External Validation of a Limited Sampling Strategy for the Estimation of Mycophenolic Acid Exposure Between Different Assay Methods: PETINIA and HPLC Methods.

INTRODUCTION: A limited sampling strategy (LSS) for estimating the area under the plasma concentration-time curve (AUC0-12) of the immunosuppressant mycophenolic acid (MPA) is used for therapeutic drug monitoring (TDM) in clinical practice. Our study delves into the applicability of the MPA AUC0-12 LSS, originally developed using particle-enhanced turbidimetric inhibition immunoassay (PETINIA) measurements, to those obtained via high-performance liquid chromatography with ultraviolet detection (HPLC-UV). METHODS: We developed an LSS for estimating MPA AUC0-12 based on PETINIA measurements in 32 adult kidney transplant patients who were receiving mycophenolate mofetil. Validation of this strategy was conducted in an additional 14 adult kidney transplant patients (validation sets) through measurements obtained by both PETINIA and HPLC-UV. Predictive performance was assessed using mean absolute error (MAE), root mean squared error (RMSE), and "good guess" defined as predicted AUC within observed AUC ± 15%. RESULTS: The three time point equation (0, 2, and 6 h) emerged as optimal for estimating MPA AUC0-12, balancing predictive performance and usefulness in clinical settings. In validation sets, the coefficient of determination for observed versus predicted AUC0-12 was consistent between PETINIA (0.978) and HPLC-UV (0.958) measurements. Comparable MAE, RMSE, and "good guess" outcomes were observed for PETINIA (6.4%, 8.1%, and 85.7%, respectively) and HPLC-UV (7.6%, 9.4%, and 85.7%, respectively) measurements. CONCLUSION: Our findings support the application of the MPA AUC0-12 LSS, originally developed using PETINIA measurements, to those obtained via HPLC-UV.

Reducing clustering of readouts in non-Cartesian cine magnetic resonance imaging.

BACKGROUND: Non-Cartesian magnetic resonance imaging trajectories at golden angle increments have the advantage of allowing motion correction and gating using intermediate real-time reconstructions. However, when the acquired data are cardiac binned for cine imaging, trajectories can cluster together at certain heart rates (HR) causing image artifacts. Here, we demonstrate an approach to reduce clustering by inserting additional angular increments within the trajectory, and optimizing them while still allowing for intermediate reconstructions. METHODS: Three acquisition models were simulated under constant and variable HR: golden angle (Mtrd), random additional angles (Mrnd), and optimized additional angles (Mopt). The standard deviations of trajectory angular differences (STAD) were compared through their interquartile ranges (IQR) and the Kolmogorov-Smirnov test (significance level: p = 0.05). Agreement between an image reconstructed with uniform sampling and images from Mtrd, Mrnd, and Mopt was analyzed using the structural similarity index measure (SSIM). Mtrd and Mopt were compared in three adults at high, low, and no HR variability. RESULTS: STADs from Mtrd were significantly different (p < 0.05) from Mopt and Mrnd. STAD (IQR × 10-2 rad) showed that Mopt (0.5) and Mrnd (0.5) reduced clustering relative to Mtrd (1.9) at constant HR. For variable HR, Mopt (0.5) and Mrnd (0.5) outperformed Mtrd (0.9). The SSIM (IQR) showed that Mopt (0.011) produced the best image quality, followed by Mrnd (0.014), and Mtrd (0.030). Mopt outperformed Mtrd at reduced HR variability in in-vivo studies. At high HR variability, both models performed well. CONCLUSION: This approach reduces clustering in k-space and improves image quality.

Effect of Sampling Time, Quantification Method, and Tree Sample Pooling on Phytophthora cinnamomi Root Quantities in South African Avocado Orchards.

