BACKGROUND:After kidney transplantation, therapeutic drug monitoring (TDM) of tacrolimus (Tac) relies mainly on the predose concentration (C0). However, C0 has limited ability to accurately reflect systemic Tac exposure. The area under the concentration-time curve (AUC) provides a more comprehensive measure of total systemic exposure and can be estimated using a limited sampling strategy (LSS). This review aimed to identify effective LSSs for predicting Tac AUC from 0 to 12 hours (AUC0-12) using 1, 2, or 3 blood samples collected between predose and 6 hours following administration of immediate-release Tac. METHODS:A literature search was conducted to identify studies reporting LSSs as predictors of Tac AUC0-12 in adult kidney transplant recipients. In the articles selected for review, the predictive performance of the strategies was evaluated based on the correlation between the predicted and the full reference AUC, as well as the associated bias and precision. Findings were synthesized using a narrative approach. RESULTS:Nineteen studies evaluating LSSs for predicting Tac AUC0-12, involving a total of approximately 600 patients, were reviewed. Compared with C0, most single concentrations measured within 6 hours of administration demonstrated a stronger correlation with Tac AUC0-12. Most 2- or 3-concentration time-point strategies could predict Tac AUC0-12 with percentage prediction error and absolute percentage prediction error of less than 10%. Correlations of 0.99 were achieved with some strategies. CONCLUSIONS:LSSs using as few as 2 or 3 concentrations obtained between predose and 6 hours after administration may estimate Tac AUC with strong predictive performance. The evidence supports the incorporation of intermittent AUC-based monitoring into TDM protocols as a complement to routine C0 measurement.
PURPOSE:Sitagliptin is an incretin enhancer used for treating type 2 diabetes mellitus. This review examined the pharmacokinetics (PK) of sitagliptin in healthy and diseased subjects. METHODS:Articles related to sitagliptin PK were obtained by searching Google Scholar, PubMed, Cochrane Library, and ScienceDirect. In total, 24 clinical studies encompassing plasma concentration-time profile data after oral and intravenous (IV) administration of sitagliptin were included. RESULTS:Sitagliptin displays a linear PK profile in healthy subjects as area under the concentration-time curves from zero to infinity (AUC0-inf); the maximum plasma concentration (Cmax) increases with dose. Its AUC0-inf after IV administration (3692.5 ± 504.9 ng·h/mL) and oral dosing (3217.8 ± 496.9 ng·h/mL) indicates an oral bioavailability of ∼87%. Its renal clearance is significantly reduced in patients with moderate hepatic impairment compared with that in healthy subjects (clinically insignificant owing to sitagliptin's wide therapeutic index and predominant renal elimination; dose adjustment is generally not required). Its Cmax is 162.93 ± 37.16 and 181.75 ± 33.44 ng/mL in fasting and fed states, respectively, representing a 1.12-fold increase with food intake (clinically insignificant). Sitagliptin AUC0-inf is 3.5-fold greater in patients with chronic kidney disease than in healthy subjects. Sitagliptin coadministration with gemfibrozil increases its exposure (Cmax increases from 282.9 ± 18.9 to 344.1 ± 14.5 ng/mL; AUC0-inf increases from 3621 ± 222.5 to 5574 ± 611.5 ng·h/mL). Moreover, dorzagliatin does not alter sitagliptin PK parameters. CONCLUSIONS:This review summarizes the available PK data on sitagliptin from published studies in healthy and diseased subjects. These data may be useful for developing physiologically based PK models and may provide additional information to support clinicians in optimizing sitagliptin dosing in patient populations.
