Therapeutic Drug Monitoring最新文献

Background: The population pharmacokinetic (popPK) variability of ciprofloxacin in patients in intensive care units (ICUs) is unclear. Two popPK models of ciprofloxacin in those in the ICU were externally cross-validated to determine if a published popPK model can be applied for model-informed precision dosing or if a new popPK model needs to be developed. The predictive performance of the 2 popPK models was evaluated.

Methods: Data were collected from patients in the ICU at Amsterdam University Medical Center (AUMC), and a popPK model for ciprofloxacin was developed using nonlinear mixed-effects modeling. The data and the published pharmacokinetic model from the ICU of the Radboud University Medical Center (RUMC) were used for cross-validation. The RUMC dataset was used to externally validate the AUMC model and vice versa. The predictive performance of the models was evaluated by comparing the population-predicted and corresponding observed concentrations in the dataset. The primary endpoints were bias and precision, calculated as the mean percentage error (MPE) and normalized root mean squared error (NRMSE), respectively. Visual predictive checks (VPCs) and Bland-Altman plots visualized predictive performance.

Results: The AUMC dataset consisted of 159 concentration-time data points from 32 patients, and the RUMC dataset consisted of 531 samples from 39 patients. A 2-compartment linear model with modification of diet in renal disease as a covariate for ciprofloxacin clearance most accurately fit both study populations. The final AUMC model predicted the RUMC population data with an MPE of -3.87% (95% CI, -7.56 to -0.185) and an NRMSE of 44.05% (95% CI, 39.48-48.19). The final RUMC model predicted the AUMC population data with a nonsignificant MPE of -31.29% (95% CI, -73.56 to -10.98) and an NRMSE of 64.02% (95% CI, 48.61-76.38). pcVPC indicated acceptable predictive performance because the observed data fell within the 95% prediction CIs; the AUMC model overestimated the variability. The Bland-Altman plots confirmed that both models were imprecise, overrepresenting large negative relative errors.

Conclusions: Neither ciprofloxacin popPK model accurately predicted external data, and the AUMC model exhibited bias. The prior RUMC model is unsuitable for the AUMC ICU population and vice versa. We recommend either adapting an existing popPK model from literature or creating a new popPK model specifically tailored to the ICU population.

Background: This study aimed to evaluate the predictive performance of published lamotrigine (LTG) population pharmacokinetic (PPK) models using an external data set of Chinese patients with epilepsy or postneurosurgery.

Methods: In total, 348 concentration measurements from 94 Chinese children and 254 Chinese adults with epilepsy or postneurosurgery were used for external validation. Data on published LTG PPK models were obtained from the literature. The predictability of the models was assessed using prediction-based diagnostics (eg, F20 and F30), simulation-based diagnostics, and Bayesian forecasting.

Results: The results of prediction-based diagnostics for all 10 models were unsatisfactory. The best-performing models, characterized as one-compartment models with nonlinear pharmacokinetics, incorporated weight as a key covariate and included interindividual variability for both clearance and volume of distribution. These models achieved exceptional predictive performance in simulation-based diagnostics and Bayesian forecasting, with IF 30 values of 90.32%, 97.23%, and 99.61%, respectively, demonstrating superior precision and accuracy. Bayesian forecasting improved the predictive accuracy of 80% of the models, significantly enhancing model predictability.

Conclusions: The published PPK models show extensive variation in predictive performance for extrapolation among Chinese patients with epilepsy or postneurosurgery. The lack of key covariates (such as concomitant medications, genetic polymorphisms, and age stratification) and fixed parameters of volume of distribution and absorption rate constant in the PPK modeling of LTG may explain its unsatisfactory predictive performance. Bayesian forecasting significantly improves the model predictability and may help individualize LTG dosing.

Background: Ceftobiprole is a broad-spectrum cephalosporin. It is currently approved for the treatment of community- and hospital-acquired pneumonia. However, the recommended dosage regimen of ceftobiprole may not be sufficient to achieve the optimal pharmacokinetic/pharmacodynamic criterion in critically ill patients. The study aimed to evaluate whether the dosage regimens proposed by the manufacturers ensure that the optimal pharmacokinetic/pharmacodynamic criterion is achieved in over 90% of critically ill patients.

Methods: Ceftobiprole concentrations were measured in 27 patients admitted to intensive care unit. An external evaluation of published population pharmacokinetic models was performed using simulations. The model that best described the data was used to evaluate the dosage regimens proposed for intensive care unit patients by evaluating the probability of attaining the optimal pharmacokinetic/pharmacodynamic criterion (100% fT > 4 * minimum inhibitory concentration). In addition, the same model was used to suggest dosage regimen adjustments for these patients.

Results: Of the 4 models evaluated, Muller's population pharmacokinetic model was selected as the best for describing the concentrations observed in 27 patients. Simulations performed with this model have shown that the manufacturer's dosing regimens do not achieve the optimal pharmacokinetic/pharmacodynamic criterion in critically ill patients. Consequently, adaptation of dosing regimens to ensure ceftobiprole effectiveness in at least 90% of the patients was proposed.