Phytophthora cinnamomi is a destructive soilborne pathogen causing Phytophthora root rot on avocados worldwide. Little is known about the effect of root sampling time, root quantification method (quantitative real-time PCR [qPCR] versus baiting), and tree sample pooling strategies on the quantification of the pathogen in roots in avocado orchard trees. This was investigated in six avocado orchards in two climatically different production regions (Mooketsi and Letaba) in the Limpopo Province, South Africa, over a 2-year period. Two different tree sample pooling strategies, consisting of either a four-pooled group (four groups each containing five pooled trees) or a single-pooled group (20 trees pooled) per 1 ha, were both shown to be suitable for quantifying P. cinnamomi in tree roots using qPCR or root baiting. P. cinnamomi root quantities from the two tree sample pooling strategies were significantly correlated for both quantification methods. Both quantification methods were suitable for quantifying the pathogen in roots, although qPCR was superior to root baiting at identifying significant differences in P. cinnamomi quantities among root sampling time points. The effect of sampling time was dependent on the investigated year. In 2017, root quantities, which were only evaluated using qPCR, did not reveal a consistent trend of a specific sampling time yielding the highest root quantities for most of the orchards. However, five of the orchards in 2018, based on the qPCR analyses, contained significantly higher P. cinnamomi root quantities in May (late autumn) than in March (early autumn), August (late winter), and October/November (late spring). In 2018, P. cinnamomi root DNA quantities were significantly positively correlated with the number of soil temperature hours at 20 to 24 and 20 to 29°C 2 months preceding the root sampling dates and negatively correlated with the number of hours at 15 to 19°C 2 months preceding root sampling. Our study has identified P. cinnamomi root quantification methods and tree sample pooling strategies, which will be useful for understanding the biology of the pathogen and when disease management strategies should be in place.

Research on enhancing the efficiency of food safety sampling inspections in China based on Pareto's law.

BACKGROUND: Food safety is pivotal for public welfare and directly impacts consumer health. Food safety sampling inspections (FSSIs) are essential in detecting unqualified food products and non-compliant manufacturers, which form an integral part of government regulatory frameworks. However, given the constraints on budgetary resources, improving the efficiency of food safety sampling inspections (EFSSIs) remains a considerable challenge in China's food quality and safety supervision. This study aims to apply Pareto's law, starting from the examination of food sample testing items and major hazard types, to theoretically analyze methods for improving the EFSSIs. Following the theoretical analysis, the research employs provincial food sampling data from China in 2022 to empirically validate the proposed improvement strategies. RESULTS: The research findings indicate that applying Pareto's law significantly reduces the number of items that should be tested for each food subcategory, effectively lowering testing costs for each batch of food samples. Theoretically, employing Pareto's law in sampling inspections can increase the EFSSIs to 2.78 times the current observed level. Furthermore, empirical validation using food sampling data confirms that EFSSIs can be improved to 2.12 times the existing level, consistent with theoretical predictions. CONCLUSION: Implementing Pareto's law in FSSIs facilitates the detection of more unqualified food products and non-compliant manufacturers without additional financial burden, significantly enhancing the EFSSIs. This approach provides an innovative strategy for government to bolster their food safety management efforts. © 2024 Society of Chemical Industry.

Limited Sampling Strategies to Predict Ganciclovir Exposure after Valganciclovir Administration and to Reduce Monitoring Constraints in Renal Transplant Children.