BACKGROUND:Perampanel (PER) is the only third-generation anti-seizure medication targeting AMPA receptors, but its efficacy varies between patients. Whether KCNA3 polymorphisms influence PER response in drug-resistant epilepsy remains unknown. This study aims to evaluate the impact of KCNA3 rs2821557 polymorphisms on PER efficacy and plasma concentrations in patients with refractory epilepsy. METHODS:Blood samples were collected from 119 patients who received PER for at least 3 weeks, in accordance with inclusion and exclusion criteria. KCNA3 rs2821557 genotypes were identified using first-generation Sanger sequencing, and plasma PER concentrations were quantified by ultra-high-performance liquid chromatography to assess associations between the KCNA3 rs2821557 gene polymorphism, PER efficacy, and plasma drug concentrations. RESULTS:The therapeutic efficacy of PER in managing patients with refractory epilepsy was 65.55% (78/119). The proportion of patients with the KCNA3 rs2821557 TT genotype was significantly greater in the effective group than the ineffective group. Plasma PER concentrations in patients with CT or TT genotypes were significantly lower than in those with the CC genotype, a pattern also observed in patients aged 4-14 years, female patients, patients with a body mass index ranging from 18.5 to 24.0 kg/m, and patients with disease duration exceeding 1 year. CONCLUSIONS:The KCNA3 rs2821557 polymorphism is an important predictor of the therapeutic efficacy of PER in controlling epileptic seizures and a determinant of the attainment of therapeutic plasma PER concentrations.
BACKGROUND:Tacrolimus is a key component of immunosuppressive therapy after renal transplantation but is characterized by a narrow therapeutic index and considerable pharmacokinetic variability. Although the month 3 concentration-to-dose (C/D) ratio identifies fast metabolizers at an increased risk of inferior outcomes after transplantation, very early C/D ratios lack prognostic value. Therefore, identifying early exposure metrics that capture cumulative tacrolimus exposure rather than single time-point concentrations represents an important challenge for therapeutic drug monitoring. This study evaluated whether early tacrolimus underexposure (trough concentration <8 ng/mL during postoperative days 1-10) predicts 12-month acute rejection (AR) and examined its association with the month 3 metabolizer phenotype. METHODS:This retrospective single-center study analyzed 374 kidney transplant recipients with complete pharmacokinetic data for the month 3 C/D classification and an extended cohort of 609 recipients transplanted between 2007 and 2018 for rejection analyses. RESULTS:The primary end point was biopsy-proven AR within 12 months. A threshold of 0.29 for the proportion of postoperative days 1-10 with trough levels <8 ng/mL provided the highest sensitivity-specificity balance. Values above this cutoff independently predicted 12-month AR. Early tacrolimus exposure patterns showed only a modest ability to discriminate the month 3 metabolizer phenotype. CONCLUSIONS:Early tacrolimus underexposure is a simple and clinically relevant predictor of 12-month AR, complementing C/D-ratio-based risk stratification at 3 months. These findings align with the evidence that high early tacrolimus clearance and low tacrolimus exposure increase AR risk and support the recommendations for optimizing early tacrolimus dosing.
BACKGROUND:Tacrolimus is central to kidney transplant immunosuppression but causes toxicity. Optimal conversion regimens to sirolimus are unclear, and therapeutic drug monitoring is retrospective. We used pharmacokinetic (PK) modeling to identify conversion strategies to minimize under- or over-immunosuppression. METHODS:Adult kidney transplant recipients converted from tacrolimus to sirolimus at a single center (March 2007-January 2024) were reviewed. Demographics, conversion regimens, and therapeutic drug monitoring concentrations were collected. PK models were developed, conversion strategies were simulated using a combined tacrolimus-sirolimus range of 8.5-11.6 ng/mL, and relationships between drug exposure and laboratory outcomes were analyzed. RESULTS:Forty-five recipients underwent conversion. The most common approach halved tacrolimus for 5-7 days during sirolimus initiation. Tacrolimus and sirolimus clearance declined with age (-1.65% and -0.95% year-1, respectively). Sirolimus clearance fell with increasing hematocrit (-4.1% per percentage point). Tacrolimus dose predicted sirolimus needs (r = 0.62, P < 0.001). Higher combined exposure was associated with alanine transaminase elevation (r = 0.54, P = 0.004). Mean glomerular filtration rate rose by 4.6 mL·min-1·1.73 m-2 after conversion (P < 0.001); white cell and platelet counts fell by 13.9% and 19.2%, respectively. Simulations favored Abbreviated Overlap with a sirolimus loading dose, achieving the greatest time within range (48.2% versus 40.2% for Standard Overlap). Immediate Switch without a loading dose resulted in 67.5% of the transition period below 8.5 ng/mL. CONCLUSIONS:PK modeling supports risk-stratified protocol selection. Sirolimus dosing should be based on age, hematocrit, and tacrolimus requirements. Abbreviated Overlap with sirolimus loading is preferred for higher immunological-risk patients, and Standard Overlap for routine conversion. Laboratory monitoring preconversion and postconversion is necessary in clinical practice.
