Therapeutic Drug Monitoring最新文献

Background: Model-informed precision dosing is increasingly integrated into therapeutic drug monitoring to optimize individualized pharmacotherapy, with maximum a posteriori Bayesian estimation serving as a key tool for pharmacokinetic parameter estimation. However, the impact of residual error specification on its precision remains underexplored. We hypothesized that reducing residual error would decrease the imprecision of area under the concentration-time curve predictions.

Methods: Using rich pharmacokinetics datasets comprising 321 profiles for tacrolimus, iohexol, and mycophenolate mofetil, maximum a posteriori Bayesian estimation was applied based on sparse sampling (3 time points) using published population pharmacokinetics models. Proportional residual error settings were varied as near-zero (10-8%), low (1%, "Flat1"), and the original published model.

Results: A low proportional error reduced the overall root mean square error of individual area under the concentration-time curve predictions by 30%-40% compared with the original residual error configuration. For tacrolimus, the root mean square error decreased from 28.5% under the published error model to 16.3% with a 1% error setting, whereas for iohexol, a near-zero residual error achieved up to a 40% reduction. The near-zero error scenario yielded the most accurate area under the concentration-time curve estimates for 45%-62% of patients across drug models.

Conclusions: Reducing residual errors in maximum a posteriori Bayesian estimation significantly strengthens the influence of observed data on posterior calculations, improving the precision area under the concentration-time curve and dosing accuracy without requiring additional data acquisition or model redevelopment.

Background: Carbon monoxide (CO) poisoning is associated with high morbidity and mortality rates. Although accidental inhalation is the most common cause, suicide through CO from a mixture of formic and sulfuric acids is extremely rare and poses unique risks to first responders.

Case presentations: Two male individuals died in their vehicles after mixing formic and sulfuric acid to generate CO. Both cases showed characteristic cherry-red or pink livor mortis and high postmortem carboxyhemoglobin levels (73% and 85%, respectively). In both scenes, labeled acid containers and enclosed spaces were present, with 1 case noting an unusual odor.

Conclusions: Chemically induced CO poisoning, although rare, requires heightened awareness among first responders because of the danger of undetected exposure. Key indicators include unusual livor mortis, enclosed or taped-off spaces, chemical smells, and the presence of acid containers. Postmortem toxicology is crucial for confirming a diagnosis and understanding the cause of death.

Background: Implant-associated spinal infections (IASI) pose challenges for outpatient management due to the need for frequent intravenous antibiotic administration. Dalbavancin has a prolonged half-life and is a practical alternative.

Methods: Two cases of IASI were treated with dalbavancin in an outpatient setting over 10-12 weeks. One patient received therapeutic drug monitoring (TDM)-guided dosing, while the other was managed with fixed-interval dosing. Dalbavancin plasma concentrations were measured using liquid chromatography-tandem mass spectrometry, and dosing adjustments were guided by pharmacokinetic modeling.

Results: In the TDM-guided case, three dalbavancin doses were sufficient to maintain therapeutic plasma concentrations (≥8 mg/L), whereas the fixed-interval approach required four doses. Both patients successfully completed therapy without recurrence of the infection during follow-up.

Conclusions: TDM-guided dalbavancin therapy optimized drug exposure and reduced the number of doses compared with fixed-interval dosing, highlighting its potential to optimize treatment. Further research is required to establish standardized therapeutic drug monitoring protocols for the management of IASI.

Background: This study assessed the utility of a therapeutic drug monitoring (TDM)-guided expert clinical pharmacological advice (ECPA) program to optimize aggressive pharmacokinetic/pharmacodynamic (PK/PD) target attainment of novel beta-lactam/beta-lactamase inhibitor (BL/BLIc) combinations and cefiderocol.

Methods: All hospitalized patients who received TDM-guided ECPA with BL/BLIc (ceftazidime-avibactam, ceftolozane-tazobactam, or meropenem-vaborbactam) or cefiderocol were assessed retrospectively. Three performance indicators were identified: the average number of ECPAs delivered per month of availability of the program and the ratio between the total number of ECPAs recommending dosing adjustment and the total number of ECPAs, at the first and at subsequent TDM assessments. The relationships between aggressive PK/PD target attainment and clinical and microbiological outcomes were assessed.

Results: A total of 595 ECPAs were administered to 263 patients to optimize 319 treatment courses. Novel agents were mostly used for targeted therapy (79.6%) by continuous infusion (CI; 82.8%). In the first TDM assessment, dose increases were mostly required for patients receiving intermittent/extended infusion (II/EI) (51.9% vs. 6.4%; P < 0.0001), whereas dose decreases were mostly recommended for patients receiving CI (60.3% vs. 23.1%; P < 0.001). In subsequent TDM assessments, the overall proportion of ECPAs recommending dosing adjustments decreased in both groups (57.1% and 39.3% in the II/EI and CI groups, respectively). Aggressive PK/PD target attainment was associated with the highest microbiological eradication rate for ceftazidime-avibactam (79.6% out of 86.0%; P < 0.001), and the highest clinical cure rate for ceftazidime-avibactam (64.2% out of 78.1%; P < 0.001) and cefiderocol (50.0% out of 51.5%; P = 0.006).