Valganciclovir, the ganciclovir prodrug, is an antiviral agent used to prevent cytomegalovirus infection in renal transplant children. Therapeutic drug monitoring is still necessary to ensure optimal therapeutic area under the concentration-time curve from 0 to 24 h (AUC0-24) of 40 to 60 µg·h/mL since valganciclovir presents a high pharmacokinetic variability. To calculate ganciclovir AUC0-24 with the trapezoidal method, 7 samples are needed. The objective of this study was to develop and validate a reliable and clinically applicable limited sampling strategy (LSS) for individualizing valganciclovir dose in renal transplant children. Rich pharmacokinetic data from ganciclovir plasmatic dosages measured in renal transplant children who received valganciclovir to prevent cytomegalovirus infection at Robert Debré University Hospital were collected retrospectively. Ganciclovir AUC0-24s were calculated using the trapezoidal method. The LSS was developed using a multilinear regression approach to predict AUC0-24. The patients included were divided into two groups for model development (50 patients) and validation (30 patients). A total of 80 patients were included between February 2005 and November 2018. Multilinear regression models were developed on 50 pharmacokinetic profiles (50 patients) and validated with an independent group of 43 pharmacokinetic profiles (30 patients). Regressions based on samples collected at T1h-T4h-T8h, T2h-T4h-T8h, or T1h-T2h-T8h presented the best AUC0-24 predictive performances with an average difference between reference and predicted AUC0-24 of -0.27, 0.34, and -0.40 µg·h/mL, respectively. In conclusion, valganciclovir dosage adaptation was required in children to achieve the target AUC0-24. Three LSS models using three pharmacokinetic blood samples instead of seven will be useful for individualizing valganciclovir prophylaxis in renal transplant children.

Mycophenolic acid therapeutic drug monitoring using area under the curve in pediatric heart transplant recipients.

INTRODUCTION: Pharmacokinetics of mycophenolic acid (MPA) display substantial interpatient variability, with up to 10-fold difference of exposure in individual patients under a fixed-dose regimen. MPA trough level (C0) monitoring is common in clinical practice but has not proven sufficiently informative in predicting MPA exposure or patient outcomes, especially in children. No limited sampling strategies (LSSs) have been generated from pediatric heart transplant (HTx) recipients to estimate MPA AUC. METHODS: Single-center, observational analysis of 135 de novo pediatric HTx recipients ≤21 years old who underwent MPA AUC between 2011 and 2021. RESULTS: Median age was 4 years (IQR .6-12.1). Median time from transplant to MPA AUC sampling was 15 days (IQR 11-19). MMF doses (mg or mg/day) had low, negative Pearson correlation coefficients (r) while doses adjusted for weight or body surface area had low correlation with Trapezoidal MPA AUC0-24 h (r = .3 and .383, respectively). MPA C0 had weak association (r = .451) with Trapezoidal MPA AUC0-24 h . LSS with two pharmacokinetic sampling time points at 90 (C3 ) and 360 (C5 ) min after MMF administration (estimated AUC0-24 h  = 32.82 + 4.12 × C3  + 11.53 × C5 ) showed strong correlation with Trapezoidal MPA AUC0-24 h (r = .87). CONCLUSION: MMF at fixed or weight-adjusted doses, as well as MPA trough levels, correlate poorly with MPA AUC0-24 h . We developed novel LSSs to estimate Trapezoidal MPA AUC from a large cohort of pediatric HTx recipients. Validation of our LSSs should be completed in a separate cohort of pediatric HTx recipients.

Soil Micro-eukaryotic Diversity Patterns Along Elevation Gradient Are Best Estimated by Increasing the Number of Elevation Steps Rather than Within Elevation Band Replication.

The development of high-throughput sequencing (HTS) of environmental DNA (eDNA) has stimulated the study of soil microbial diversity patterns and drivers at all scales. However, given the heterogeneity of soils, a challenge is to define effective and efficient sampling protocols that allow sound comparison with other records, especially vegetation. In studies of elevational diversity pattern, a trade-off is choosing between replication within elevation bands vs. sampling more elevation bands. We addressed this question for soil protists along an elevation gradient on Mt. Asahi, Hokkaido, Japan. We compared two sampling approaches: (1) the replicate strategy (five replicates at six elevational bands, total = 30) and (2) the transect strategy (one sample in each of 16 different elevational bands). Despite a nearly twofold lower sampling effort, the transect strategy yielded congruent results compared to the replicate strategy for the estimation of elevational alpha diversity pattern: the regression coefficients between diversity indices and elevation did not differ between the two options. Furthermore, for a given total number of samples, gamma diversity estimated across the entire transect was higher when sampling more elevational bands as compared to replication from fewer elevational bands. Beta diversity (community composition turnover) was lower within a given elevational band than between adjacent bands and increased with elevation distance. In redundancy analyses, soil organic matter-related variable (the first principal component of soil organic matter, water content, total organic carbon, and nitrogen by whom were highly correlated) and elevation best explained elevational beta diversity pattern for both sampling approaches. Taken together, our results suggest that sampling a single plot per elevation band will be sufficient to obtain a good estimate of soil micro-eukaryotic diversity patterns along elevation gradients. This study demonstrated the effectiveness of the transect strategy in estimating diversity patterns along elevation gradients which is instructive for future environmental or even experimental studies. While not advocating for completely replacing replication-based sampling practices, it is important to note that both replicate and transect strategies have their merits and can be employed based on specific research goals and resource limitations.