BACKGROUND:Venetoclax is a B-cell lymphoma 2 inhibitor with breakthrough therapy designations and is used in the treatment of acute myeloid leukemia, particularly in patients ineligible for conventional cytotoxic chemotherapy. However, these drugs exhibit extensive pharmacokinetic variability among patients. Therapeutic drug monitoring (TDM) is useful in determining venetoclax dosage regimens. This study aimed to develop a high-throughput method for the quantification of plasma venetoclax concentration using ultrahigh-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) and to apply this method to TDM. METHODS:After a simple solid-phase extraction step using a 96-well µElution plate, venetoclax was analyzed using UHPLC-MS/MS in positive electrospray ionization mode. This novel method fulfilled the requirements of the US Food and Drug Administration guidelines for bioanalytical assay validation, with a 4 ng/mL lower limit of quantification. The calibration curves were linear over a concentration range of 4-50,000 ng/mL. RESULTS:The recovery rate was 96.2 ± 18.0% (mean ± SD). The imprecision was below 7.2% coefficient of variation, and the accuracy was within 10.4% for all quality control levels. The matrix effect varied from 80.6% to 97.9%. This assay was successfully applied to the TDM of trough concentrations in 3 patients treated with venetoclax for acute myeloid leukemia. CONCLUSIONS:We successfully developed a novel high-throughput UHPLC-MS/MS method for quantifying venetoclax in human plasma. This method can be applied for TDM in patients receiving venetoclax in clinical settings.
BACKGROUND:Cefepime is widely used in intensive care units for the treatment of complicated gram-negative infections. Owing to its concentration-dependent neurological toxicity, cefepime is a candidate for therapeutic drug monitoring and model-informed precision dosing (MIPD), especially in critically ill patients. METHODS:A monocentric, retrospective, before‒after study was performed, including all adult patients hospitalized in the intensive care unit who were administered cefepime (prolonged infusion) and for whom at least 2 cefepime plasma trough concentrations (Cmin) were measured on separate days. Empirical therapeutic drug monitoring-based dosing optimization was compared with MIPD. The main end point was a Cmin between 4 × MIC (or 10 mg/L if the MIC was unavailable) and 20 mg/L. The odds of target attainment were modeled via a mixed-effect logistic model and the rate of target attainment via a spline model. RESULTS:A total of 254 patients were studied, of whom 113 were in the MIPD group and 141 in the control group. The proportion of compliance to dosage recommendation in the MIPD group was 70%. A total of 701 cefepime trough concentrations were analyzed. MIPD was nonsignificantly associated with higher odds of achieving concentrations within the therapeutic range (aOR 1.38 [0.87-2.19]) and significantly associated with lower odds of overexposure (aOR 0.59 [0.36-0.98]). The hazard ratio of target attainment was 1.1 [0.7-1.9] on day 1 and 1.6 [0.9-2.9] on day 7. CONCLUSIONS:Cefepime MIPD in the intensive care unit reduces the odds of overexposure when compared with empirical dosing. It may also improve the odds and rate of pharmacokinetic/pharmacodynamic target attainment. Further research on clinical safety end points is needed to consolidate the present findings on the added value of MIPD.
