Therapeutic Drug Monitoring最新文献

Objectives: Sertraline is one of the most prescribed selective serotonin reuptake inhibitors. Pharmacokinetic variability of sertraline is extensive and primarily determined by metabolism mediated by the polymorphic enzymes CYP2C19 and CYP2B6. However, the metabolites formed by these 2 enzymes are poorly characterized. The aim of this study was to investigate the formation of the primary sertraline metabolites in relation to CYP2B6 and CYP2C19 phenotypes in a real-life patient population.

Methods: The study included patients who (1) were genotyped for CYP2B6, CYP2C19 , and CYP2C:TG and (2) had undergone therapeutic drug monitoring (TDM) of sertraline at the Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway. Semiquantitative levels of sertraline and its 2 primary phase I metabolites, N-desmethyl sertraline and α-hydroxy sertraline ketone, were extracted by reprocessing high-resolution mass spectral data from previously analyzed TDM samples. Metabolite-to-sertraline ratios were calculated using peak areas and compared between genotype-translated phenotype subgroups using normal metabolizers (NMs), that is, carriers of CYP2B6*1/*1 and CYP2C19*1/*1 genotype, as reference.

Results: Overall, 470 patients representing 769 TDM measurements were included. Compared with CYP2C19 NMs and CYP2B6 NMs, CYP2C19 PMs (n = 16) showed a significant reduction in the formation of α-hydroxy sertraline ketone, with levels approximately 90% lower than in NMs ( n = 92) ( P < 0.001). CYP2B6 metabolism was most important for N-desmethyl sertraline formation with a 21.5% reduced metabolic ratio in CYP2B6 PMs ( n = 25) versus NMs ( n = 274) ( P = 0.02).

Conclusions: This study demonstrates that α-hydroxy sertraline ketone is mainly formed by CYP2C19, whereas CYP2B6 is the major enzyme responsible for the formation of N-desmethyl sertraline. These novel findings provide a clinical foundation for applying metabolite monitoring as part of sertraline TDM.

Background: The concentration of antiseizure medications in vivo has a significant influence on therapeutic and adverse effects. To use antiseizure medications safely and rationally, it is crucial to establish a method of measuring their blood concentration, provide individualized medication guidance to patients, and avoid adverse effects through therapeutic drug monitoring. Therefore, we developed a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for the rapid detection of antiseizure medication concentrations in blood and applied it to the monitoring of clinical antiseizure medication therapy.

Methods: A Waters HPLC-ESI TQ-D with a Waters BEH C18 column (50 × 2.1 mm, 1.7 µm) was used; the blood samples were injected after protein precipitation in 50% methanol in water, and the analysis was conducted within 3.5 minutes. The ion source for mass spectrometry was an electrospray ionization source with the multiple ion reaction monitoring mode. An isotope standard was selected as the internal standard.

Results: The linearity of each epileptic drug was good in the studied concentration range (R2 > 0.9930), and the lower limit of quantification was 0.01-8.00 mcg/mL, with intraday and interday precision less than 15.00%. The recoveries of the method ranged from 91.86% to 113.97%, and the stability of the method was good and unaffected by matrix effects.

Conclusions: The established LC-MS/MS method for the determination of antiseizure medication concentration was characterized by a simple operation procedure, short analysis time, high sensitivity, and good accuracy, providing a simple and reliable assay for the monitoring of antiseizure medication therapy.

Background: This study was conducted to develop a novel population pharmacokinetic (popPK) model for vancomycin in hemodialysis patients using a large, real-world data set and compare its predictive performance against published models for use in model-informed precision dosing.

Methods: Routine clinical care data from 2409 adult patients across 91 US sites, including 821 patients undergoing continuous renal replacement therapy (CRRT), were included in this analysis. A two-compartment popPK model was developed using a training set comprising 70% of the data and evaluated against a test set (30%). Predictive performance was evaluated using clinical accuracy, mean percent error, and normalized root mean square error against five published vancomycin popPK models for hemodialysis patients.