Conclusions: The proposed dosing regimens can be used to guide ceftobiprole administration in critically ill patients. However, measurement of ceftobiprole plasma concentration remains essential, at least once, to confirm patient exposure.

Background: Therapeutic drug monitoring of mycophenolic acid (MPA) is limited due to the requirement for intensive pharmacokinetic sampling to assess the area under the curve (AUC). Limited sampling strategies (LSS) offer a practical alternative; however, enterohepatic recirculation (EHR) affects prediction accuracy and precision. This study is the first to develop LSS models capable of simultaneously predicting the AUC of MPA, its metabolites [mycophenolic acid glucuronide (MPAG) and acyl mycophenolic acid glucuronide (Acyl-MPAG)], and MPA EHR in kidney transplant recipients (KTRs).

Methods: Intensive pharmacokinetic sampling was conducted in 84 adult KTRs receiving mycophenolate mofetil. MPA AUC 0-12 was calculated, and MPA EHR was determined. During the development of the LSS models, a balanced representation of patients with high and low EHR was ensured. Multiple linear regression was used to develop AUC prediction models for MPA, MPAG, and Acyl-MPAG, as well as an EHR prediction model. The best models were selected based on prediction performance, the highest prediction concordance, and the shortest interval between the first and last samples.

Results: Three models for AUC 0-12 prediction were identified, incorporating 4, 5, and 6 concentration timepoints. The LSS model with 6 concentrations demonstrated the best performance, with excellent prediction concordance (100% for MPA and MPAG, and 93% for Acyl-MPAG). The EHR prediction model included 4 concentrations and exhibited an ∼80% prediction concordance. An online calculator was developed for these models.

Conclusions: The developed LSS models simultaneously predict MPA, MPAG, and Acyl-MPAG AUC 0-12 using the same timepoints with high accuracy and precision. MPA EHR can be predicted using 4 concentration timepoints. The inclusion of late concentration timepoints is essential for the high predictive performance of LSS models.

Clinical trial notation: clinicaltrials.gov, NCT04953715.

Background: Optimal antibiotic dosing is challenging in critically ill neonates because of the substantial pharmacokinetic variability, which is influenced by factors such as immature renal function, body composition, and critical illness. The use of extracorporeal therapies adds complexity, making predictions difficult in many cases. Meropenem, a broad-spectrum antibiotic, is commonly used due to resistant gram-negative organisms in neonates; however, dosing guidelines for neonates on CARPEDIEM dialysis are lacking.

Methods: This is a case of a neonate with liver failure of unclear etiology, who was on CARPEDIEM dialysis and started on meropenem for sepsis due to extended-spectrum beta-lactamase-producing Escherichia coli and suspected meningitis. Blood samples were sent to an external laboratory for meropenem concentration measurements, and model-informed precision dosing was used to guide the dosing adjustments.

Results: Initially, meropenem was administered at 40 mg/kg every 8 hours with a 30-min infusion, resulting in exposures that exceeded those required to achieve free concentrations above 4 times the minimum inhibitory concentration for the entire dosing interval (100% f T >4xMIC). The dosing interval was adjusted to every 12 hours to avoid unnecessarily high exposure. The regimen was continued without further complications, and the patient underwent successful liver transplantation.

Conclusions: This case highlights the successful application of model-informed precision dosing to individualize meropenem therapy in a critically ill neonate with liver failure on CARPEDIEM dialysis. MIPD is a valuable tool for dose adjustment in patients with unique and unpredictable pharmacokinetics.

Background: Vancomycin is a glycopeptide antibiotic indicated in patients suffering from infections caused by gram-positive bacteria. Therapeutic drug monitoring is crucial because of its high interindividual variability, especially in pediatric populations. However, validated data guiding vancomycin monitoring in pediatric patients are lacking. This study aimed to assess vancomycin plasma concentrations in a Tunisian pediatric population according to patient's age and administration mode.

Methods: A retrospective study was conducted at department of Clinical Pharmacology of National Pharmacovigilance Center. It obtained approval from the Institutional Review Board at Charles Nicolle Hospital in Tunis, Tunisia. Patients included in this study were classified by age. Only vancomycin levels associated with initial doses were evaluated. Continuous and intermittent infusion modes were assessed.

Results: The study included 146 patients. Each age group was separately evaluated. Only 11.8% of initial trough concentrations were within the therapeutic range with an average dosage of 38 mg/kg/d. Using the continuous infusion, 29.5% of initial concentrations reached the therapeutic range with an average vancomycin dose of 44 mg/kg/d. Only 20.5% of plasma concentrations during continuous infusion were supratherapeutic, compared with intermittent infusion (29.4%). Infants and children required higher daily doses to achieve therapeutic range. Lower doses were needed for prematurely born neonates.

Conclusions: Although numerous studies have evaluated vancomycin prescribing practices in pediatric populations, clinical data validating recent recommendations remain lacking. More personalized dosing approaches, including Area Under the Curve-guided monitoring, should be established.