Conclusions: A dedicated TDM-guided ECPA program may be helpful for optimizing the use of novel agents in settings with a high prevalence of multidrug-resistant pathogens.

Background: Common genetic variants in CYP3A4 together only explain a limited amount of the variability in tacrolimus clearance. This cross-sectional study aimed to explore the extent to which pharmacokinetic variability can be explained by methylation of the CYP3A4 gene.

Methods: Residual tissue material from liver biopsies routinely collected 6 months post-transplantation was used. Inclusion criteria were tacrolimus once daily (Advagraf) in a steady state (ie, no dose change in the previous 3 days); assessment of tacrolimus pharmacokinetics within 3 weeks of the biopsy; and no documented episode of rejection for at least 3 months prior. Patients and liver donor tissue were genotyped. Only patients in which the patient and the donor had a genotype that did not express the CYP3A5 protein were included. The liver biopsy tissue material was then analyzed using an Illumina Infinium MethylationEPIC array.

Results: Of the 28 patients who met the inclusion criteria, 23 passed the quality control assessment required for the methylation analysis. Increased methylation of 1 of the 10 methylation probes within the CYP3A4 gene region (cg19046783) was positively correlated (Spearman correlation coefficient, 0.52) with the dose-normalized area under the concentration versus time curve (AUC) 0-24h ( P = 0.01). When quantified using univariate linear regression, this probe explained 18% of the variation in the dose-normalized AUC 0-24h . Interestingly, cg19046783 had the lowest mean methylation and highest biological variation.

Conclusions: Increased methylation of 1 of the 10 methylation probes within the CYP3A4 gene region (cg19046783) was positively correlated with increased dose-normalized AUC 0-24h , which explained 18% of the variation in the dose-normalized AUC 0-24h using univariate linear regression.

Background: Mycophenolic acid (MPA) is a cornerstone of immunosuppressive treatment in kidney transplant recipients (KTRs). Traditional therapeutic drug monitoring for MPA is based on venous blood sampling. Finger-prick capillary microsampling is patient-friendly and enables limited sampling to predict the area under the curve. A liquid chromatography-tandem mass spectrometry assay to detect MPA and its metabolite mycophenolic acid glucuronide (MPAG) using volumetric absorptive capillary microsampling (VAMS) was developed and clinically validated.

Methods: An assay based on VAMS and liquid chromatography-tandem mass spectrometry was validated bioanalytically and clinically. Agreement between dried microsamples and plasma samples was investigated in KTR on mycophenolate mofetil therapy. Paired microsamples and plasma samples were obtained before and at 0.5 and 2 hours postdosing. The samples were divided into development (75%) and validation (25%) datasets. Conversion from VAMS to plasma concentrations was established using a regression model, with at least 67% of paired samples required to fall within a mean relative difference of ±20%.

Results: Twelve KTRs (median age: 49 years) provided 69 paired microsamples and plasma samples. For the VAMS method, the between-series mean accuracy was 90%-106% with a coefficient of variation <7% at concentrations of 0.25-32 mg/L (MPA) and 2.5-320 mg/L (MPAG). A conversion equation based on the regression model was applied and validated using an independent dataset. The mean relative differences between corrected microsamples and plasma samples were 1.9% for MPA and 2.7% for MPAG, with <5% outside ±20% for both analytes. Dried microsamples were stable for 3 months at ambient temperature.

Conclusions: The VAMS method demonstrated acceptable performance. MPA and MPAG can be reliably quantified using VAMS and are suitable for patient self-sampling in clinical pharmacokinetics studies of KTR.

Background: Population pharmacokinetic (popPK) models are essential tools for optimizing ustekinumab (UST) dosing for the treatment of inflammatory bowel disease (IBD) through therapeutic drug monitoring. The external validation of these models is necessary to ensure their predictive performance and clinical utility. The aim of the study was to externally validate 4 published popPK models of UST in a real-world cohort of patients with IBD using prediction-based and simulation-based diagnostics, as well as Bayesian forecasting.

Methods: Four popPK models of UST, identified through a systematic literature review, were evaluated using data from 99 patients with IBD and 374 serum UST concentrations. Predictive performance and Bayesian forecasting were assessed using statistical metrics, including mean prediction error, median prediction error (MDPE), and median absolute prediction error (MADPE). The acceptability criteria (MDPE ±20%, MADPE ≤30%, F20 ≥35%, and F30 ≥50%) were applied.

Results: None of the models satisfied the predefined acceptability criteria. The Xu et al model demonstrated the best performance, achieving an MDPE of 19.55% and the lowest RMSPE (2.88 mcg/mL), but F20 (20.1%) and F30 (32.4%) values fell below thresholds. The model proposed by Adedokun et al showed strong results in simulation-based diagnostics, with only 5.6% of the observed concentrations outside the prediction interval.