Results: The new popPK model, which incorporated fat-free mass, CKD-EPI 2021-estimated glomerular filtration rate, and age as covariates, and an additive model for CRRT clearance, demonstrated superior predictive performance. It showed significantly higher accuracy, lower bias, and lower normalized root mean square error compared with published models. The model accurately described vancomycin PK under CRRT, with an estimated additive CRRT clearance of 1.02 L/h. Inclusion of nonhemodialysis patients in model development stabilized parameter estimates without affecting predictive performance in CRRT patients.

Conclusions: We developed a novel vancomycin popPK model for adult CRRT patients that outperformed published models in a large real-world hemodialysis data set. This model improved predictive performance for serum vancomycin prediction, suggesting its potential to improve target attainment based on model-informed precision dosing for hemodialysis patients.

Background: Kratom use is rising, increasing the need for safety and tolerability studies of high-quality and well-characterized kratom products in humans. Kratom's risk-benefit ratio, recommended dose, treatment-emergent adverse events (TEAEs), abuse potential, and withdrawal require evaluation. Thus, the safety and tolerability of 4 escalating single and 15 daily dried kratom leaf powder doses in human volunteers were evaluated over 47 days in the largest controlled kratom-administration study to date.

Methods: A randomized, between-subject, double-blind, placebo-controlled, dose-escalation study of MitraLeaf kratom powder after single doses (SD), during 15 daily doses (multiple doses; MD), and a 23-day follow-up was conducted in 116 volunteers (49 MitraLeaf and 67 placebo). Twelve participants each received a SD of either 6.65, 13.3, 26.6, or 53.2 mg (n = 13) mitragynine in 500, 1000, 2000, or 4000 mg of MitraLeaf, respectively, with a 10-day follow-up. The same participants received 15 daily doses at the same concentration of SD mitragynine received, with a 27-day follow-up period. Inclusion criteria were nonsmoking healthy males and females who never used kratom or had not used kratom for ≥12 months, 18-55 years old, and BMI ≥18.5 and ≤29.9 kg/m2. Participants were excluded if they had known CYP3A4, CYP2D6, or CYP1A2 genetic polymorphisms.

Results: No serious adverse events or deaths were reported. TEAEs after SD or MD generally increased as the dose increased. Dizziness, nausea, and feeling of relaxation were the most commonly reported TEAEs after SD, and headache, feeling hot, increased alanine aminotransferase level, and nausea were most common after MD.

Conclusions: This SD and first MD controlled study shows that Mitragyna speciosa-derived MitraLeaf kratom powder was safe and well tolerated at the dose ranges tested, with no evidence of meaningful abuse potential or withdrawal.

Background: Sepsis, a condition characterized by the excessive release of inflammatory mediators, significantly alters body composition and physiology and is potentially altered by intensive care therapy. The present study aimed to expand a comprehensive physiologically based pharmacokinetic (PBPK) model for healthy individuals based on physiological changes associated with conditions in critically ill adult patients with sepsis and to validate its accuracy using fosfomycin as a therapeutically relevant medication.

Methods: A comprehensive literature search was conducted using various online databases, and pharmacokinetic (PK) data for fosfomycin were gathered. PKSim, a PBPK modeling program, was then used to construct and evaluate drug-specific and intravenous models. The robustness of the model was then evaluated for each of the crucial PK parameters using the average fold error, predicted/observed ratios, and visual predictive checks.

Results: Analysis of the performance of the model showed that all PK parameters were within the 2-fold allowable error range. The average fold error value, which is an indication of how much model predictions normally differ from the reported value, was 0.81 after intravenous infusions in healthy individuals, which validates the proposed fosfomycin model. In patients with sepsis, the RPre/obs ratio was predicted to be 310.2 mcg/mL, which is comparable to the value of 332.3 mcg/mL from the literature. In addition, the results were further analyzed to show how sepsis affects the area under the systemic concentration over a time curve from zero to infinity (AUC0-∞). The results of this analysis serve as a reference for clinicians determining the optimal dosage of fosfomycin.

Conclusions: The proposed model, which is effective in predicting fosfomycin PK variations in both healthy and critically ill patients with sepsis, will aid in optimizing the dose of fosfomycin for critically ill patients.

