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Real-time medication monitoring (RTMM) is a promising tool for improving adherence to inhaled corticosteroids (ICS), but has not been sufficiently tested in children with asthma. We aimed to study the effects of RTMM with short message service (SMS) reminders on adherence to ICS, asthma control, asthma-specific quality of life and asthma exacerbation rate; and to study the associated cost-effectiveness.In a multicentre, randomised controlled trial, children (aged 4-11 years) using ICS were recruited from five outpatient clinics and were given an RTMM device for 12 months. The intervention group also received tailored SMS reminders, sent only when a dose was at risk of omission. Outcome measures were adherence to ICS (RTMM data), asthma control (childhood asthma control test questionnaire), quality of life (paediatric asthma quality of life questionnaire) and asthma exacerbations. Costs were calculated from a healthcare and societal perspective.We included 209 children. Mean adherence was higher in the intervention group: 69.3% versus 57.3% (difference 12.0%, 95% CI 6.7%-17.7%). No differences were found for asthma control, quality of life or asthma exacerbations. Costs were higher in the intervention group, but this difference was not statistically significant.RTMM with tailored SMS reminders improved adherence to ICS, but not asthma control, quality of life or exacerbations in children using ICS for asthma., (Copyright ©ERS 2016.)

Background: Many children with asthma do not have sufficient asthma control, which leads to increased healthcare costs and productivity loss of parents. One of the causative factors are adherence problems. Effective interventions improving medication adherence may therefore improve asthma control and reduce costs. A promising solution is sending real time text-messages via the mobile phone network, when a medicine is about to be forgotten. As the effect of real time text-messages in children with asthma is unknown, the primary aim of this study is to determine the effect of a Real Time Medication Monitoring system (RTMM) with text-messages on adherence to inhaled corticosteroids (ICS). The secondary objective is to study the effects of RTMM on asthma control, quality of life and cost-effectiveness of treatment., Methods: A multicenter, randomized controlled trial involving 220 children (4-11 years) using ICS for asthma. All children receive an RTMM-device for one year, which registers time and date of ICS doses. Children in the intervention group also receive tailored text-messages, sent only when a dose is at risk of omission. Primary outcome measure is the proportion of ICS dosages taken within the individually predefined time-interval. Secondary outcome measures include asthma control (monthly Asthma Control Tests), asthma exacerbations, healthcare use (collected from hospital records, patient reports and pharmacy record data), and disease-specific quality of life (PAQLQ questionnaire). Parental and children's acceptance of RTMM is evaluated with online focus groups and patient questionnaires. An economic evaluation is performed adopting a societal perspective, including relevant healthcare costs and parental productivity loss. Furthermore, a decision-analytic model is developed in which different levels of adherence are associated with clinical and financial outcomes. Also, sensitivity analyses are carried out on different price levels for RTMM., Discussion: If RTMM with tailored text-message reminders proves to be effective, this technique can be used in daily practice, which would support children with suboptimal adherence in their asthma (self)management and in achieving better asthma control and better quality of life., Trial Registration: Netherlands Trial Register NTR2583.

