Ten years ago, a consensus report on the optimization of tacrolimus was published in this journal. In 2017, the Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicity (IATDMCT) decided to issue an updated consensus report considering the most relevant advances in tacrolimus pharmacokinetics (PK), pharmacogenetics (PG), pharmacodynamics, and immunologic biomarkers, with the aim to provide analytical and drug-exposure recommendations to assist TDM professionals and clinicians to individualize tacrolimus TDM and treatment. The consensus is based on in-depth literature searches regarding each topic that is addressed in this document. Thirty-seven international experts in the field of TDM of tacrolimus as well as its PG and biomarkers contributed to the drafting of sections most relevant for their expertise. Whenever applicable, the quality of evidence and the strength of recommendations were graded according to a published grading guide. After iterated editing, the final version of the complete document was approved by all authors. For each category of solid organ and stem cell transplantation, the current state of PK monitoring is discussed and the specific targets of tacrolimus trough concentrations (predose sample C0) are presented for subgroups of patients along with the grading of these recommendations. In addition, tacrolimus area under the concentration–time curve determination is proposed as the best TDM option early after transplantation, at the time of immunosuppression minimization, for special populations, and specific clinical situations. For indications other than transplantation, the potentially effective tacrolimus concentrations in systemic treatment are discussed without formal grading. The importance of consistency, calibration, proficiency testing, and the requirement for standardization and need for traceability and reference materials is highlighted. The status for alternative approaches for tacrolimus TDM is presented including dried blood spots, volumetric absorptive microsampling, and the development of intracellular measurements of tacrolimus. The association between CYP3A5 genotype and tacrolimus dose requirement is consistent (Grading A I). So far, pharmacodynamic and immunologic biomarkers have not entered routine monitoring, but determination of residual nuclear factor of activated T cells–regulated gene expression supports the identification of renal transplant recipients at risk of rejection, infections, and malignancy (B II). In addition, monitoring intracellular T-cell IFN-g production can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II). Although cell-free DNA seems a promising biomarker of acute donor injury and to assess the minimally effective C0 of tacrolimus, multicenter prospective interventional studies are required to better evaluate its clinical utility in solid organ transplantation. Population PK models including CYP3A5 and CYP3A4 genotypes will be considered to guide initial tacrolimus dosing. Future studies should investigate the clinical benefit of time-to-event models to better evaluate biomarkers as predictive of personal response, the risk of rejection, and graft outcome. The Expert Committee concludes that considerable advances in the different fields of tacrolimus monitoring have been achieved during this last decade. Continued efforts should focus on the opportunities to implement in clinical routine the combination of new standardized PK approaches with PG, and valid biomarkers to further personalize tacrolimus therapy and to improve long-term outcomes for treated patients.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Tacrolimus is an immunosuppressant agent, largely used in kidney transplantation, with a narrow therapeutic range. • Therapeutic drug monitoring of tacrolimus exposure improves the efficacy and toxicity of this drug. • Separated Bayesian estimators have been developed to estimate tacrolimus exposure following administration of Prograf® and the prolonged release formulation Advagraf®. WHAT THIS STUDY ADDS • A population model was developed to compare the pharmacokinetics of 32 patients treated with Prograf® and 41 treated with Advagraf®. • A mixture model and a model using formulation as covariates were developed to describe the bimodal distribution in the absorption rate following Advagraf® administration. Comparison of these two models showed that the nonmixture model was adequate. • A single Bayesian estimator was developed to estimate the exposure for both formulations, which is more suitable for clinical practice. AIM To investigate the differences in the pharmacokinetics of Prograf® and the prolonged release formulation Advagraf® and to develop a Bayesian estimator to estimate tacrolimus inter‐dose area under the curve (AUC) in renal transplant patients receiving either Prograf® or Advagraf®. METHODS Tacrolimus concentration–time profiles were collected, in adult renal transplant recipients, at weeks 1 and 2, and at months 1, 3 and 6 post‐transplantation from 32 Prograf® treated patients, and one profile was collected from 41 Advagraf® patients more than 12 months post‐transplantation. Population pharmacokinetic (popPK) parameters were estimated using nonmem®. In a second step, the popPK model was used to develop a single Bayesian estimator for the two tacrolimus formulations. RESULTS A two‐compartment model with Erlang absorption (n= 3) and first‐order elimination best described the data. In Advagraf® patients, a bimodal distribution was observed for the absorption rate constant (Ktr): one group with a Ktr similar to that of Prograf® treated patients and the other group with a slower absorption. A mixture model for Ktr was tested to describe this bimodal distribution. However, the data were best described by the nonmixture model including covariates (cytochrome P450 3A5, haematocrit and drug formulation). Using this model and tacrolimus concentrations measured at 0, 1 and 3 h post‐dose, the Bayesian estimator could estimate tacrolimus AUC accurately (bias = 0.1%) and with good precision (8.6%). CONCLUSIONS The single Bayesian estimator developed yields good predictive performance for estimation of individual tacrolimus inter‐dose AUC in Prograf® and Advagraf® treated patients and is suitable for clinical practice.

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.

Aims The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient. Methods Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. Results A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h−1, clearance was 23.0 l h–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: Dose0.25em()mg=2220.25emng0.25emnormalh0.25emml–1*0.5em22.50.25emnormall0.25emnormalh–1*[](),1.0if0.25emCYP3normalA5*3/*30.25emor0.25em(),1.62if0.25emCYP3normalA5*1/*30.25emor0.25emCYP3normalA5*1/*1*[](),1.0if0.25emCYP3normalA4*10.25emor unknown0.25emor0.25em(),0.814if0.25emCYP3normalA4*22*Age56−0.50*BSA1.930.72/1000 Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model. Conclusions For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

Because of increasing antimicrobial resistance and the shortage of new antibiotics, there is a growing need to optimize the use of old and new antibiotics. Modelling of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics of antibiotics can support the optimization of dosing regimens. Antimicrobial efficacy is determined by susceptibility of the drug to the microorganism and exposure to the drug, which relies on the PK and the dose. Population PK models describe relationships between patients characteristics and drug exposure. This article highlights three clinical applications of these models applied to antibiotics: 1) dosing evaluation of old antibiotics, 2) setting clinical breakpoints and 3) dosing individualization using therapeutic drug monitoring (TDM). For each clinical application, challenges regarding interpretation are discussed. An important challenge is to improve the understanding of the interpretation of modelling results for good implementation of the dosing recommendations, clinical breakpoints and TDM advices. Therefore, also background information on PK/PD principles and approaches to analyse PK/PD data are provided.