Evaluation of limited sampling strategies for tacrolimus.
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ABSTRACT:
SUBMITTER: Op den Buijsch RA
PROVIDER: S-EPMC2039832 | biostudies-other | 2007 Nov
REPOSITORIES: biostudies-other
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Project description:BackgroundMeltdose tacrolimus (Envarsus®) has been marketed as a formulation achieving a more consistent tacrolimus exposure. Due to the narrow therapeutic window of tacrolimus, dose individualization is essential. Relaxation of the upper age limits for kidney transplantations has resulted in larger numbers of elderly patients receiving tacrolimus. However, due to the physiological changes caused by aging, the tacrolimus pharmacokinetics (PK) might be altered. The primary aim was to develop a population PK model in elderly kidney transplant recipients. Secondary aims were the development and evaluation of a limited sampling strategy (LSS) for AUC estimation.MethodsA total of 34 kidney transplant recipients aged ≥65 years, starting on meltdose tacrolimus directly after transplantation, were included. An eight-point whole blood AUC0-24h and an abbreviated dried blood spot (DBS) AUC0-24h were obtained. The PK data were analyzed using nonlinear mixed effect modeling methods.ResultsThe PK data were best described using a two-compartment model, including three transit compartments and a mixture model for oral absorption. The best three-sample LSS was T = 0, 2, 6 h. The best four-sample LSSs were T = 0, 2, 6, 8 h and T = 0, 1, 6, 8 h.ConclusionsThe developed population PK model adequately described the tacrolimus PK data in a population of elderly kidney transplant recipients. In addition, the developed population PK model and LSS showed an adequate estimation of tacrolimus exposure, and may therefore be used to aid in tacrolimus dose individualization.
| S-EPMC10819724 | biostudies-literature
Project description:Background: Kidney transplantation (KTx) requires immunosuppressive drugs such as Tacrolimus (TAC) which is mainly metabolized by CYP3A5. TAC is routinely monitored by trough levels (C0) although it has not shown to be a reliable marker. The area-under-curve (AUC) is a more realistic measure of drug exposure, but sampling is challenging in pediatric patients. Limited-sampling strategies (LSS) have been developed to estimate AUC. Herein, we aimed to determine AUC(0-24) and CYP3A5 genotype in Chilean pediatric kidney recipients using extended-release TAC, to evaluate different LSS-AUC(0-24) formulas and dose requirements. Patients and methods: We analyzed pediatric kidney recipients using different extended-release TAC brands to determine their trapezoidal AUC(0-24) and CYP3A5 genotypes (SNP rs776746). Daily TAC dose (TAC-D mg/kg) and AUC(0-24) normalized by dose were compared between CYP3A5 expressors (*1/*1 and *1/*3) and non-expressors (*3/*3). We evaluated the single and combined time-points to identify the best LSS-AUC(0-24) model. We compared the performance of this model with two pediatric LSS-AUC(0-24) equations for clinical validation. Results: Fifty-one pharmacokinetic profiles were obtained from kidney recipients (age 13.1 ± 2.9 years). When normalizing AUC(0-24) by TAC-D significant differences were found between CYP3A5 expressors and non-expressors (1701.9 vs. 2718.1 ng*h/mL/mg/kg, p < 0.05). C0 had a poor fit with AUC(0-24) (r 2 = 0.5011). The model which included C0, C1 and C4, showed the best performance to predict LSS-AUC(0-24) (r 2 = 0.8765) and yielded the lowest precision error (7.1% ± 6.4%) with the lowest fraction (9.8%) of deviated AUC(0-24), in comparison to other LSS equations. Conclusion: Estimation of LSS-AUC(0-24) with 3 time-points is an advisable and clinically useful option for pediatric kidney recipients using extended-release TAC to provide better guidance of decisions if toxicity or drug inefficacy is suspected. The different CYP3A5 genotypes associated with variable dose requirements reinforce considering genotyping before KTx. Further multi-centric studies with admixed cohorts are needed to determine the short- and long-term clinical benefits.