BACKGROUND:Tacrolimus is a first-line immunosuppressant used in kidney transplantation. However, its narrow therapeutic window and variable exposure present challenges. Extended- and prolonged-release formulations, ER-tacrolimus (Advagraf) and LCP-tacrolimus (Envarsus), respectively, improve adherence, and LCP-tacrolimus provides higher bioavailability. The safety and benefits of switching from ER-tacrolimus to LCP-tacrolimus were evaluated. METHODS:In this open-label switch study, first-time, adult, kidney transplant recipients with stable graft function, therapeutic ER-tacrolimus levels, and rapid metabolizer status (C/D ratio <1.05) were switched to LCP-tacrolimus, followed by reconversion to ER-tacrolimus after 3 weeks. The primary outcome was the dose required to maintain therapeutic trough levels; secondary outcomes included differences in adverse effects, pill burden, patient preference, and pharmacokinetics (tacrolimus trough concentration, C0; peak blood levels, Cmax; area under the concentration-time curve, AUC). RESULTS:Twelve patients were included. The dose to achieve therapeutic C0 (5-8 µg/L) was 30% lower with LCP-tacrolimus than with ER-tacrolimus (6.0 mg versus 8.5 mg, P = 0.004), with similar pill burden and adverse effects. AUC or Cmax were similar [LCP-tac: 14.75 (11.68-16.27); ER-tac: 17.15 (13.53-20.93)]. For CYP3A5 expressors (n = 7; 58%), there was a stronger correlation between C0 and AUC for LCP-tacrolimus (0.85, P = 0.016; 0.43, P = 0.34, respectively). No serious adverse events were observed. CONCLUSIONS:Conversion to LCP-tacrolimus with a 30% dose reduction achieved therapeutic C0 without differences in pill burden or overall exposure. In the CYP3A5 expressors, C0 was more strongly correlated with exposure to LCP-tacrolimus, suggesting more reliable trough monitoring. The small sample size, short follow-up period, and selection bias limit interpretation.
BACKGROUND:Therapeutic drug monitoring (TDM) helps optimize pharmacotherapy, but research activity in low- and middle-income countries (LMICs) is poorly characterized. This scoping review systematically maps the volume, characteristics, and geographic distribution of TDM research in LMICs from 2015 to 2024. METHODS:PubMed, Embase, and Web of Science were searched to identify peer-reviewed TDM studies conducted in World Bank-defined LMICs. Extracted data covered publication characteristics, study design, geographic distribution, drug classes, analytical methods, and descriptive quality indicators. RESULTS:A total of 385 publications from 38 LMICs were included; annual output steadily increased from 29 publications in 2015 to 53 in 2024. The Western Pacific Region accounted for 54.5% of all publications, with China contributing 197 (51.2%). Antimicrobials, immunosuppressants, antiseizure drugs, and antipsychotics accounted for 95.5% of drug-class attributions. Vancomycin (n = 84) and tacrolimus (n = 81) were the most studied drugs. Immunoassay was the predominant analytical method (35.1%), followed by liquid chromatography-mass spectrometry (18.7%). Pharmacy and clinical pharmacology departments were involved in 47.0% of studies. Only 6 low-income countries contributed publications (n = 7) through collaborations with high-income countries (10.4%). The analytical method did not align with the country's income. Pharmacogenetic studies (n = 45), guidelines (n = 2), and external quality assessment studies (n = 2) were rare. CONCLUSIONS:TDM research in LMICs has expanded substantially but remains geographically concentrated and focused on drugs with commercial assays. Key gaps remain in pharmacogenetics, implementation science, and guideline development. Strengthening laboratory infrastructure, equitable international collaboration, and locally adapted TDM strategies may help advance precision medicine in resource-limited settings.
BACKGROUND:The aim of this study was to (1) determine the incidence of acute kidney injury (AKI) in patients with oncological diseases and identify its probable causes, (2) estimate the pharmacokinetic (PK) parameters in this population, and (3) propose vancomycin dosage regimens to support safer dosing in nontherapeutic drug monitoring settings. METHODS:The authors conducted a prospective observational cohort study involving 334 adult patients with oncological diseases (406 vancomycin treatment courses) at a tertiary cancer center in Brazil. Vancomycin was administered using a Bayesian AUC-guided protocol. AKI was defined using the RIFLE criteria and adjudicated by a multidisciplinary team to determine probable vancomycin attribution. Linear regression was used to estimate the PK parameters and propose a practical dosing regimen for resource-limited settings without therapeutic drug monitoring. RESULTS:AKI occurred in 15% (62/406) of vancomycin courses, with 34% of the events occurring within the first 24 hours of therapy initiation. After excluding early-onset and non-drug-related AKI, the incidence of vancomycin-induced acute kidney injury was 4.9%. A wide trough variability (5.35-20.11 mg/L) was observed for AUC values of 400-500 mg*h/L. Multiple linear regression showed that CrCl, intensive care unit admission, and oncohematological status independently influenced PK parameters. Stratified maintenance dose ranges were described, offering potential guidance for empirical dosing in therapeutic drug monitoring-limited settings. CONCLUSIONS:The incidence of vancomycin-induced acute kidney injury in this oncological cohort was 4.9%. CrCl, intensive care unit admission, and type of oncological disease significantly influenced the PK parameters. These findings support the development of context-specific dosing guidance but require external validation before implementation in clinical practice.
