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.

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.

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.

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.

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.

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.

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).

The Evaluation of Multiple Linear Regression-Based Limited Sampling Strategies for Mycophenolic Acid in Children with Nephrotic Syndrome.

We evaluated mycophenolic acid (MPA) limited sampling strategies (LSSs) established using multiple linear regression (MLR) in children with nephrotic syndrome treated with mycophenolate mofetil (MMF). MLR-LSS is an easy-to-determine approach of therapeutic drug monitoring (TDM). We assessed the practicability of different LSSs for the estimation of MPA exposure as well as the optimal time points for MPA TDM. The literature search returned 29 studies dated 1998-2020. We applied 53 LSSs (n = 48 for MPA, n = 5 for free MPA [fMPA]) to predict the area under the time-concentration curve (AUCpred) in 24 children with nephrotic syndrome, for whom we previously determined MPA and fMPA concentrations, and compare the results with the determined AUC (AUCtotal). Nine equations met the requirements for bias and precision ±15%. The MPA AUC in children with nephrotic syndrome was predicted the best by four time-point LSSs developed for renal transplant recipients. Out of five LSSs evaluated for fMPA, none fulfilled the ±15% criteria for bias and precision probably due to very high percentage of bound MPA (99.64%). MPA LSS for children with nephrotic syndrome should include blood samples collected 1 h, 2 h and near the second MPA maximum concentration. MPA concentrations determined with the high performance liquid chromatography after multiplying by 1.175 may be used in LSSs based on MPA concentrations determined with the immunoassay technique. MPA LSS may facilitate TDM in the case of MMF, however, more studies on fMPA LSS are required for children with nephrotic syndrome.

A limited sampling strategy to estimate exposure of once-daily modified release tacrolimus in renal transplant recipients using linear regression analysis and comparison with Bayesian population pharmacokinetics in different cohorts.

PURPOSE: Tacrolimus has a narrow therapeutic window. Measuring trough level (C0) as surrogate for drug exposure (AUC) in renal transplant recipients has limitations. Therefore, limited sampling strategies (LSS's) have been developed. For the newer modified release, once-daily formulation (Tac QD) LSS's are based on either linear regression analysis (LRA) or population pharmacokinetics with maximum a posteriori Bayesian (MAPB) estimation. The predictive performances of both methods were compared, also to LSS's as described in literature. METHODS: LSS's (maximally three sampling time points) were developed for Tac QD from full 24-h sampling by LRA in 27 Caucasian, stable renal transplant recipients. Performance for accuracy (mean absolute prediction error < 10%) and precision (root mean squared error < 15%) was quantified also after MAPB estimation in two independent groups (early and late post-transplant, n = 12 each). RESULTS: LRA determined a single 8 hours post-dose measurement (C8) to fulfil predefined criteria for accuracy (MAPE 3.41%) and precision (RMSE 4.28%). The best LSS contained C2, C8 and C12 for the stable (MAPE 2.42%, RMSE 3.1%) and the early post-transplant group (MAPE 2.46%, RMSE 3.14%). LRA did not include C0 for any LSS, unless it was forced into the model. MAPB estimation showed similar performance. CONCLUSIONS: In renal transplant patients, sampling in the elimination phase (C8) accurately predicted Tac QD exposure, contrary to C0. The 3-point sampling C2, C8 and C12 had the best performance and is also valid early post-transplant. These LSS's were similarly predictive with MAPB estimation. Dried blood spot could facilitate late sampling in clinical practice.

Limited sampling strategy to predict the area under the curve of tacrolimus in Mexican renal transplant pediatric patients receiving Prograf® or non-innovator formulations.

TDM of tacrolimus is usually performed with trough levels (C0h ). However, in pediatric patients, C0h may not be an adequate marker. The AUC is considered a more suitable indicator of drug exposure. As several blood samples are needed for the estimation of AUC, and LSS for predicting tacrolimus AUC and optimizing the dose adjustment have been proposed. Moreover, in emerging countries such as Mexico, non-innovator formulations, which bioequivalence has not been demonstrated, are frequently used. Hence, the aim of this study was to develop and validate a LSS to predict the tacrolimus AUC0-12h in Mexican pediatric kidney transplant recipients who received either Prograf® or non-innovator tacrolimus formulations. A total of 56 pharmacokinetic profiles were randomized into two groups: model development (n = 28) and model validation (n = 28). The limited sampling equations were obtained after a stepwise multiple regression using AUC as the dependent variable and tacrolimus blood concentrations, quantified by CMIA, at different time points as the independent variables. The final equation included observed concentrations at 1 hour (C1h ) and 4 hours (C4h ) after dose administration. The predictive performance of the model was adequate in terms of both, bias and precision. Results strongly suggest that the clinical use of this LSS could provide an ethical, cost-, and time-effective method in the TDM of tacrolimus in pediatric patients with kidney transplant. The model proved to be adequate with either Prograf® or non-innovator tacrolimus formulations of dubious bioequivalence.

