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: urn:x-wiley:03065251:media:bcp13838:bcp13838-math-0001 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.

Background Multiple clinical, demographic, and genetic factors affect the pharmacokinetics of tacrolimus in children, yet in daily practice, a uniform body-weight based starting dose is used. It can take weeks to reach the target tacrolimus pre-dose concentration. Objectives The objectives of this study were to determine the pharmacokinetics of tacrolimus immediately after kidney transplantation and to find relevant parameters for dose individualization using a population pharmacokinetic analysis. Methods A total of 722 blood samples were collected from 46 children treated with tacrolimus over the first 6 weeks after renal transplantation. Non-linear mixed-effects modeling (NONMEM^®) was used to develop a population pharmacokinetic model and perform a covariate analysis. Simulations were performed to determine the optimal starting dose and to develop dosing guidelines. Results The data were accurately described by a two-compartment model with allometric scaling for bodyweight. Mean tacrolimus apparent clearance was 50.5 L/h, with an inter-patient variability of 25%. Higher bodyweight, lower estimated glomerular filtration rate, and higher hematocrit levels resulted in lower total tacrolimus clearance. Cytochrome P450 3A5 expressers and recipients who received a kidney from a deceased donor had a significantly higher tacrolimus clearance. The model was successfully externally validated. In total, these covariates explained 41% of the variability in clearance. From the significant covariates, the cytochrome P450 3A5 genotype, bodyweight, and donor type were useful to adjust the starting dose to reach the target pre-dose concentration. Dosing guidelines range from 0.27 to 1.33 mg/kg/day. Conclusion During the first 6 weeks after transplantation, the tacrolimus weight-normalized starting dose should be higher in pediatric kidney transplant recipients with a lower bodyweight, those who express the cytochrome P450 3A5 genotype, and those who receive a kidney from a deceased donor.

Background: Bodyweight-based dosing of tacrolimus (Tac) is considered standard care, even though the available evidence is thin. An increasing proportion of transplant recipients is overweight, prompting the question if the starting dose should always be based on bodyweight. Methods: For this analysis, data were used from a randomized-controlled trial in which patients received either a standard Tac starting dose or a dose that was based on CYP3A5 genotype. The hypothesis was that overweight patients would have Tac overexposure following standard bodyweight-based dosing. Results: Data were available for 203 kidney transplant recipients, with a median body mass index (BMI) of 25.6 (range, 17.2-42.2). More than 50% of the overweight or obese patients had a Tac predose concentration above the target range. The CYP3A5 nonexpressers tended to be above target when they weighed more than 67.5 kg or had a BMI of 24.5 or higher. Dosing guidelines were proposed with a decrease up to 40% in Tac starting doses for different BMI groups. The dosing guideline for patients with an unknown genotype was validated using the fixed-dose versus concentration controlled data set. Conclusions: This study demonstrates that dosing Tac solely on bodyweight results in overexposure in more than half of overweight or obese patients.

Starting doses of tacrolimus and ciclosporin are typically chosen on a calculated mg/kg bodyweight basis. After initiation of treatment, doses are adjusted with therapeutic drug monitoring (TDM). This trial-and-error approach has been accepted by most physicians and pharmacists involved in the care of transplanted patients. Dosing algorithms have only fairly recently been proposed to better individualize the starting dose. This review provides an overview of all the currently available dosing algorithms in adult and children for the starting dose of ciclosporin, tacrolimus and mycophenolic acid. In these algorithms, multiple other covariates influencing the starting dose, such as age, hematocrit, comedication and genotype are taken into account. After selecting the starting dose with an algorithm and after initiation of treatment, TDM will, however, remain necessary. Whether or not implementation of such algorithms will improve clinical outcome remains to be demonstrated. First of all an algorithm needs to be validated, against an independent dataset. Second, in a prospective study the algorithm should prove to reduce the time to reach the target concentration, and to reduce the number of patients with drug concentrations (far) outside the therapeutic window. Finally, a clinical trial demonstrating a benefit on clinical outcome will be crucial in achieving broad acceptance of calculating starting dose using individualized dosing algorithms.

Fibroblast activation protein (FAP) could be a promising target for tumor imaging and therapy, as it is expressed in >90% of epithelial cancers. A high level of FAP-expression might be associated with worse prognosis in several cancer types, including lung cancer. FAPI binds this protein and allows for labelling to Gallium-68, as well as several therapeutic radiopharmaceuticals. As FAP is only expressed at insignificant levels in adult normal tissue, FAPI provides a highly specific tumor-marker for many epithelial cancers. In this review, current information on the use of [⁶⁸Ga]Ga-FAPI PET/CT in lung cancer is presented. [⁶⁸Ga]Ga-FAPI shows a high uptake (standardized uptake value = SUV_max) and tumor-to-background ratio (TBR) in primary lung cancer lesions, as well as in metastatic lesions of other tumor types located in the lung and in lung cancer metastases located throughout the body. Where a comparison was made to [¹⁸F]FDG PET/CT, [⁶⁸Ga]Ga-FAPI showed a similar or higher SUV_max and TBR. In brain and bone metastases, [⁶⁸Ga]Ga-FAPI PET/CT outperformed [¹⁸F]FDG PET/CT. In addition to this strong diagnostic performance, a possible prognostic value of [⁶⁸Ga]Ga-FAPI PET/CT in lung cancer is proposed.