BACKGROUND:Tacrolimus is a critical immunosuppressant that requires therapeutic drug monitoring because of its narrow therapeutic index and erratic pharmacokinetics. Although liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been deemed the "gold standard" for tacrolimus quantitation, immunoassays are widely used in practice because of their suitability for automation and their accessibility. However, interassay variability poses risks of dosing errors and therapeutic mismanagement. METHODS:The authors compared Siemens Atellica CH and Abbott ARCHITECT i2000 tacrolimus immunoassays with LC-MS/MS as the reference method. Forty whole blood samples from transplant recipients were used. The Clinical and Laboratory Standards Institute guidelines were followed for the method comparison, linearity, and precision studies. Deming regression and Bland-Altman plots were used to evaluate agreement and bias. RESULTS:Both immunoassays exhibited good precision (coefficient of variation <15%) and linearity over the range established by the manufacturer. The results for both platforms correlated well with the LC-MS/MS results ( r > 0.97). The Siemens Atellica and Abbott ARCHITECT methods showed negative biases of -13.8% and -6.6%, respectively, compared with LC-MS/MS. A mean difference of -14.3% was observed between the 2 immunoassays. CONCLUSIONS:Although both immunoassays met the performance standards, systematic negative bias, particularly with the Siemens Atellica method, may have affected tacrolimus dosing in the transplant recipients. Method-specific variability underscores the need for a stable monitoring platform or to re-establish the baseline during a method changeover. LC-MS/MS remains an essential method for verifying patient safety.
BACKGROUND:Integrating machine learning (ML) with population pharmacokinetic (PPK) modeling may improve therapeutic drug monitoring predictions. METHODS:Tacrolimus trough concentrations from lung transplant patients were split into a training data set (1151 concentrations in 80 patients) and a testing data set (224 concentrations in 20 patients). A PPK model was developed using NONMEM, followed by the development of 10 ML models to fit individual pharmacokinetic parameters from the PPK model with Bayesian forecasting. The best performing ML model was selected as the final model. Both the final PPK and ML models were compared for prediction performance. A web-based dashboard was established with R-shiny to recommend dosing regimens based on patient data. RESULTS:In the PPK model, postoperative days, hematocrit, aspartate aminotransferase, tacrolimus daily dose, coadministered voriconazole or posaconazole, and the CYP3A5*3 genotype were identified significant covariates on the clearance. The Cubist ML model outperformed the PPK model, showing lower root mean squared error for tacrolimus concentrations in the testing data set. An online web-based precision dosing dashboard was created, accessible at (https://tac-dose-ml.shinyapps.io/shiny/). CONCLUSIONS:Integrating ML with PPK modeling could yield superior tacrolimus concentration predictions for lung transplant patients, offering an efficient alternative to Bayesian forecasting. The online dashboard provides rapid, individualized dosing recommendations.
BACKGROUND:Population pharmacokinetic (popPK) models are increasingly used to support model-informed precision dosing owing to their abilities to account for variability in drug exposure. However, there is no accepted/validated risk of bias (RoB) framework tailored to systematic reviews that externally evaluate popPK models. Therefore, this study was conducted to explore how existing systematic reviews on popPK assess model quality and bias and appraise RoB. METHODS:A systematic review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Systematic reviews that externally evaluated popPK models quantitatively and reported bias/accuracy metrics were searched for on Embase, MEDLINE, PubMed, Web of Science, Cochrane Library, Google Scholar, and CINAHL from inception to November 2025. Data on study selection, external datasets, and model evaluation metrics were extracted. RoB was assessed with RoB in systematic reviews (ROBIS). RESULTS:Twenty-two systematic reviews were included. Considerable variation existed in study selection approaches, external validation datasets, and bias assessment metrics. Prediction error-based metrics were most frequently reported ( n = 21), followed by Bayesian forecasting and simulation-based diagnostics (both n = 14). ROBIS assessment indicated recurrent concerns regarding the identification/selection of studies and collection/appraisal of data. Common sources of bias were limitations of external datasets ( n = 19; retrospective design, sparse sampling, small sample size), heterogeneity in bioanalytical methods ( n = 8), and unaccounted treatment-related factors such as concomitant medication ( n = 4). CONCLUSIONS:ROBIS is only partially applicable to systematic reviews on popPK and insufficiently captures external validation-specific issues. An "external validation" domain with signaling questions focused on dataset provenance, adequacy, and assay consistency is proposed for improving transparency, reproducibility, and comparability across future systematic reviews on popPK.