Background: Therapeutic drug monitoring of tacrolimus is a major support to patient management and could help improve the outcome of lung transplant recipients, by minimizing the risk of rejections and infections. However, despite the wide use of tacrolimus as part of maintenance immunosuppressive regimens after lung transplantation, little is known about its pharmacokinetics in this population. Better knowledge of the pharmacokinetics of tacrolimus in lung transplant recipients, and the development of tools dedicated to its therapeutic drug monitoring, could thus help improve their outcome., Objectives: The aims of this study were (i) to characterize the population pharmacokinetics of tacrolimus in lung transplant recipients, including the influence of biological and pharmacogenetic covariates; and (ii) to develop a Bayesian estimator of the tacrolimus area under the blood concentration-time curve from time zero to 12 hours (AUC(12)) for its therapeutic drug monitoring in lung transplant recipients., Methods: A population pharmacokinetic model was developed by nonlinear mixed-effects modelling using NONMEM® version VI, from 182 tacrolimus full concentration-time profiles collected in 78 lung transplant recipients within the first year post-transplantation. Patient genotypes for the cytochrome P450 3A5 (CYP3A5) A6986G single nucleotide polymorphism (SNP) were characterized by TaqMan allelic discrimination. Patients were divided into an index dataset (n = 125 profiles) and a validation dataset (n = 57 profiles). A Bayesian estimator was derived from the final model using the index dataset, in order to determine the tacrolimus AUC(12) on the basis of a limited number of samples. The predictive performance of the Bayesian estimator was evaluated in the validation dataset by comparing the estimated AUC(12) with the trapezoidal AUC(12)., Results: Tacrolimus pharmacokinetics were described using a two-compartment model with Erlang absorption and first-order elimination. The model included cystic fibrosis (CF) and CYP3A5 polymorphism as covariates. The relative bioavailability in patients with CF was approximately 60% of the relative bioavailability observed in patients without CF, and the transfer rate constant between the transit compartments was 2-fold smaller in patients with CF than in those without CF (3.32 vs 7.06 h-1). The apparent clearance was 40% faster in CYP3A5 expressers than in non-expressers (24.5 vs 17.5 L/h). Good predictive performance was obtained with the Bayesian estimator developed using the final model and concentrations measured at 40 minutes and at 2 and 4 hours post-dose, as shown by the mean bias (1.1%, 95% CI -1.4, 3.7) and imprecision (9.8%) between the estimated and the trapezoidal AUC(12). The bias was >20% in 1.8% of patients., Conclusion: Population pharmacokinetic analysis showed that lung transplant patients with CF displayed lower bioavailability and a smaller transfer rate constant between transit compartments than those without CF, while the apparent clearance was faster in CYP3A5 expressers than in non-expressers. The Bayesian estimator developed in this study provides an accurate prediction of tacrolimus exposure in lung transplant patients, with and without CF, throughout the first year post-transplantation. This tool may allow routine tacrolimus dose individualization and may be used to conduct clinical trials on therapeutic drug monitoring of tacrolimus after lung transplantation.

Background and Objectives: Mycophenolate mofetil (MMF) is an immunosuppressive drug used in renal transplant patients. Upon oral administration it is hydrolyzed to the active agent mycophenolic acid (MPA). In renal transplant recipients, MMF therapy is optimal when the area under the curve of MPA is 30 to 60 mg·h/L. When MMF doses are adjusted, a linear relationship between dose and MPA exposure is assumed. In this study, the linearity of MMF pharmacokinetics was investigated., Design, Setting, Participants, & Measurements: MPA concentration-time profiles from renal transplant recipients cotreated with cyclosporine (n = 140) or tacrolimus (n = 101) were analyzed retrospectively using nonlinear mixed-effects modeling. The correlation between the MMF dose and the pharmacokinetics parameters was evaluated., Results: In the developed population pharmacokinetics model MPA clearance and the central volume of distribution were correlated with cyclosporine coadministration and time posttransplantation. The pharmacokinetics of MPA were not linear. Bioavailability decreased with increasing MMF doses. Compared with an MMF dose of 1000 mg (=100%), relative bioavailability was 123%, 111%, 94%, and 90% in patients receiving MMF doses of 250, 500, 1500, and 2000 mg in combination with cyclosporine (P < 0.001); respective values in tacrolimus-cotreated patients were 176%, 133%, 85%, and 76% (P < 0.001). Because of the decreasing relative bioavailability, MPA exposure will increase less than proportionally with increasing MMF doses., Conclusions: MMF exhibits nonlinear pharmacokinetics. This should be taken into account when performing therapeutic drug monitoring.