Background: Patients with cancer, particularly those receiving chemotherapy, may require higher initial amikacin doses. This highlights the need for individualized dosing strategies supported by pharmacokinetic models. Early therapeutic drug monitoring is essential to optimize efficacy and safety. Therefore, this study aimed to develop a population pharmacokinetic model of empirical amikacin administered in high-risk adults with neutropenia and hematological malignancies. Subsequently, Monte Carlo simulations were performed to assess whether the locally recommended dosing regimen achieves the predefined therapeutic targets.

Methods: The study included 83 patients with hematological disorders. A one-compartment population pharmacokinetic model was developed using NONMEM to evaluate multiple clinical covariates for their effects on clearance and volume of distribution. These included renal function estimates (Cockcroft-Gault, Chronic Kidney Disease Epidemiology Collaboration 2021, Modification of Diet in Renal Disease Study Equation [4-variable]), body size metrics, and laboratory parameters. Monte Carlo simulations were conducted to assess 4 once-daily amikacin dosing regimens (15-30 mg/kg) by estimating 1-h peak concentrations across various clinical scenarios.

Results: The final model identified albumin, body size, and creatinine clearance, adjusted for body weight, as key covariates influencing amikacin pharmacokinetics. Moreover, simulations suggested that the standard 15-mg/kg dose may be suboptimal in certain clinical subgroups.

Conclusions: Renal function and serum albumin substantially affect amikacin exposure. Therefore, model-informed dosing strategies may be required to optimize empirical amikacin therapy in high-risk patients with neutropenia and hematological malignancies.

Background: Treatment strategies for hematological cancers and immunological diseases increasingly incorporate targeted oral therapies. These drugs provide improved quality of life but exhibit complex pharmacokinetics. Therapeutic drug monitoring (TDM) may help optimize treatment in various clinical situations, including managing drug-drug interactions, assessing adherence, evaluating exposure-response relationships, and investigating suspected drug toxicities. In this study, we developed and validated a sensitive liquid chromatography-tandem mass spectrometry method for simultaneous quantification of multiple targeted therapies and applied this method to clinical samples for TDM.

Methods: After simple protein precipitation of plasma samples, chromatographic separation was performed on a UPLC system coupled with MS/MS in positive ionization mode. The mobile phase consisted of a gradient elution using 10 mM of ammonium formate with 0.1% (v/v) formic acid (phase A) and acetonitrile with 0.1% (v/v) formic acid (phase B), at a flow rate of 300 µL/min.

Results: The analysis time was 7.0 minutes per run. Calibration curves were linear over the ranges of 0.5-500 ng/mL for ruxolitinib, tofacitinib, baricitinib, and hydroxyquizartinib AC488; 5-2500 ng/mL for asciminib, gilteritinib, and quizartinib; 50-10000 ng/mL for ivosidenib, venetoclax, midostaurin, CGP52421, CGP62221, and pacritinib; 100-100000 ng/mL for enasidenib; and 500-100000 ng/mL for eltrombopag. All analytes showed correlation coefficients above 0.99. Intra- and interday precision values were below 14.67%.

Conclusions: We developed and validated a sensitive liquid chromatography-tandem mass spectrometry method requiring only 50 µL of plasma volume for the quantification of 12 targeted oral anticancer drugs and 3 active metabolites. This multianalyte assay offers strong potential for TDM in patients receiving contemporary anticancer treatments.

Background: High-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS) is a critical analytical technique for determining the different forms of arsenic present in human urine. A new method was developed to overcome the shortcomings, such as poor resolution between critical arsenic species, of previously validated methods. The goal was to achieve resolution between 6 arsenic species that can be present in urine, including 2 common nontoxic organic species (arsenobetaine [AsB] and arsenocholine [AsC]), 2 methylated species with intermediate toxicity (monomethylarsonic acid [MMA] and dimethylarsinic acid [DMA]), and toxic inorganic forms (arsenous acid [AsIII] and arsenic acid [AsV]). In addition, efforts have been focused on identifying 2 unknown arsenic peaks present in approximately 5% of the patient population.

Methods: HPLC-ICP-MS was developed using an anion-exchange analytical column with upfront dilution of urine samples. Some samples were reinjected into a high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer (QTOF-MS) to identify the unknown compounds.