BACKGROUND:Belumosudil is an oral, selective inhibitor of Rho-associated coiled-coil-containing protein kinase 2, approved for the treatment of chronic graft-versus-host disease (cGVHD). Clinical trials have shown that higher doses of belumosudil are associated with a greater incidence of adverse events. The pharmacokinetics of belumosudil are markedly affected by food intake and concurrent medications, and substantial interindividual variability in plasma concentrations has also been reported. Therefore, monitoring belumosudil blood concentrations may facilitate more effective drug therapy. METHODS:Belumosudil and luliconazole (internal standard, IS) were extracted from 50 µL of human plasma using methanol. Chromatographic analysis was performed on a reversed-phase column (250 mm × 4.6 mm, 5 µm) under isocratic conditions, with a mobile phase of 0.5% KH2PO4 (pH 4.5) and acetonitrile (40:60, v/v) at a flow rate of 1 mL/min. Ultraviolet detection was performed at 337 nm. The calibration curve for belumosudil demonstrated linearity over the concentration range of 250 to 15,000 ng/mL, with a correlation coefficient (r2) of 0.9998. The accuracy and precision of all validation experiments met the criteria established by the US Food and Drug Administration. RESULTS:In this study, 44 plasma samples from 5 patients with cGVHD were analyzed to determine belumosudil concentrations. The chromatographic peaks of both the IS and belumosudil showed no interference from the biological matrix or from concurrent medications and their metabolites, confirming adequate selectivity and specificity. CONCLUSIONS:This study presents the first development and validation of a simple, novel, and broad-range high-performance liquid chromatography-ultraviolet approach for quantifying plasma belumosudil, supporting its suitability for therapeutic drug monitoring in clinical practice.
BACKGROUND:Tacrolimus therapeutic drug monitoring after liver transplantation is characterized by significant interindividual variability and sparse, irregularly sampled concentration data, which limits the applicability of conventional pharmacokinetic and data-intensive modelling approaches. METHODS:We developed a hierarchical prediction framework integrating density-based spatial clustering of applications with noise-derived patient stratification with self-memory algorithm-based nonlinear grey Bernoulli model (SA-NGBM) using retrospective data from 129 liver transplant recipients. Patients were stratified into homogeneous subgroups using routinely available clinical indicators, and cluster-specific SA-NGBM models calibrated on representative patients were used to predict subsequent tacrolimus trough concentrations. Performance was further evaluated in an independent same-center validation cohort of 60 patients. RESULTS:In the development cohort, the overall mean absolute relative prediction error for the next tacrolimus concentration decreased from 41.4% with the nonclustered baseline to 21.2% with the clustered SA-NGBM framework. In the independent validation cohort, consistent performance gains were observed, with the mean absolute relative prediction error decreasing from 56.8% to 27.3% in the largest patient subgroup. Full longitudinal concentration profiles were required for only 4 representative patients. CONCLUSIONS:Overall, this clustered SA-NGBM framework reduces prediction error under sparse and irregular therapeutic drug monitoring conditions and provides a data-efficient stratified modelling strategy. However, further refinement and prospective validation are required before clinical implementation.