Synthetic Antioxidants as Contaminants of Emerging Concern in Indoor Environments: Knowns and Unknowns.

Synthetic antioxidants, including synthetic phenolic antioxidants (SPAs), amine antioxidants (AAs), and organophosphite antioxidants (OPAs), are essential additives for preventing oxidative aging in various industrial and consumer products. Increasing attention has been paid to the environmental contamination caused by these chemicals, but our understanding of synthetic antioxidants is generally limited compared to other emerging contaminants such as plasticizers and flame retardants. Many people spend a significant portion (normally greater than 80%) of their time indoors, meaning that they experience widespread and persistent exposure to indoor contaminants. Thus, this Perspective focuses on the problem of synthetic antioxidants as indoor environmental contaminants. The wide application of antioxidants in commercial products and their demonstrated toxicity make them an important family of indoor contaminants of emerging concern. However, significant knowledge gaps still need to be bridged: novel synthetic antioxidants and their related transformation products need to be identified in indoor environments, different dust sampling strategies should be employed to evaluate human exposure to these contaminants, geographic scope and sampling scope of research on indoor contamination should be broadened, and the partition coefficients of synthetic antioxidants among different media need to be investigated.

Fishing eDNA in One of the World's Largest Rivers: A Case Study of Cross-Sectional and Depth Profile Sampling in the Yangtze.

The world's largest rivers are home to diverse, endemic, and threatened fish species. However, their sheer sizes make large-scale biomonitoring challenging. While environmental DNA (eDNA) metabarcoding has become an established monitoring approach in smaller freshwater ecosystems, its suitability for large rivers may be challenged by the sheer extent of their cross sections (>1 km wide and tens of meters deep). Here, we sampled fish eDNA from multiple vertical layers and horizontal locations from two cross sections of the lower reach of the Yangtze River in China. Over half of the ASVs (amplicon sequence variants) were detected in only a single combination of the vertical layers and horizontal locations, with ∼7% across all combinations. We estimated the need to sample >100 L of water across the cross-sectional profiles to achieve ASV richness saturation, which translates to ∼60 L of water at the species level. No consistent pattern emerged for prioritizing certain depth and horizontal samples, yet we underline the importance of sampling and integrating different layers and locations simultaneously. Our study highlights the significance of spatially stratified sampling and sampling volumes when using eDNA approaches. Specifically, we developed and tested a scalable and broadly applicable strategy that advances the monitoring and conservation of large rivers.

Limited sampling strategy to predict free mycophenolic acid area under the concentration-time curve in paediatric patients with nephrotic syndrome.