Previous studies predicted that limited sampling strategies (LSS) for estimation of mycophenolic acid (MPA) area-under-the-curve (AUC(0-12)) after ingestion of enteric-coated mycophenolate sodium (EC-MPS) using a clinically feasible sampling scheme may have poor predictive performance. Failure of LSS was thought to be due to the slow absorption of MPA causing late and variable times of maximum MPA concentration and variable predose concentrations. The aim of this study was to formally test the performance of LSS by developing and validating LSS for estimation of MPA AUC(0-12) after EC-MPS administration. Pharmacokinetic data from 109 renal transplant recipients collected during the maintenance period after transplantation were analysed retrospectively. LSS were developed separately for renal transplant patients who concurrently used cyclosporine (n = 79) and for patients not concurrently treated with cyclosporine (n = 30). Data were split into an index and a validation data set. For clinical feasibility reasons, a LSS could consist of a maximum of 3 sampling time points with the latest sample drawn 2 hours after drug administration. LSS with the latest sample drawn 3 hours after drug administration or even later were also tested. The validation of the developed LSS showed that MPA AUC(0-12) for patients concurrently treated with cyclosporine was best estimated by AUC(0-12) (mg x h x L(-1)) = 36.536 + 1.642 x C0.5 + 0.569 x C1.5 + 0.905 x C2 (r2 = 0.33, bias = -1.0 mg x h x L(-1), precision = 24 mg x h x L(-1)), whereas AUC(0-12) [mg x h x L(-1)] = 19.801 + 1.827 x C0.5 + 1.111 x C1 + 1.429 x C2 was the best AUC(0-12) estimator for patients not cotreated with cyclosporine (r2 = 0.31, bias = 0.4 mg x h x L(-1), precision = 14.5 mg x h x L(-1)). Both LSS showed poor precision and overestimation of AUC(0-12) values below the therapeutic window and underestimation of AUC(0-12) values above the therapeutic window of MPA. Using C3 as latest sampling time point improved the fit slightly, but not satisfactory, with r2 still <0.40 and precision still >14.0 mg x h x L(-1). Estimation of MPA AUC(0-12) with LSS for EC-MPS drawn within 2 or 3 hours postdose in renal transplant recipients in the maintenance period is likely to result in biased and imprecise results.

Mycophenolate mofetil (MMF) is increasingly used for the treatment of autoimmune diseases (AID). In renal transplant recipients, it has been demonstrated that adjustment of the MMF dose according to the area under the plasma concentration versus time curve (AUC) of mycophenolic acid (MPA), the active moiety of MMF, improves clinical outcome. The aim of this study was to develop a maximum a posteriori Bayesian estimator (MAP-BE) to estimate MPA AUC(0-12) in patients with AID using a limited number of samples. The predictive performance of the MAP-BE was compared with a multiple linear regression method. Full MPA concentration versus time curves were available from 38 patients with AID treated with MMF. Nonlinear mixed-effect modeling was used to develop a population pharmacokinetic model. Patients were divided in an index and a validation data set. The pharmacokinetic model derived from the index data set was used to develop several MAP-BEs. The Bayesian estimators were used to predict AUC(0-12) in the validation data set on the basis of a limited number of blood samples. The bias and precision of these predictions were compared with those of limited sampling strategies developed with multiple linear regression. The absorption of MPA was described with 2 first-order processes with a short and a long lag time and a subsequent first-order elimination. The 2-compartment model accounted for the enterohepatic recirculation of MPA as well. Using 1-4 samples, MPA AUC(0-12) was adequately estimated by the MAP-BE. Bias (-5.5%) was not significantly different from zero, and precision was below 27%. The predictive performance of the multiple linear regression method was comparable. In conclusion, MAP-BEs were developed for the estimation of MPA AUC(0-12) in patients with AID. The predictive performance was good and comparable to those of the multiple linear regression method. Due to its flexibility with respect to sample times, the MAP-BE may be preferred over the multiple linear regression method.

Treatment with the immunosuppressive agent mycophenolate mofetil (MMF) decreases the risk of rejection after renal transplantation and improves graft survival compared with azathioprine. The exposure to the active metabolite mycophenolic acid (MPA) is correlated to the risk of developing acute rejection. The interpatient variability in exposure of MPA is wide relative to the proposed therapeutic window of the MPA AUC(0 12) (30 - 60 mg.h/l). The pharmacokinetics of MPA are influenced by patient characteristics such as gender, time after transplantation, serum albumin concentration, renal function, comedication and pharmacogenetic factors. Therapeutic drug monitoring is likely to reduce inter-patient variability. Limited sampling strategies are used to predict the full AUC(0 12). Three prospective randomised studies compared concentration controlled MMF therapy to a fixed-dose regimen. Preliminary outcomes of these studies showed conflicting results and longer follow up is needed to further clarify the role of therapeutic drug monitoring in increasing the therapeutic potential of MMF.