Background: This study aimed to identify population pharmacokinetic models suitable for optimizing individualized teicoplanin dosing regimens in adult Chinese patients.

Methods: PubMed and Web of Science were searched for teicoplanin population pharmacokinetic models developed for the general adult population. Patient data used for the external evaluation, including demographics, teicoplanin-related information (administration and blood concentration), and laboratory test values, were collected from the Beijing Electric Power Hospital. External evaluation was performed using the Nonlinear Mixed-Effects Modeling software. Models with excellent predictive abilities were identified, and Monte Carlo simulations were conducted.

Results: A total of 66 teicoplanin concentrations used for external evaluation were obtained from 62 patients. The model built by Ogami et al performed excellently, with a bias of -7.56% and inaccuracy of 26.28%. The model consisted of the following parameters: clearance (L/h) = (0.379 + 0.211 × creatinine clearance/100) × (total body weight/70) 0.75 ; volume (V) 1 (L) = 38.2 × (fat-free mass/70); Q (L/h) = 2.42 × (total body weight/70) 0.75 ; V2 (L) = 106 × (fat-free mass/70). The model was subsequently used in Monte Carlo simulations (n = 1000). For general infections (minimum plasma concentration [C min ] = 10-15 mg/L), the loading dose (LD) and maintenance dose (MD) of teicoplanin should be at least 400 mg to achieve the target concentration. For endocarditis or severe infections, where a target concentration (C min = 15-30 mg/L) is required, LD should be at least 800 mg. Alternatively, the LD and MD of teicoplanin should be at least 600 mg to achieve desired therapeutic levels.

Conclusions: By combining external evaluations using Nonlinear Mixed-Effects Modeling with Monte Carlo simulations, the model developed by Ogami et al was identified as the most suitable for guiding clinical dosing under different pathophysiological conditions.

Background: Therapeutic drug monitoring (TDM) of tyrosine kinase inhibitors may improve treatment outcomes and individualized therapy in patients with cancer. Compared with plasma, the standard TDM matrix, dried blood microsampling is associated with several advantages, including the collection of samples by the patients themselves in their home setting. This study aimed to compare dasatinib and imatinib concentrations in different blood-based matrices and to determine whether dried capillary blood collected via volumetric absorptive microsampling (VAMS) could be used as an alternative to perform TDM in patients with chronic myeloid leukemia.

Methods: In addition to venous liquid whole blood, plasma, and VAMS samples (referred to as venous VAMS) prepared thereof, also fingerprick capillary VAMS samples were collected from patients receiving dasatinib or imatinib treatment by skilled personnel in a controlled environment. All samples were analyzed using validated liquid chromatography tandem mass spectrometry methods.

Results: Fifty-three patients were included in the study: 33 were treated with dasatinib and 20 with imatinib. Although a bias between dasatinib and imatinib venous VAMS and liquid blood concentrations was observed, 94% and 95% of the samples, respectively, fulfilled the 20% difference acceptance criterion. Capillary and venous concentrations were interchangeable and independent of the collection time. Using venous blood-to-plasma ratios from a prior proof-of-concept study, the VAMS results for imatinib, but not dasatinib, could reliably be converted into plasma concentrations.

Conclusions: Through a clinical validation study, the authors demonstrated that VAMS is a viable alternative for imatinib monitoring in patients with chronic myeloid leukemia. For dasatinib, VAMS-based analysis may still allow for longitudinal follow-up (ie, provide insight into fluctuations in patients). As a next step, capillary microsampling can be integrated into the home sampling context.

Background: The purpose of the review is to explain and encourage the use of terminology that distinguishes between the steps of measurement and reporting of concentrations, interpretation of the measurements, and subsequent prediction of individualized doses. The principles of concentration-guided dosing (CGD) provide a rational basis for personalized dosing. Existing terminology such as therapeutic drug monitoring (TDM) or model-informed precision dosing (MIPD) may have multiple meanings or be imprecisely defined. A brief history of CGD reveals the evolution of more accurate terminology focused on using concentration observations to provide individual drug dose guidance to clinicians.

Methods: Relevant literature was identified using keyword searches such as "TDM," "therapeutic range," "individualized dosing," "target concentration intervention," "precision dosing," "MIPD," and "personalized dosing." Studies were included if they addressed the theoretical basis, clinical implementation, and/or effectiveness of CGD approaches. The findings were synthesized to underscore the relevance of a CGD approach in the context of clinical pharmacology.

Results: CGD is commonly implemented using either the therapeutic window approach (TWA) or the target concentration approach (TCA). The dosing approach is often not specified for TDM and MIPD. Clinicians, clinical pharmacologists, and pharmacists have typically been trained to view TWA as the gold standard for personalized dosing.

Conclusions: Although many clinicians are well-versed in dosing using TWA, understanding and awareness of the benefits of TCA are still lacking. TCA offers accurate, personalized treatment by guiding the clinical team to use an optimally effective and safe dose for each patient.