In paediatric patients, there is no data on the recommended area under the concentration-time curve from 0 to 12 h (AUC0-12 ) for free mycophenolic acid (fMPA), which is the active form of the drug, responsible for the pharmacological effect. We decided to establish the limited sampling strategy (LSS) for fMPA for its use in MPA therapeutic monitoring in children with nephrotic syndrome treated with mycophenolate mofetil (MMF). This study included 23 children (aged 11 ± 4 years) from whom eight blood samples were collected within 12 h after MMF administration. The fMPA was determined using the high-performance liquid chromatography with fluorescence detection method. LSSs were estimated with the use of R software and bootstrap procedure. The best model was chosen based on a number of profiles with AUC predicted within ± 20% of AUC0-12 (good guess), r2 , mean prediction error (%MPE) of ±10% and mean absolute error (%MAE) of less than 25%. The fMPA AUC0-12 was 0.1669 ± 0.0697 µg h/mL and the free fraction was within 0.16%-0.81%. In total, there were 92 equations developed of which five fulfilled the acceptance criteria for %MPE, %MAE, good guess >80% and r2 > 0.900. These equations consisted of three time points: model 1 (C1 , C2 , C6 ), model 2 (C1 , C3 , C6 ), model 3 (C1 , C4 , C6 ), model 5 (C0 , C1 , C2 ), and model 6 (C1 , C2 , C9 ). Although blood sampling up to 9 h after MMF dosing is impractical, it is crucial to include C6 or C9 in LSS to assess fMPA AUCpred correctly. The most practical fMPA LSS, which fulfilled the acceptance criteria in the estimation group, was fMPA AUCpred  = 0.040 + 2.220 × C0 + 1.130 × C1 + 1.742 × C2 . Further studies should define the recommended fMPA AUC0-12 value in children with nephrotic syndrome.

Real-Time Segmentation of Unstructured Environments by Combining Domain Generalization and Attention Mechanisms.

This paper presents a focused investigation into real-time segmentation in unstructured environments, a crucial aspect for enabling autonomous navigation in off-road robots. To address this challenge, an improved variant of the DDRNet23-slim model is proposed, which includes a lightweight network architecture and reclassifies ten different categories, including drivable roads, trees, high vegetation, obstacles, and buildings, based on the RUGD dataset. The model's design includes the integration of the semantic-aware normalization and semantic-aware whitening (SAN-SAW) module into the main network to improve generalization ability beyond the visible domain. The model's segmentation accuracy is improved through the fusion of channel attention and spatial attention mechanisms in the low-resolution branch to enhance its ability to capture fine details in complex scenes. Additionally, to tackle the issue of category imbalance in unstructured scene datasets, a rare class sampling strategy (RCS) is employed to mitigate the negative impact of low segmentation accuracy for rare classes on the overall performance of the model. Experimental results demonstrate that the improved model achieves a significant 14% increase mIoU in the invisible domain, indicating its strong generalization ability. With a parameter count of only 5.79M, the model achieves mAcc of 85.21% and mIoU of 77.75%. The model has been successfully deployed on a a Jetson Xavier NX ROS robot and tested in both real and simulated orchard environments. Speed optimization using TensorRT increased the segmentation speed to 30.17 FPS. The proposed model strikes a desirable balance between inference speed and accuracy and has good domain migration ability, making it applicable in various domains such as forestry rescue and intelligent agricultural orchard harvesting.

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring.

Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In this paper, an adaptive sampling framework of segment intervals is proposed, based on the summary and improvement of existing problems. The framework is implemented to monitor mechanical degradation, and experiments are implemented on simulation data and real datasets. Subsequently, the distributions of the samples collected by different sampling strategies are visually presented through a color map, and five metrics are designed to assess the sampling results. The intuitive and numerical results show the superiority of the proposed method in comparison to existing methods, and the results are closely related to data status and degradation indicators. The smaller the data fluctuation and the more stable the degradation trend, the better the result. Furthermore, the results of the objective physical indicators are obviously better than those of the feature indicators. By addressing existing problems, the proposed framework opens up a new idea of predictive sampling, which significantly improves the degradation monitoring.

Population Pharmacokinetic Modelling and Limited Sampling Strategies for Therapeutic Drug Monitoring of Pyrazinamide in Patients with Tuberculosis.

