Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients.

Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA). Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicenter dataset (561 patients, 11 German centers, 3654 TDM-samples). The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 hours. In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimization of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.

Towards model-informed precision dosing of piperacillin: multicenter systematic external evaluation of pharmacokinetic models in critically ill adults with a focus on Bayesian forecasting.

PURPOSE: Inadequate piperacillin (PIP) exposure in intensive care unit (ICU) patients threatens therapeutic success. Model-informed precision dosing (MIPD) might be promising to individualize dosing; however, the transferability of published models to external populations is uncertain. This study aimed to externally evaluate the available PIP population pharmacokinetic (PopPK) models. METHODS: A multicenter dataset of 561 ICU patients (11 centers/3654 concentrations) was used for the evaluation of 24 identified models. Model performance was investigated for a priori (A) predictions, i.e., considering dosing records and patient characteristics only, and for Bayesian forecasting, i.e., additionally including the first (B1) or first and second (B2) therapeutic drug monitoring (TDM) samples per patient. Median relative prediction error (MPE) [%] and median absolute relative prediction error (MAPE) [%] were calculated to quantify accuracy and precision. RESULTS: The evaluation revealed a large inter-model variability (A: MPE - 135.6-78.3% and MAPE 35.7-135.6%). Integration of TDM data improved all model predictions (B1/B2 relative improvement vs. A: |MPE|median_all_models 45.1/67.5%; MAPEmedian_all_models 29/39%). The model by Kim et al. was identified to be most appropriate for the total dataset (A/B1/B2: MPE - 9.8/- 5.9/- 0.9%; MAPE 37/27.3/23.7%), Udy et al. performed best in patients receiving intermittent infusion, and Klastrup et al. best predicted patients receiving continuous infusion. Additional evaluations stratified by sex and renal replacement therapy revealed further promising models. CONCLUSION: The predictive performance of published PIP models in ICU patients varied considerably, highlighting the relevance of appropriate model selection for MIPD. Our differentiated external evaluation identified specific models suitable for clinical use, especially in combination with TDM.

Developing a Method for Quantifying Meropenem in Children-Volumetric Adsorptive Microsampling Versus Plasma Sampling.

BACKGROUND: Meropenem is a carbapenem antibiotic often used in pediatric intensive care units due to its broad spectrum of activity. Therapeutic drug monitoring (TDM) is a useful tool to increase the effectiveness of meropenem by adjusting the dose based on plasma levels; however, the relatively large sample volume required for TDM can limit its use in children. Therefore, this study aimed to determine meropenem concentrations and consequently perform TDM effectively using the smallest possible sample volume. Volumetric absorptive microsampling (VAMS) is a sampling technology developed to collect a small, precise volume of blood. For the applicability of VAMS in TDM, plasma concentrations must be reliably calculated from whole blood (WB) collected by VAMS. METHODS: VAMS technology using 10 µL of WB was evaluated and compared with EDTA-plasma sampling. High-performance liquid chromatography with UV detection was applied to quantify meropenem in VAMS and plasma samples after the removal of proteins by precipitation. Ertapenem was used as the internal standard. Samples were collected simultaneously from critically ill children receiving meropenem using VAMS and traditional sampling. RESULTS: It was found that no consistent factor could be determined to calculate meropenem plasma concentrations from the WB, indicating that VAMS was not reliable in the TDM of meropenem. Therefore, to reduce the required sample amount in pediatric patients, a method for quantifying meropenem from 50 µL of plasma with a lower limit of quantification of 1 mg/L was developed and successfully validated. CONCLUSIONS: A simple, reliable, and low-cost method was established using high-performance liquid chromatography-UV to determine the concentration of meropenem in 50 µL of plasma. VAMS using WB does not seem to be suitable for TDM of meropenem.

Systematic Evaluation of Pharmacokinetic Models for Model-Informed Precision Dosing of Meropenem in Critically Ill Patients Undergoing Continuous Renal Replacement Therapy.

The altered pharmacokinetics of renally cleared drugs such as meropenem in critically ill patients receiving continuous renal replacement therapy (CRRT) might impact target attainment. Model-informed precision dosing (MIPD) is applied to individualize meropenem dosing. However, most population pharmacokinetic (PopPK) models developed to date have not yet been evaluated for MIPD. Eight PopPK models based on adult CRRT patients were identified in a systematic literature research and encoded in NONMEM 7.4. A data set of 73 CRRT patients from two different study centers was used to evaluate the predictive performance of the models using simulation and prediction-based diagnostics for i) a priori dosing based on patient characteristics only and ii) Bayesian dosing by including the first measured trough concentration. Median prediction error (MPE) for accuracy within |20%| (95% confidence intervals including zero) and median absolute prediction error (MAPE) for precision ≤ 30% were considered clinically acceptable. For a priori dosing, most models (n = 5) showed accuracy and precision MPE within |20%| and MAPE <35%. The integration of the first measured meropenem concentration improved the predictive performance of all models (median MAPE decreased from 35.4 to 25.0%; median MPE decreased from 21.8 to 4.6%). The best predictive performance for intermittent infusion was observed for the O'Jeanson model, including residual diuresis as covariate (a priori and Bayesian dosing MPE within |2%|, MAPE <30%). Our study revealed the O'Jeanson model as the best-predicting model for intermittent infusion. However, most of the selected PopPK models are suitable for MIPD in CRRT patients when one therapeutic drug monitoring sample is available.

Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients.

Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from -15.4 to 124.6%/-0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use.

[Therapeutic drug monitoring and pharmacokinetic models as a strategy for rational antibiotic therapy in intensive care patients]. / Therapeutisches Drugmonitoring und pharmakokinetische Modelle als Strategie zur rationalen Antibiotikatherapie bei IntensivpatientInnen.

BACKGROUND AND OBJECTIVE: Antibiotic dosing in intensive care patients is complex due to pharmacokinetic (PK) alterations. The aim of this article is to illustrate the role of therapeutic drug monitoring (TDM) and PK models to individualize antibiotic dosing. MATERIAL AND METHODS: Guidelines and recommendations are discussed in the context of clinical practice and the prerequisites for routine TDM of different antibiotics are presented. In addition, the benefits and limitations of TDM are discussed. The advantages and disadvantages of TDM and PK models are described and the resulting future options are presented. RESULTS: In the clinical routine, the peak or trough concentrations of antibiotics in blood are measured depending on the antibiotic class. Prerequisites for a purposeful TDM are a coordinated blood sampling and a prompt reporting of findings. As target ranges are not uniformly defined following rules, dosage adjustments are difficult. The PK models offer a valid possibility to individualize the antibiotic therapy of intensive care patients. Areas of application are the calculation of the loading dose and the combination with TDM for treatment control. For whom and how often TDM is necessary and how it can best be combined with PK models or even replace them should be investigated in the future, in addition to evaluation of the optimal target area. CONCLUSION: The routine use of TDM for antibiotics in intensive care patients is only effective under the abovementioned conditions. By combination with PK models the treatment could be optimized in the future.