Limited sampling strategy to predict mycophenolic acid area under the curve in pediatric patients with nephrotic syndrome: a retrospective cohort study.

PURPOSE: Limited sampling strategy (LSS) is a precise and relatively convenient therapeutic drug monitoring method. We evaluated LSSs for mycophenolic acid (MPA) in children with nephrotic syndrome treated with mycophenolic mofetil (MMF) and validated the LSSs using two different approaches. METHODS: We measured MPA plasma concentrations in 31 children using HPLC-UV method and received 37 MPA pharmacokinetic profiles (0-12 h). For six children, MPA profiles were estimated twice after two MMF doses. LSSs were developed using multilinear regression with STATISTICA and R software and validated using validation group and bootstrap method, respectively. RESULTS: The best three time point equations included C1, C3, C6 (good guess 83%, bias - 2.78%; 95% confidence interval (CI) - 9.85-0.46); C1, C2, C6 (good guess 72%, bias 0.72%; 95% CI - 5.33-7.69); and C1, C2, C4 (good guess 72%, bias 2.05%; 95% CI - 4.92-13.01) for STATISTICA software. For R software, the best equations consisted of C1, C3, C6 (good guess 92%, bias - 2.69%; 95% CI - 27.18-33.75); C0, C1, C3 (good guess 84%, bias - 2.11%; 95% CI - 24.19-22.29); and C0, C1, C2 (good guess 84%, bias - 0.48%; 95% CI - 30.77-54.07). During validation, better results were obtained for R evaluations, i.e., bootstrap method. CONCLUSIONS: The most useful equations included C0, C1, C3 and C0, C1, C2 time points; however, the most precise included C1, C3, C6 time points because of MPA enterohepatic recirculation. Better results were obtained for bootstrap validation due to greater number of patients. Validated LSS should be used only in the population for which it was developed. As there is growing evidence that underexposure of MPA is associated with insufficient treatment response, we recommend the introduction of therapeutic drug monitoring for MPA in children with nephrotic syndrome.

Limited sampling strategy for the estimation of the area under the concentration-time curve for ganciclovir in Chinese adult renal allograft recipients.

OBJECTIVES: Valganciclovir (VGCV) treatment is recommended for the prevention of cytomegalovirus (CMV) infection in renal allograft recipients. The aim of the present study is to investigate the pharmacokinetic characteristics of ganciclovir (GCV) after administration of VGCV in Chinese adult renal allograft recipients and estimate the exposure to GCV using limited sampling strategy (LSS). METHODS: Forty Chinese renal allograft recipients were given 450 mg or 900 mg VGCV daily. Blood samples were drawn before treatment and 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, and 24 h after 5 days of VGCV therapy, and the plasma concentrations of VGCV and GCV were determined using a liquid chromatography-mass spectrometry assay. The major pharmacokinetic parameters for GCV and VGCV were determined using a noncompartmental assay. Multiple stepwise linear regression analysis was conducted to establish a model equation for the estimation of the GCV AUC0-24 h in Chinese patients using LSS. RESULTS: In the 450 and 900 mg groups, the Cmax for VGCV was 0.2 ± 0.10 and 0.4 ± 0.16 mg/L, respectively; the Cmax for GCV was 4.2 ± 1.1 and 8.6 ± 1.6 mg/L, respectively; and the AUC0-24 h for GCV was 28.4 ± 8.4 and 60.7 ± 17.5 mg·h/L, respectively. For the establishment of LSS models, 40 patients were divided into the training group (n = 24) and validation group (n = 16). The model equations used for the calculation of AUC0-24 h for GCV were established in the training group by using multiple linear regression assay. Equations including AUC = 8.1 + 29.7 × C0 + 5.7 × C4 (r2 = 0.91) and AUC = - 0.4 + 11.0 × C0 + 2.1 × C2 + 13.7 × C8 (r2 = 0.98) were acceptable. The %MPE and %MAPE values obtained from the validation group for the two model equations were 5.89 ± 14.5% and 12.1 ± 9.53%, and - 1.30 ± 4.40% and 3.28 ± 3.11%, respectively. CONCLUSIONS: The LSS models that included C0 and C4 or C0, C2, and C8 in the estimation of AUC0-24 h for GCV had favorable performance and can be used for therapeutic drug monitoring in the prevention of CMV infection using VGCV in Chinese renal allograft recipients.