Pyrazinamide is one of the first-line antituberculosis drugs. The efficacy of pyrazinamide is associated with the ratio of 24-h area under the concentration-time curve (AUC24) to MIC. The objective of this study was to develop and validate a limited sampling strategy (LSS) based on a population pharmacokinetic (popPK) model to predict AUC24. A popPK model was developed using an iterative two-stage Bayesian procedure and was externally validated. Using data from 20 treatment-naive adult tuberculosis (TB) patients, a one compartment model with transit absorption and first-order elimination best described pyrazinamide pharmacokinetics and fed state was the only significant covariate for absorption rate constant (ka). External validation, using data from 26 TB patients, showed that the popPK model predicted AUC24 with a slight underestimation of 2.1%. LSS were calculated using Monte Carlo simulation (n = 10,000). External validation showed LSS with time points 0 h, 2 h, and 6 h performed best with RMSE of 9.90% and bias of 0.06%. Food slowed absorption of pyrazinamide, but did not affect bioavailability, which may be advantageous in case of nausea or vomiting in which food can be used to diminish these effects. In this study, we successfully developed and validated a popPK model and LSS, using 0 h, 2 h, and 6 h postdose samples, that could be used to perform therapeutic drug monitoring (TDM) of pyrazinamide in TB patients.

The pharmacokinetics of tacrolimus in peripheral blood mononuclear cells and limited sampling strategy for estimation of exposure in renal transplant recipients.

PURPOSE: Intracellular exposure of tacrolimus (TAC) may be a better marker of therapeutic effect than whole blood exposure. We aimed to evaluate the influence of genetic polymorphism on the pharmacokinetics of TAC in peripheral blood mononuclear cells (PBMCs) and develop limited sampling strategy (LSS) models to estimate the area under the curve (AUC0-12h) in the PBMC of Chinese renal transplant patients. METHODS: Ten blood samples of each of the 23 renal transplant patients were collected 0-12h after 14 (10-18) days of TAC administration. PBMCs were separated and quantified. The TAC level in PBMCs was determined, and pharmacokinetic parameters were estimated by noncompartmental study. The AUC0-12h of TAC in whole blood was estimated by Bayesian approach based on a population pharmacokinetic model established in 65 renal transplant patients. The influence of CYP3A5 and ABCB1 genotypes on exposure was estimated. By applying multiple stepwise linear regression analysis, LSS equations for TAC AUC0-12h in the PMBC of renal transplant patients were established, and the bias and precision of various equations were identified and compared. RESULTS: We found a modest correlation between TAC exposure in whole blood and PBMC (r2 = 0.5260). Patients with the CYP3A5 6986GG genotype had a higher AUC0-12h in PBMCs than those with the 6986 AA or GA genotype (P = 0.026). Conversely, patients with the ABCB1 3435TT genotype had a higher AUC0-12h in PBMC than those with the 3435 CC and CT genotypes (P = 0.046). LSS models with 1-4 blood time points were established (r2 = 0.570-0.989). The best model for predicting TAC AUC0-12h was C2-C4-C6-C10 (r2 = 0.989). The model with C0.5-C6 (r2 = 0.849) can be used for outpatients who need monitoring to be performed in a short period. CONCLUSIONS: The CYP3A5 and ABCB1 genotypes impact TAC exposure in PBMCs, which may further alter the effects of TAC. The LSS model consisting of 2-4 time points is an effective approach for estimating full TAC AUC0-12h in Chinese renal transplant patients. This approach may provide convenience and the possibility for clinical monitoring of TAC intracellular exposure.

Using a limited sampling strategy to investigate the interindividual pharmacokinetic variability in metformin: A large prospective trial.

