concentration. For S. aureus the predefined target of 95% PTA was not reached with higher eGFR and/or albumin concentrations. CI of 6 g 24 h−1 for S. aureus resulted in a minimum of 99% PTA. Conclusion: Cefotaxime PK in critically ill patients was best described by a 2‐compartment model with eGFR and albumin concentration as covariates influencing clearance. For Enterobacterales 1 g q8h or CI of 4 g 24 h−1 was adequate for all combinations of eGFR and albumin concentration. For S. aureus CI of 6 g 24 h−1 would be preferred if eGFR and albumin concentration exceed 80 mL min−1 and 40 g L−1 respectively. [ABSTRACT FROM AUTHOR]

The number of pharmacokinetic (PK) models of meropenem is increasing. However, the daily role of these PK models in the clinic remains unclear, especially for critically ill patients. Therefore, we evaluated the published meropenem models on real-world ICU data to assess their suitability for use in clinical practice. All models were built in NONMEM and evaluated using prediction and simulation-based diagnostics for the ability to predict the subsequent meropenem concentrations without plasma concentrations (a priori), and with plasma concentrations (a posteriori), for use in therapeutic drug monitoring (TDM). Eighteen PopPK models were included for evaluation. The a priori fit of the models, without the use of plasma concentrations, was poor, with a prediction error (PE)% of the interquartile range (IQR) exceeding the ±30% threshold. The fit improved when one to three concentrations were used to improve model predictions for TDM purposes. Two models were in the acceptable range with an IQR PE% within ±30%, when two or three concentrations were used. The role of PK models to determine the starting dose of meropenem in this population seems limited. However, certain models might be suitable for TDM-based dose adjustment using two to three plasma concentrations. [ABSTRACT FROM AUTHOR]

Background and Objective: Many patients treated for COVID-19 related acute respiratory distress syndrome in the intensive care unit are sedated with the benzodiazepine midazolam. Midazolam undergoes extensive metabolism by CYP3A enzymes, which may be inhibited by hyperinflammation. Therefore, an exaggerated proinflammatory response, as often observed in COVID-19, may decrease midazolam clearance. To develop a population pharmacokinetic model for midazolam in adult intensive care unit patients infected with COVID-19 and to assess the effect of inflammation, reflected by IL-6, on the pharmacokinetics of midazolam. Methods: Midazolam blood samples were collected once a week between March 31 and April 30 2020. Patients were excluded if they concomitantly received CYP3A4 inhibitors, CYP3A4 inducers and/or continuous renal replacement therapy. Midazolam and metabolites were analyzed with an ultra-performance liquid chromatography–tandem mass spectrometry method. A population pharmacokinetic model was developed, using nonlinear mixed effects modelling. IL-6 and CRP, markers of inflammation, were analyzed as covariates. Results: The data were described by a one-compartment model for midazolam and the metabolites 1-OH-midazolam and 1-OH-midazolam-glucuronide. The population mean estimate for midazolam clearance was 6.7 L/h (4.8–8.5 L/h). Midazolam clearance was reduced by increased IL-6 and IL-6 explained more of the variability within our patients than CRP. The midazolam clearance was reduced by 24% (6.7–5.1 L/h) when IL-6 increases from population median 116 to 300 pg/mL. Conclusions: Inflammation, reflected by high IL-6, reduces midazolam clearance in critically ill patients with COVID-19. This knowledge may help avoid oversedation, but further research is warranted. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Hyperinflammation Reduces Midazolam Metabolism in Critically Ill Adults with COVID-19" published in Clinical Pharmacokinetics. The correction addresses an error in the PK section of the article's Supplementary PDF, where the codes 'COVCREA= 96' and 'COVIL6= 116.5' should have been 'COVCREA= 1' and 'COVIL6= 1'. The original article has been corrected accordingly. The document also provides a link to the electronic supplementary material. [Extracted from the article]

Haloperidol is considered the first-line treatment for delirium in critically ill patients. However, clinical evidence of efficacy is lacking and no pharmacokinetic studies have been performed in intensive care unit (ICU) patients. The aim of this study was to establish a pharmacokinetic model to describe the PK in this population to improve insight into dosing. One hundred and thirty-nine samples from 22 patients were collected in a single-center study in adults with ICU delirium who were treated with low-dose intravenous haloperidol (3–6 mg per day). We conducted a population pharmacokinetic analysis using Nonlinear Mixed Effects Modelling (NONMEM). A one-compartment model best described the data. The mean population estimates were 51.7 L/h (IIV 42.1%) for clearance and 1490 L for the volume of distribution. The calculated half-life was around 22 h (12.3–29.73 h) for an average patient. A negative correlation between C-Reactive Protein (CRP) and haloperidol clearance was observed, where clearance decreased significantly with increasing CRP up to a CRP concentration of 100 mg/L. This is the first step towards haloperidol precision dosing in ICU patients and our results indicate a possible role of inflammation. [ABSTRACT FROM AUTHOR]

The article discuses the findings of a meta-analysis combining data from the DOLPHIN trial with a systematic review on therapeutic drug monitoring (TDM) of beta-lactam antibiotics in critically ill patients.

Purpose: To develop and validate a population pharmacokinetic model of ciprofloxacin intravenously in critically ill patients, and determine target attainment to provide guidance for more effective regimens. Methods: Non-linear mixed-effects modelling was used for the model development and covariate analysis. Target attainment of an ƒAUC0–24/MIC ≥ 100 for different MICs was calculated for standard dosing regimens. Monte Carlo simulations were performed to define the probability of target attainment (PTA) of several dosing regimens. Results: A total of 204 blood samples were collected from 42 ICU patients treated with ciprofloxacin 400–1200 mg/day, with median values for age of 66 years, APACHE II score of 22, BMI of 26 kg/m2, and eGFR of 58.5 mL/min/1.73 m2. The median ƒAUC0–24 and ƒCmax were 29.9 mg•h/L and 3.1 mg/L, respectively. Ciprofloxacin pharmacokinetics were best described by a two-compartment model. We did not find any significant covariate to add to the structural model. The proportion of patients achieving the target ƒAUC0–24/MIC ≥ 100 were 61.9% and 16.7% with MICs of 0.25 and 0.5 mg/L, respectively. Results of the PTA simulations suggest that a dose of ≥ 1200 mg/day is needed to achieve sufficient ƒAUC0–24/MIC ratios. Conclusions: The model described the pharmacokinetics of ciprofloxacin in ICU patients adequately. No significant covariates were found and high inter-individual variability of ciprofloxacin pharmacokinetics in ICU patients was observed. The poor target attainment supports the use of higher doses such as 1200 mg/day in critically ill patients, while the variability of inter-individual pharmacokinetics parameters emphasizes the need for therapeutic drug monitoring to ensure optimal exposure. [ABSTRACT FROM AUTHOR]

Background: Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results. Methods: Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m2 (interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software. Results: For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (Ke). The parametric population parameter estimates were Ke 0.637 h−1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (Vc) 29.6 L (without BSV). The nonparametric values were Ke 0.681 h−1 (34.0% CV) and Vc 31.1 L (42.6% CV). Conclusions: Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]