Limited sampling strategy for determining metformin area under the plasma concentration-time curve.

AIM: The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin. METHODS: Metformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS-derived AUC(0,24 h) was compared with the parameter 'best estimate' obtained by non-compartmental analysis using all plasma concentration data points. Correlation between the LSS-derived and the best estimated AUC(0,24 h) (r(2) ), bias and precision of the LSS estimates were quantified. The LSS models were validated in independent cohorts. RESULTS: A two-point (3 h and 10 h) regression equation with no intercept estimated accurately the individual AUC(0,24 h) in the development cohort: r(2)  = 0.927, bias (mean, 95% CI) -0.5, -2.7-1.8% and precision 6.3, 4.9-7.7%. The accuracy of the two point LSS model was verified in study cohorts of individuals receiving single 500 or 1000 mg (r(2)  = -0.933-0.934) or seven 1000 mg daily doses (r(2)  = 0.918), as well as using data from 16 published studies covering a wide range of metformin doses, demographics, clinical and experimental conditions (r(2)  = 0.976). The LSS model reproduced previously reported results for effects of polymorphisms in OCT2 and MATE1 genes on AUC(0,24 h) and renal clearance of metformin. CONCLUSIONS: The two point LSS algorithm may be used to assess the systemic exposure to metformin under diverse conditions, with reduced costs of sampling and analysis, and saving time for both subjects and investigators.

Therapeutic drug monitoring of cyclosporine and area under the curve prediction using a single time point strategy: appraisal using peak concentration data.

There is an ongoing debate on the use of a single concentration time point C2 for therapeutic drug monitoring (TDM) and exposure prediction for cyclosporine. The objective of the present work was to evaluate the relationship between the peak concentration (Cmax ) versus area under the curve (AUC) for cyclosporine. Using published data from renal transplant patients from an 8-12 week study with two formulations, a simple linear regression model represented by AUC - cyclosporine = Cmax - Cyclosporine × 3.9965 + 384.5 (r = 0.9647; p < 0.001) was developed. Using the regression equation, predictions of AUC from the reported Cmax data were performed; the fold difference between observed vs predicted AUC was computed and the root mean square error for the prediction was calculated. While all but one of the predicted AUCs were contained within a 0.5-2-fold difference (99.1%), a greater proportion of the AUC values were predicted within a narrower range of 0.75 to 1.5-fold difference (78.2%), suggesting the utility of Cmax as the right surrogate for predicting the AUC for cyclosporine with a correlation coefficient of 0.8698 (n = 126; p < 0.001) and a RMSE of 26.2%. Since the time to Cmax generally varies from 1 to 2 h, although the results validate the use of C2, there may be an opportunity to explore the suitability of C1 or C1.5 in a prospective study for the purpose of TDM and AUC prediction of cyclosporine.

Impact of sampling time deviations on the prediction of the area under the curve using regression limited sampling strategies.

The regression limited sampling strategy approach (R-LSS), which is based on a small number of blood samples drawn at selected time points, has been used as an alternative method for the estimation of the area under the concentration-time curve (AUC). However, deviations from planned sampling times may affect the performance of R-LSS, influencing related therapeutic decisions and outcomes. The aim of this study was to investigate the impact of different sampling time deviation (STD) scenarios on the estimation of AUC by the R-LSS using a simulation approach. Three types of scenarios were considered going from the simplest case of fixed deviations, to random deviations and then to a more realistic case where deviations of mixed nature can occur. In addition, the sensitivity of the R-LSS to STD in each involved sampling point was evaluated. A significant impact of STD on the performance of R-LSS was demonstrated. The tolerance of R-LSS to STD was found to depend not only on the number of sampling points but more importantly on the duration of the sampling process. Sensitivity analysis showed that sampling points at which rapid concentration changes occur were relatively more critical for AUC prediction by R-LSS. As a practical approach, nomograms were proposed, where the expected predictive performance of R-LSS was provided as a function of STD information. The investigation of STD impact on the predictive performance of R-LSS is a critical element and should be routinely performed to guide R-LSS selection and use.