Results: The newly developed method achieved adequate resolution for 6 known arsenic species. The method was successfully validated after additional challenges, such as stabilizing the transformation of certain arsenic species (AsB and AsIII), were resolved. A retrospective analysis of 3050 urine samples revealed positivity rates of 79.1% and 54.7% for AsB and DMA, respectively. All the other species were detected ≤2% of the time. Two unknown arsenic species analyzed by HPLC-QTOF-MS were identified as dimethylarsinoyl acetic acid (DMAA), a possible arsenosugar metabolite, and p-arsanilic acid (ASA), a likely contaminant from preanalytical exposure to urinalysis test strips.

Conclusions: This new method improves the reliability of arsenic speciation analysis in urine, benefiting both clinical laboratories and patients.

Background: Posaconazole (PCZ) is used to prevent invasive fungal infection (IFI) in patients with hematological malignancies or postallogeneic hemopoietic stem cell transplantation (allo-HSCT). However, data are lacking on the utility of PCZ therapeutic drug monitoring (TDM) in this cohort.

Methods: In this retrospective, single-center cohort study, the authors identified patients with hematological malignancies or post-allo-HSCT who were prescribed prophylactic PCZ and had TDM results available from 2015 to 2020 at Alfred Health, Australia. The primary objective was to identify risk factors associated with subtherapeutic target levels (<0.7 mg/L).

Results: The authors analyzed 230 PCZ courses (142 patients, 1056 measurements). Of these, 18% of measurements were subtherapeutic and 46.5% of courses were associated with at least 1 subtherapeutic level. Most subtherapeutic levels (51.1%) occurred within the first 14 days after initiation. On multivariate regression, for every 0.1 mg/L increase in D7 PCZ level, there is a 77% reduced risk of having a future subtherapeutic level (Odds Ratio 0.23, 95% CI, 0.12-0.43, P < 0.001). Patients with courses with subtherapeutic levels took longer to attain target threshold (11.5 vs 8.0 days, P 0.01). A guideline-directed, TDM-guided dose-escalation strategy, led by pharmacists, aided in improving target attainment, with 73.4% of courses requiring only 1 dose increase to achieve targets. Adverse events were infrequent (10.9% of courses, 25/230), with hepatotoxicity (9.1%, 21/230) being most prevalent. Breakthrough infection was rare (5.7%).

Conclusions: PCZ levels remain below target for nearly one-half of PCZ courses. Regular TDM during the first 14 days is critical, and patients with subtherapeutic levels at D7 should be prioritized for further close monitoring. A stepwise approach to pharmacist-led TDM-guided dose changes that follows a dosing guideline can improve target attainment for PCZ.

Background: The emergence of novel psychoactive substances (NPSs) has overwhelmed forensic, health care, and regulatory systems. Conventional analytical techniques are ineffective for identifying known compounds but fail against newly synthesized NPSs lacking reference standards. This review explores the roles of artificial intelligence (AI) and machine learning in addressing growing challenges in NPS identification and characterization.

Methods: The authors reviewed the current forensic workflows and the integration of AI-based approaches, including deep learning models, chemical language models, and spectral prediction tools. Particular emphasis was placed on the DarkNPS framework, which uses Long Short-Term Memory networks and SMILES-based data augmentation to generate millions of plausible NPS structures, and on spectral prediction tools, such as Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) and novel psychoactive substances-mass spectrometry, for in silico MS/MS spectra generation. Additional emerging AI technologies, such as transformers, graph neural networks, and multimodal frameworks, were also examined.

Results: AI-based systems significantly reduced the time and resources required for NPS identification by enabling structure generation, spectral prediction, and prioritization without physical standards. The DarkNPS model successfully predicted structures for >8.9 million plausible NPS compounds, with over 90% of the future market NPS accurately anticipated. In silico MS/MS spectral libraries built using AI tools demonstrated high cosine similarity scores (>0.7) with the experimental spectra, allowing top-hit identification in 75%-90% of the cases. This improved efficiency can facilitate more accurate diagnoses, guide timely treatment decisions, and support public health responses to emerging NPS threats.

Conclusions: Integrating AI with traditional analytical chemistry significantly enhanced the speed, scope, precision, and utility of NPS identification, marking a promising shift in forensic toxicology and chemical surveillance.