AIMS: Recently a limited sampling strategy (LSS) for determination of metformin' pharmacokinetics was developed. The LSS utilizes the plasma concentration of metformin 3 and 10 hours after oral intake of a single dose to estimate the area under the concentration-time curve up to 24 hours (AUC0-24h ). The main purpose of this study was to support the feasibility of this strategy in a large prospective trial. METHODS: Volunteers orally ingested two 500-mg tablets of metformin hydrochloride. A blood sample was drawn three and ten hours after the ingestion. Urine was collected for 0-10 and 10-24 hours and urine volumes recorded. The AUC0-24h was calculated using the equation AUC0-24h = 4.779 * C3 + 13.174 * C10 . Additionally, all participants were genotyped for the single-nucleotide polymorphism A270S in OCT2, g.-66 T > C in MATE1, R61C, G465R, G401S and the deletion M420del in OCT1. RESULTS: In total, 212 healthy volunteers participated. The median (25th - 75th interquartile range) AUC0 - 24h , CLrenal , C3 and C10 , were 10 600 (8470-12 500) ng* hr* mL-1 , 29 (24-34) L* hour-1 , 1460 (1180-1770) and 260 (200-330) ng* mL-1 , respectively, which is in agreement with our previous results. GFRi was correlated with metformin AUC and CLrenal (P < .001). As expected, we found a great pharmacokinetic interindividual variability among the volunteers and no effect of the OCT1 genotype on the AUC0 - 24h . We were unable to reproduce our previous finding of a gene-gene interaction (OCT2 and MATE1) effect on CLrenal in this cohort. CONCLUSION: This study further supports the use of the 2-point LSS algorithm in large pharmacokinetic trials.

Systemic exposure to 5-fluorouracil and its metabolite, 5,6-dihydrofluorouracil, and development of a limited sampling strategy for therapeutic drug management of 5-fluorouracil in patients with gastrointestinal malignancy.

AIMS: 5-Fluorouracil (5-FU) is widely used in combination chemotherapy, and literature suggests pharmacokinetic-guided dosing to improve clinical efficacy and reduce toxicity. This study aimed to determine the pharmacokinetic exposure of both 5-FU and its metabolite, 5,6-dihydrofluorouracil (DHFU), in patients with gastrointestinal malignancy and to establish a simplified strategy to assist in therapeutic drug management for dose optimization. METHODS: This was a prospective, observational study, performed in 27 patients diagnosed with gastrointestinal malignancy who were prescribed 5-FU. Multiple samples were collected per patient over the slow bolus (15-20 min) and continuous infusion period (over 44 h) in doses 1 and 3, and the concentrations of 5-FU and DHFU were measured. RESULTS: A higher proportion of patients had exposures within the therapeutic range in dose 3 (50%) as compared to dose 1 (37.5%) with 5-FU. There was an association between delayed time to maximum concentration of DHFU and a high maximum concentration of 5-FU. A limited sampling strategy was developed with 4 samples, 2 during the bolus period and 2 during the continuous period (at 18 h and the end of infusion), which accurately predicted the total area under the curve of 5-FU. CONCLUSION: Using body surface area-based dosing with 5-FU, 50-60% of patients were outside of the therapeutic range. In the absence of genotype testing, measurement of the metabolite DHFU could be a phenotypical measure of dihydropyrimidine dehydrogenase enzyme activity. A limited sampling strategy was developed in patients who were prescribed a combination regimen of slow bolus, followed by a 44-hour continuous infusion of 5-FU to assist in the therapeutic drug management of patients.

Two-point blood sampling is sufficient and necessary to estimate the area under the concentration-time curve for intravenous busulfan in infants and young children.

BACKGROUND: Therapeutic drug monitoring for busulfan is important to prevent adverse events and improve outcomes in stem cell transplantation. We investigated intravenous busulfan pharmacokinetics and evaluated the utility of limited sampling strategy (LSS) as a simple method to estimate the area under the concentration-time curve (AUC). PROCEDURE: The study comprised 87 busulfan measurements in 54 children who received intravenous busulfan between August 2015 and May 2020. AUCs were calculated from three to five blood sampling points in each patient, and the correlation between AUC and plasma concentrations (ng/mL) at 1, 2, 3, 4, and 6 h after initiating busulfan infusion (C1 , C2 , C3 , C4 , and C6 , respectively). RESULTS: By one-point sampling strategy, the most relevant predicted AUC was based on C6 (r2  = 0.789; precision, 11.0%) in all patients. The predicted AUC based on C6 was acceptable (r2  = 0.937; precision, 5.9%) for adolescent patients weighing >23 kg, but the correlation was poor in infants and young children weighing ≤ 23 kg (r2  = 0.782; precision, 11.4%). By two-point sampling strategy, the predicted AUC based on C3 and C6 showed the most relevant concentrations (r2  = 0.943; precision, 6.4%), even in infants and young children, whereas the predicted AUC based on C3 and C6 was acceptable (r2  = 0.963; precision, 5.7%). CONCLUSIONS: The AUC of busulfan can be predicted based on C6 in adolescent patients. However, there was substantial interindividual variation in busulfan pharmacokinetics in infants and young children, in whom two-point LSS was necessary for accurate AUC prediction.

