Quantification and Explanation of the Variability of First-Dose Amikacin Concentrations in Critically Ill Patients Admitted to the Emergency Department: A Population Pharmacokinetic Analysis

Risk Factors Associated with Antibiotic Exposure Variability in Critically Ill Patients: A Systematic Review

(1) Background: Knowledge about the behavior of antibiotics in critically ill patients has been increasing in recent years. Some studies have concluded that a high percentage may be outside the therapeutic range. The most likely cause of this is the pharmacokinetic variability of critically ill patients, but it is not clear which factors have the greatest impact. The aim of this systematic review is to identify risk factors among critically ill patients that may exhibit significant pharmacokinetic alterations, compromising treatment efficacy and safety. (2) Methods: The search included the PubMed, Web of Science, and Embase databases. (3) Results: We identified 246 observational studies and ten clinical trials. The most studied risk factors in the literature were renal function, weight, age, sex, and renal replacement therapy. Risk factors with the greatest impact included renal function, weight, renal replacement therapy, age, protein or albumin levels, and APACHE or SAPS scores. (4) Conclusions: The review allows us to identify which critically ill patients are at a higher risk of not reaching therapeutic targets and helps us to recognize the extensive number of risk factors that have been studied, guiding their inclusion in future studies. It is essential to continue researching, especially in real clinical practice and with clinical outcomes.

Failure to predict amikacin elimination in critically ill patients with cancer based on the estimated glomerular filtration rate: applying PBPK approach in a therapeutic drug monitoring study

PURPOSE

The aim of this work was to integrate the Therapeutic Drug Monitoring (TDM) with the model-informed precision dosing (MIPD) approach, using Physiologically-based Pharmacokinetic/Pharmacodynamic (PBPK/PD) modelling and simulation, to explore the relationship between amikacin exposure and estimated glomerular filtration rate (GFR) in critically ill patients with cancer.

METHODS

In the TDM study, samples from 51 critically-ill patients with cancer treated with amikacin were analysed. Patients were stratified according to renal function based on GFR status. A full-body PBPK model with 12 organs model was developed using Simcyp V. 21, including steady-state volume of distribution of 0.21 L/kg and renal clearance of 6.9 L/h in healthy adults. PK parameters evaluated were within the 2-fold error range.

RESULTS

During the validation step, predicted vs observed amikacin clearance values after single infusion dose in patients with normal renal function, mild and moderate renal impairment were 7.6 vs 8.1 L/h (7.5 mg/kg dose); 3.8 vs 4.5 L/h (1500 mg dose) and 2.2 vs 3.1 L/h (25 mg/kg dose), respectively. However, predicted vs observed amikacin clearance after a single dose infusion of 1400 mg in critically-ill patients with cancer were 1.46 vs 1.63 (P = 0.6406) L/h (severe), 2.83 vs 1.08 (P < 0.05) L/h (moderate), 4.23 vs 2.49 (P = 0.0625) L/h (mild) and 7.41 vs 3.36 (P < 0.05) L/h (normal renal function).

CONCLUSION

This study demonstrated that estimated GFR did not predict amikacin elimination in critically-ill patients with cancer. Further studies are necessary to find amikacin PK covariates to optimize the pharmacotherapy in this population. Therefore, TDM of amikacin is imperative in cancer patients.

A simple and rapid HPLC-MS/MS method for therapeutic drug monitoring of amikacin in dried matrix spots

Individualized treatment of amikacin under the guidance of therapeutic drug monitoring (TDM) is important to reduce the occurrence of toxicity and improve clinical efficacy. In the present study, we developed and validated a simple and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to determine the concentration of amikacin in dried matrix spots (DMS) which the matrix is serum. DMS samples were obtained by spotting volumetric blood onto Whatman 903® cards. Samples were punched into 3 mm diameter discs and extracted with 0.2 % formic acid in water. The HILIC column (2.1 mm × 100 mm, 3.0 µm) under gradient elution was applied, and the analysis time was 3 min per injection. The mass spectrometry transitions were m/z 586.3 → 163.0 for amikacin and m/z 591.4 → 163.1 for D5-amikacin. Full validation was conducted for DMS method, and the method was applied for the amikacin TDM and compared with serum method. The linearity was ranged from 0.5 to 100 mg/L. Both within-run and between-run accuracy and precision of DMS ranged from 91.8 % to 109.6 % and 3.6 % to 14.2 %, respectively. The matrix effect was 100.5 %-106.5 % of DMS method. Amikacin remained stable in DMS for at least 6 days at room temperature, 16 days at 4 °C, 86 days at -20 °C and -70 °C. A good agreement between the DMS method and serum method has been shown in Bland-Altman plots and Passing-Bablok regression. All of the results demonstrated that the DMS methods can be a favorable replacement for amikacin TDM.

Pooled Population Pharmacokinetic Analysis for Exploring Ciprofloxacin Pharmacokinetic Variability in Intensive Care Patients

Background and Objective

Previous pharmacokinetic (PK) studies of ciprofloxacin in intensive care (ICU) patients have shown large differences in estimated PK parameters, suggesting that further investigation is needed for this population. Hence, we performed a pooled population PK analysis of ciprofloxacin after intravenous administration using individual patient data from three studies. Additionally, we studied the PK differences between these studies through a post-hoc analysis.

Methods

Individual patient data from three studies (study 1, 2, and 3) were pooled. The pooled data set consisted of 1094 ciprofloxacin concentration–time data points from 140 ICU patients. Nonlinear mixed-effects modeling was used to develop a population PK model. Covariates were selected following a stepwise covariate modeling procedure. To analyze PK differences between the three original studies, random samples were drawn from the posterior distribution of individual PK parameters. These samples were used for a simulation study comparing PK exposure and the percentage of target attainment between patients of these studies.

Results

A two-compartment model with first-order elimination best described the data. Inter-individual variability was added to the clearance, central volume, and peripheral volume. Inter-occasion variability was added to clearance only. Body weight was added to all parameters allometrically. Estimated glomerular filtration rate on ciprofloxacin clearance was identified as the only covariate relationship resulting in a drop in inter-individual variability of clearance from 58.7 to 47.2%. In the post-hoc analysis, clearance showed the highest deviation between the three studies with a coefficient of variation of 14.3% for posterior mean and 24.1% for posterior inter-individual variability. The simulation study showed that following the same dose regimen of 400 mg three times daily, the area under the concentration–time curve of study 3 was the highest with a mean area under the concentration–time curve at 24 h of 58 mg·h/L compared with that of 47.7 mg·h/L for study 1 and 47.6 mg·h/L for study 2. Similar differences were also observed in the percentage of target attainment, defined as the ratio of area under the concentration–time curve at 24 h and the minimum inhibitory concentration. At the epidemiological cut-off minimum inhibitory concentration of Pseudomonas aeruginosa of 0.5 mg/L, percentage of target attainment was only 21%, 18%, and 38% for study 1, 2, and 3, respectively.

Conclusions

We developed a population PK model of ciprofloxacin in ICU patients using pooled data of individual patients from three studies. A simple ciprofloxacin dose recommendation for the entire ICU population remains challenging owing to the PK differences within ICU patients, hence dose individualization may be needed for the optimization of ciprofloxacin treatment.