| S-EPMC10043346 | biostudies-literature
Project description:BackgroundLimited sampling strategy (LSS) is a validated method to estimate pharmacokinetic (PK) parameters from a reduced number of samples. Omeprazole is used to phenotype in vivo cytochrome P450 (CYP) 2C19 activity. This study examined an LSS using 2 estimation methods to determine apparent oral clearance (CL/F) and thus CYP2C19 activity.MethodsData from 7 previously published studies included healthy subjects receiving a single, oral dose of omeprazole with intensive PK sampling. CL/F was estimated using noncompartmental analysis (NCA) and population PK modeling. LSS was simulated by selecting the 1, 2, 4, and/or 6-hour postdose time points. Linear regression was performed to assess whether CL/F estimated from limited sampling could accurately predict CL/F from the full PK profile.ResultsMedian CL/F was 23.7 L/h by NCA and 19.3 L/h by population PK modeling. In comparing the LSS NCA estimated versus observed CL/F, all evaluated linear regression models had unacceptable coefficients of determination (r, range: 0.14-0.81). With the population PK approach, 737 plasma concentrations (n = 71) and CYP2C19 genotype data were described with a 1-compartment structural model with mixed zero and first-order absorption and lag time. In comparing the population PK LSS estimated versus observed CL/F, all evaluated linear regression models had unacceptable r (range: 0.02-0.74). Post hoc comparison of CYP2C19 poor metabolizers versus CYP2C19 extensive metabolizers resulted in significantly lower CL/F in poor metabolizers versus extensive metabolizers.ConclusionsOmeprazole LSS performed poorly in estimating CL/F using 2 separate estimation approaches and does not seem to be a suitable method for determining CYP2C19 activity.
| S-EPMC6237623 | biostudies-literature
Project description:Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW\Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC0-24) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R2=0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R2=0.89; bias, -0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.).
| S-EPMC4335880 | biostudies-literature
Project description:PurposeThe aim of this study was to develop a clinically applicable limited sampling strategy for ambulatory Caucasian kidney transplant patients to estimate area under the curve in a 24-h period (AUC0-24) of prolonged-release tacrolimus.MethodsTwenty six kidney recipients, at least 6 months after transplantation, receiving prolonged-release tacrolimus, were enrolled. In each patient, seven blood samples were collected during a period of 24 h by use of the validated dried blood spot method. Best subset selection multiple linear regression was performed to derive limited sampling strategy (LSS). The equations were constrained to include a maximum of three samples collected within 4 h after the intake to maintain clinical applicability. To assess the predictive performance of LSS, residuals for each patient were calculated based on models fitted to a dataset where that patient was omitted.ResultsThe prediction formula for the AUC(0-24) using the time points 0, 2, and 4 h after ingestion (C(0h)-C(2h)-C(4h)) provided the highest correlation with the AUC(0-24) (r(2)?= 0.95): AUC0-24 = 44.9 + 8.9 × C(0h)?+ 2.1 × C(2h)?+ 7.6 × C(4h). Measures for bias and precision, i.e., median percentage prediction error (MPPE) and median absolute prediction error (MAPE), were 0.4 and 4.8%, respectively. For the same patients, the correlation between C(24h) and AUC0-24 was worse (r(2)?= 0.77) while MPPE and MAPE were 6.2 and 7.2%, respectively.ConclusionIn the outpatient department, a LSS using C(0h)-C(2h)-C(4h) can be used for reliable estimation of the AUC(0-24) of prolonged-release tacrolimus.
| S-EPMC4464598 | biostudies-literature
Project description:Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC0-24) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach. The popPK model and Bayesian LSS were developed using data from 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum time span, 8 h; minimum interval, 1 h; 1 to 3 samples) were tested. Performance criteria were root-mean-square error (RMSE) of <15%, mean prediction error (MPE) of <5%, and r2 value of >0.95. A one-compartment model with lag time best described the data while only slightly underestimating the AUC0-24 (mean, -7.9%; standard error [SE], 1.7%). The Bayesian LSS using 0- and 5-h postdose samples (RMSE, 8.8%; MPE, 0.42%; r2 = 0.957) adequately estimated the AUC0-24, with a mean underestimation of -4.4% (SE, 2.7%). The multiple linear regression LSS using 0- and 4-h postdose samples (RMSE, 7.0%; MPE, 5.5%; r2 = 0.977) was internally validated, with a mean underestimation of -0.46% (SE, 2.0%). In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin. (This study has been registered at ClinicalTrials.gov under identifier NCT01918397.).
| S-EPMC6256746 | biostudies-literature
Project description:Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0-12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0-12h estimation was evaluated by Passing-Bablok regression analysis and Bland-Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0-12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0-12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0-12h for MPA.