Conclusions: The models developed by Xu et al and Adedokun et al exhibited the most promising predictive performance and potential clinical applicability for model-informed precision dosing. Refinements to these models and further research are required to enhance their use in personalized UST therapies for IBD.

Background: Amikacin (AMK) is used to treat gram-negative bacterial infections in intensive care unit (ICU) patients. However, its narrow therapeutic range and high interindividual variability can lead to toxicity and ineffectiveness. This study aimed to establish a roadmap for AMK therapeutic drug monitoring in critically ill patients with cancer to provide a Bayesian estimator of bedside applicability.

Methods: An observational retrospective study was conducted on oncological patients admitted to the ICU, treated with AMK as a 30-min intravenous infusion at 5.8-39.2 mg/kg. The plasma concentrations were analyzed using a nonlinear mixed-effects modeling approach. Covariate analyses were performed using anthropometric and laboratory data, concomitant drugs, and comorbidities. The model predictive performance was compared with previous AMK dosing approaches using the Bland-Altman method.

Results: The concentration-time profiles were best described using a one-compartment model with linear elimination. The estimated glomerular filtration rate was a significant covariate of clearance (CL), explaining 16% of the interpatient variability. Body weight was positively correlated with the volume of distribution, accounting for 4% of the variability. Our model reduced the bias in the estimates of individual CL values compared with that of other available methods and was further implemented in DoseMeRx for real-time application at the bedside.

Conclusions: This study provides an effective example of a Bayesian estimation method for individualizing AMK doses in critically ill patients with cancer. Collecting more comprehensive patient information, including additional biomarkers for renal function, could further refine the model and improve its predictive performance in this special population.

Background: Pharmacokinetic-based dosing calculators for individualized drug dosing remain underutilized in clinical practice, often due to poor usability and a lack of user-centered design. Understanding how health care professionals interact with these tools can inform design strategies and enhance usability.

Methods: Health care professionals wore eye-tracking glasses while using a codesigned vancomycin dosing calculator with supporting clinical information to complete example clinical scenarios. Eye-tracking data were collected for 23 predefined areas of interest, and fixation sequences were analyzed. A Post-Study System Usability Questionnaire was administered to assess the tool's perceived usability.

Results: Eleven pharmacists and three doctors participated in the study. The highest average dwell times were recorded for the pharmacokinetic plot, dosage regimen selection, dosing history, drug concentrations, and the area under the concentration-time curve and dose visualization area. Participants generally viewed patient demographic information first and pharmacokinetic and dosage regimen information last. Considerable heterogeneity was observed among participants' fixation sequences, with frequent eye movements between key areas, particularly between the pharmacokinetic plot and dosage regimen selection, and between dosing history and drug concentrations. Participants expressed a preference for these elements to be positioned close together.

Conclusions: Understanding how health care professionals interact with decision support systems is essential for developing user-friendly tools that align with clinical workflows. Eye-tracking data provided valuable insights into user engagement patterns with the dosing calculator and clinical information interface. These insights will guide future design strategies to address usability barriers that limit the utilization of dosing calculators in clinical practice and promote the implementation of individualized drug dosing.

Background: Home sampling for therapeutic drug monitoring (TDM) shows promise as a valuable tool, particularly for stable patients with limited access to health care. Quantitative dried blood spot (qDBS) technology has emerged as a promising solution. This study aimed to develop a method for simultaneous quantification of fluconazole, voriconazole, isavuconazole, posaconazole, itraconazole, and hydroxyitraconazole and to investigate key technical issues such as transport stability and the conversion formulas between different matrices.

Methods: The extraction protocol was optimized using spiked qDBS samples with a series of methanol/water and acetonitrile/water mixtures. Stability tests were performed at 2-8°C and 45°C to mimic environmental conditions of regular mail delivery. Linear regression for converting voriconazole concentrations from qDBS to plasma samples was established using 101 routine TDM samples.

Results: The optimal extraction solvent was acetonitrile/water (70/30). The method validation demonstrated excellent linearity (R 2 > 0.99), accuracy (recovery of 92.1%-104.2%), and precision (intraday and interday variability <3.3% and 5.7%, respectively). Stability tests confirmed that all azole drugs remained stable, even at 45°C for 7 days. Conversion formula indicated that qDBS sample concentration was equivalent to that in plasma, as supported by Bland-Altman analysis, which revealed a mean bias of -8.49% and 95% limits of agreement ranging from -20.11% to +3.13%.

Conclusions: This study established a sensitive, accurate, precise, and robust quantification method in qDBS samples containing only 10 μL of whole blood. Stability tests showed that samples could be safely mailed, offering a cheaper alternative to cold chain logistics. A conversion formula using routine TDM samples was developed, providing a straightforward and cost-effective approach for establishing a conversion formula that is potentially applicable to other TDM analytes. This study represents an in vitro analytical validation, and future clinical research is needed to translate the analytical method from bench to bedside.