Optimal sampling strategies for darunavir and external validation of the underlying population pharmacokinetic model.

PURPOSE: A variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible. METHODS: A validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations. RESULTS: External validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly. CONCLUSIONS: Optimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.

Estimation of the area under concentration-time curve of polymyxin B based on limited sampling concentrations in Chinese patients with severe pneumonia.

AIMS: The efficacy and toxicity of polymyxin B (PB) are closely related to its pharmacokinetic/pharmacodynamic (PK/PD) index area under the concentration-time curve (AUC) to minimum inhibitory concentration (MIC) ratio. The purpose of this study was to obtain PK data for PB in Chinese severe pneumonia patients and establish appropriate blood sampling time points for the PB therapeutic drug monitoring (TDM). SUBJECT AND METHOD: After treatment with at least four doses of PB (50 IU, q12h), the blood samples were collected immediately after the end of infusion (C0) and 1.5, 2, 4, 6, 8, and 12 h (C1.5, C2, C4, C6, C8, C12) after PB administration. The PB blood plasma concentrations were determined using an ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). All 42 patients were randomly divided into modeling (n = 24) and validation (n = 18) groups. The relationship between AUCss,24h and PB plasma concentration at each time point in modeling group was analyzed using limited sampling strategy and a PK method based on one-compartment with correction model. RESULTS: C6 scheme was found to provide the most accurate prediction of AUCss,24h values (r2 = 0.984) with the target value of 1.9-4.2 µg/ml at steady state to reach the 50-100 µg h/ml criteria of AUCss,24h. C0 with target value of 1.0-2.8 µg/ml can be considered an alternative sampling scheme (r2 = 0.900) but prediction deviation may exist. C0 and Cmax sampling scheme also demonstrated good predicting ability of AUC values using PK model. CONCLUSION: This study provides a clear plan for the implementation of TDM of PB, which is useful for optimizing the dosing regimen and individualizing treatment in severe pneumonia patients.

Population pharmacokinetics of mycophenolic acid in pediatric patients with juvenile dermatomyositis and optimization of limited sampling strategy.

Juvenile dermatomyositis (JDM) is a rare systemic autoimmune disease specifically affecting children. Mycophenolate mofetil (MMF) is an immunosuppressant used to treat JDM. Mycophenolic acid (MPA) is an active metabolite of MMF. This study aimed to develop a population pharmacokinetic (PPK) model of MPA in children with JDM and optimize the limited sampling strategy (LSS). Fifteen JDM patients treated with MMF, at a median age of 7.35 (range, 3.09-16.1) years, were included. Blood samples were collected at 30 minutes pre-dose, 20 minutes, 60 minutes and 180 minutes post-dose to measure the MPA concentrations. Data were retrospectively collected from the electronic medical records. A two-compartment model with first-order absorption, lag time in absorption, and first-order elimination was developed. Height and co-administered cotrimoxazole were added as the covariates to the model. Concentrations at different time points were simulated and area under the concentration-time curve (AUC0-12 h) was calculated. By removing one sampling point at a time, AUC0-12 h from three-point sampling strategy was re-calculated via Bayesian approach. AUC0-12 h from the three-point sampling strategy (by removing the point at 20 minutes post-dose) had the strongest correlation with AUC0-12 h from the four-point sampling strategy (Pearson's r = 0.971).