| S-EPMC8640522 | biostudies-literature
Project description:BACKGROUND:An extended-release, once-daily, oral formulation of tacrolimus is currently used after kidney transplantation as a substitute for the conventional twice-daily formulation. The purpose of this study was to provide a limited sampling strategy with minimum and optimum sampling points to predict the tacrolimus area under the concentration-time curve (AUC) after administration of once-daily tacrolimus in de novo adult kidney transplant patients. METHODS:A total of 36 adult Japanese kidney transplant patients receiving once-daily tacrolimus were included: 31 were allocated to a study group to develop limited sampling strategy (LSS) model equations based on multiple stepwise linear regression analysis, and 5 were allocated to a validation group to estimate the precision of the LSS equations developed by the study group. Twelve-hour AUC (AUC0-12) was calculated by the trapezoidal rule, and the relationship between individual concentration points and AUC0-12 were determined by multiple linear regression analysis. The coefficient of determination (R2) was used to assess the goodness-of-fit of the regression models. Three error indices (mean error, mean absolute error, and root mean squared prediction error) were calculated to evaluate predictive bias, accuracy, and precision, respectively. Quality of the statistical models was compared with Akaike's information criterion (AIC). RESULTS:A four-point model using C0, C2, C4 and C6 gave the best fit to predict AUC0-12 (R2 = 0.978). In the three- and two-point models, the best fits were at time points C2, C4, and C6 (R2 = 0.973), and C2 and C6 (R2 = 0.962), respectively. All three models reliably estimated tacrolimus AUC0-12, consistent with evaluations by the three error indices and Akaike's information criterion. Practically, the two-point model with C2 and C6 was considered to be the best combination, providing a highly accurate prediction and the lowest blood sampling frequency. CONCLUSIONS:The two-point model with C2 and C6 may be valuable in reducing the burden on patients, as well as medical costs, for once-daily tacrolimus monitoring.
| S-EPMC6905578 | biostudies-literature
Project description:ObjectiveTo compare the costs and effects of three sampling strategies for human papillomavirus (HPV) primary screening.DesignCost-consequence analysis from a health system perspective using a deterministic decision tree model.SettingEngland.ParticipantsA cohort of 10 000 women aged 25-65 years eligible for the National Health Service Cervical Screening Programme (NHSCSP).MethodsThe model was based on the NHSCSP HPV primary screening pathway and adapted for self-sampling. It used a 3-year cycle: routine screening (year 1) and recall screening (years 2/3). Parameter inputs were informed using published studies, NHSCSP reports and input from experts and manufacturers. Costs were from 2020/2021, British pound sterling (£).InterventionsThree sampling strategies were implemented: (1) routine clinician-collected cervical sample, (2) self-collected first-void (FV) urine, (3) self-collected vaginal swab. The hypothetical self-sampling strategies involved mailing women a sampling kit.Main outcome measuresPrimary outcomes: overall costs (for all screening steps to colposcopy), number of complete screens and cost per complete screen.Secondary outcomesnumber of women screened, number of women lost to follow-up, cost per colposcopy and total screening costs for a plausible range of uptake scenarios.ResultsIn the base case, the average cost per complete screen was £56.81 for clinician-collected cervical sampling, £38.57 for FV urine self-sampling and £40.37 for vaginal self-sampling. In deterministic sensitivity analysis, the variables most affecting the average cost per screen were the cost of sample collection for clinician-collected sampling and the cost of laboratory HPV testing for the self-sampling strategies. Scaled to consider routine screening in England, if uptake in non-attenders increased by 15% and 50% of current screeners converted to self-sampling, the NHSCSP would save £19.2 million (FV urine) or £16.5 million (vaginal) per year.ConclusionSelf-sampling could provide a less costly alternative to clinician-collected sampling for routine HPV primary screening and offers opportunities to expand the reach of cervical screening to under-screened women.
| S-EPMC10254906 | biostudies-literature
Project description:BackgroundThe current World Health Organization (WHO) target for the three major soil-transmitted helminth (STH) infections is to reduce prevalence of moderate-to-heavy infections to below 1% by 2020. In terms of monitoring and evaluation (M&E), the current WHO guidelines for control of STHs recommend evaluation of infection levels in school-age children (SAC) after five to six years of preventive chemotherapy (PC), using the standard Kato-Katz faecal smear. Here, we assess the predictive performance of various sampling designs for the evaluation of the morbidity target.Methodology/principal findingsUsing two mathematical models for STH transmission and control, we simulate how the number of villages and SAC sampled affect the ability of survey results in sentinel villages to predict the achievement of the morbidity target in PC implementation units (e.g. districts). As PC is stopped when the prevalence of infection in SAC in sentinel villages is less than 1%, we estimate the positive predictive value (PPV) of this indicator for meeting the morbidity target in the whole district. The PPV varies by species and PC strategy, and it is generally higher in areas with lower pre-control prevalence. Sampling a fixed number of SAC spread out over 10 instead of 5 sentinel villages may increase the PPV by up to 20 percentage points. If every SAC in a village is tested, a higher number of villages may increase the PPV by up to 80 percentage points. Increasing the proportion of SAC tested per village does not result in a relevant increase of PPV.Conclusions/significanceAlthough the WHO guidelines provide a combined strategy to control the three STH species, the efficacy of PC strategies clearly differs by species. There is added value in considering more villages within implementation units for M&E of morbidity targets, the extent varying by STH species. A better understanding of pre- and post-control local STH prevalence levels is essential for an adequate M&E strategy including the definition of morbidity targets at the appropriate geographical scale.
| S-EPMC6615707 | biostudies-literature