BACKGROUND:The aim of this study was to assess the presence of pyrethroid metabolites in the urine of mothers and their full-term newborns during the first days of life and to investigate the correlation between maternal and neonatal urinary concentrations. It also explores potential clinical and environmental factors influencing these levels. METHODS:Creatinine-adjusted urinary concentrations of 5 pyrethroid metabolites, 3-phenoxybenzoic acid, 4-fluoro-3-phenoxybenzyl acid (FPBA), cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclopropane-carboxylic acid, and cis and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid (Cis-dimethylcyclopropane-1-carboxylic acid), and (Trans-dimethylcyclopropane-1-carboxylic acid), were measured within the first 3 days postpartum in mothers and their children. RESULTS:The study included 88 mothers and 91 full-term newborns. At least 1 pyrethroid metabolite was detected in 99% of mothers and 77% of newborns. A significant (P < 0.05) correlation was observed between maternal and neonatal urinary concentrations of dimethylcyclopropane-carboxylic acid, a metabolite specific to deltamethrin, supporting the hypothesis of placental transfer. Newborns born during winter had considerably lower urinary metabolite concentrations compared with those born in other seasons. No significant correlations were found for the other metabolites, likely due to the diversity of their parent compounds. CONCLUSIONS:This study provides evidence of transplacental transfer of deltamethrin, while the lack of 3-phenoxybenzoic acid correlation suggests differential metabolic or exposure pathways for nonspecific metabolites. The seasonal variation in metabolite levels suggests environmental or behavioral factors influencing exposure. These findings emphasize the need to investigate dietary and domestic pesticide exposure as key contributors to perinatal pyrethroid exposure, as well as longitudinal studies to assess the evolution of metabolite concentrations and their potential health effects on mothers and infants.
BACKGROUND:The combined creatinine (SCr)- and cystatin C (CysC)-based estimated glomerular filtration rate (eGFR) provides a more accurate assessment of kidney function. Given that precise estimation of kidney function is important for optimal vancomycin dosing, few studies have evaluated whether a combined estimate better predicts vancomycin clearance and improves clinical outcomes. METHODS:In this retrospective study, 4 kidney function estimation methods, Cockcroft-Gault (CG), SCr-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), CysC-based CKD-EPI, and the combined SCr and CysC CKD-EPI equations, were assessed in 66 adult inpatients to determine which method most accurately predicts vancomycin clearance (CL vanco). RESULTS:Although the CG- and SCr-based CKD-EPI estimates were comparable, substantial differences were observed among the methods. The combined SCr and CysC estimate showed the highest correlation [ρ = 0.854; 95% confidence interval (CI): 0.76-0.91], the most linear relationship (R2 = 0.75), the best precision (root mean square error = 0.92 L/h; 95% CI: 0.76-1.05), and the least bias (mean predicted error = 0.16 L/h; 95% CI: -0.07 to 0.38), indicating the best overall performance among the 4 methods. CONCLUSIONS:These results suggest that combined SCr and CysC estimates may be associated with more accurate and precise vancomycin dosing than the other methods. This finding implies that other medications cleared by the kidneys, especially those with narrow therapeutic windows, may be appropriately administered using the combined estimate. Further studies are required to determine whether these findings lead to improved clinical outcomes.
BACKGROUND:Comparing drug concentrations in blood, saliva, and hair may provide important insights into pharmacokinetics. Salivary drug levels may serve as a substitute for blood concentrations, while drug content in hair can reflect long-term exposure and allow reconstruction of treatment history. The study investigated the relationship between lacosamide concentrations in plasma, saliva, and hair, the number of seizures during the previous 3 months, and treatment adherence. METHODS:An observational, retrospective study was conducted between 2020 and 2022 to evaluate the correlation between plasma, saliva, and hair lacosamide concentrations, seizure frequency in the preceding 3 months, and patient adherence. Lacosamide concentrations in plasma, saliva, and hair were determined using liquid chromatography-tandem mass spectrometry in the positive ionization mode. The study group consisted of patients with epilepsy treated at the Institute of Psychiatry and Neurology in Warsaw, Poland. RESULTS:A total of 39 patients with epilepsy were included. Neither hair nor plasma lacosamide concentrations correlated with the number of seizures in the previous 3 months. Unexpectedly, patients who reported regular medication intake had significantly lower lacosamide concentrations in hair (8.1 ± 2.90 ng/mg) compared with those who did not adhere to fixed dosing times (43.00 ± 14.76 ng/mg). A strong correlation was observed between plasma and saliva lacosamide concentrations (r = 0.85, P < 0.05), indicating that saliva may serve as an effective alternative matrix for monitoring lacosamide levels. CONCLUSIONS:Lacosamide concentrations in blood, saliva, or hair do not correlate with seizure frequency and are therefore not reliable markers of adherence. However, salivary lacosamide measurement may offer a practical alternative to plasma monitoring.
