The extent of continuance of melatonin therapy initiated in pre-pubertal children with chronic sleep onset insomnia (CSOI) was investigated in young adult life. Sleep timing, sleep quality, adverse events, reasons for cessation of therapy, and patient characteristics with regard to therapy regimen, chronotype and lifestyle factors possibly influencing sleeping behavior were assessed. With an online survey using questionnaires (Pittsburgh Sleep Quality Index, Insomnia Severity Index, Morningness-Eveningness Questionnaire, and Munich Chronotype Questionnaire), outcomes were measured and compared with age-related controls. These controls were extracted from published epidemiological research programs applying the same questionnaires. At the moment of the survey, melatonin was still continued by 27.3% of the patients, with a mean treatment duration of 10.8 years. The overall average treatment duration was 7.1 years. Sleep quality of both discontinued and persistent melatonin users did not deviate from controls. Sleep timing and chronotype scores indicated evening type preference in all responders. Adverse events were scarce but the perceived timing of pubertal development suggested a tendency towards delayed puberty in former and current users of melatonin. This study may underestimate the number of children that are able to stop using melatonin due to the response rate (47.8%) and appeal for continuing users. Sleep timing parameters were based on self-reported estimates. Control populations were predominantly students and were of varying nationalities. The statistical power of this study is low due to the limited sample size. Melatonin therapy sustained for 7.1 years does not result in substantial deviations of sleep quality as compared to controls and appears to be safe. The evening type preference suggests a causal relation with CSOI. This study shows that ten years after initiation of treatment with melatonin for CSOI, approximately 75% of the patients will have normal sleep quality without medication.

Aims: Immunosuppressant and kidney function monitoring are crucial for kidney transplant recipient follow‐up. Microsamples enable remote sampling and minimise patient burden as compared to conventional venous sampling at the clinic. We developed a liquid chromatography–tandem mass spectrometry assay to quantify tacrolimus, mycophenolic acid (MPA), creatinine and iohexol in dried blood spot (DBS), and volumetric absorptive microsample (VAMS) samples.Methods: The assay was successfully validated analytically for all analytes. Clinical validation was conducted by direct comparison of paired DBS, VAMS and venous reference samples from 25 kidney transplant recipients. Patients received iohexol 5–15 minutes before immunosuppressant intake and were sampled 0, 1, 2 and 3 hours thereafter, enabling tacrolimus and MPA area under the concentration–time curve (AUC) and creatinine‐based and iohexol‐based glomerular filtration rate (GFR) estimation. Method agreement was evaluated using Passing–Bablok regression, Bland–Altman analysis and the percentages of values within 15–30% of the reference (P₁₅–P₃₀) with a P₂₀acceptance threshold of 80%.Results: For DBS samples, method agreement was excellent for tacrolimus trough concentrations (n = 25, P₁₅ = 92.0%) and AUCs (n = 25; P₂₀ = 95.8%) and adequate for creatinine‐based GFR trend monitoring (n = 25; P₂₀ = 80%). DBS‐based MPA AUC assessment showed suboptimal agreement (n = 16; P₂₀ = 68.8%), but was considered acceptable given its P₃₀of 100%. The assay performed inadequately for DBS‐based iohexol GFR determination (n = 24; P₂₀ = 75%). The VAMS technique generally showed inferior performance, but can be considered for certain situations.Conclusion: The assay was successfully validated for tacrolimus, MPA and creatinine quantification in DBS samples, enabling simultaneous remote kidney function trend monitoring and immunosuppressant therapeutic drug monitoring in kidney transplant recipients.

The current coronavirus disease 2019 (COVID-19) pandemic requires extra attention for immunocompromised patients, including solid organ transplant recipients. We report on a case of a 35-year-old renal transplant recipient who suffered from a severe COVID-19 pneumonia. The clinical course was complicated by extreme overexposure to the mammalian target of rapamycin inhibitor everolimus, following coadministration of chloroquine and lopinavir/ritonavir therapy. The case is illustrative for dilemmas that transplant professionals may face in the absence of evidence-based COVID-19 therapy and concurrent pressure for exploration of experimental pharmacological treatment options. However, the risk-benefit balance of experimental or off-label therapy may be weighed differently in organ transplant recipients than in otherwise healthy COVID-19 patients, owing to their immunocompromised status and potential drug interactions with immunosuppressive therapy. With this case report, we aimed to achieve increased awareness and improved management of drug-drug interactions associated with the various treatment options for COVID-19 in renal transplant patients.

Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.

Background and Objective The immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring, typically based on whole-blood trough concentrations (C₀). Unfortunately, therapeutic drug monitoring target attainment rates are often unsatisfactory and patients with on-target exposure may still develop organ rejection. As everolimus displays erythrocyte partitioning, haematocrit-normalised whole-blood exposure has been suggested as a more informative therapeutic drug monitoring marker. Furthermore, model-informed precision dosing has introduced options for more sophisticated dose adaptation. We have previously developed a mechanistic population pharmacokinetic model, which described everolimus plasma pharmacokinetics and enabled estimation of haematocrit-normalised whole-blood exposure. Here, we externally evaluated this model for its utility for model-informed precision dosing. Methods The retrospective dataset included 4123 pharmacokinetic observations from routine clinical therapeutic drug monitoring in 173 renal transplant recipients. Model appropriateness was confirmed with a visual predictive check. A fit-for-purpose analysis was conducted to evaluate whether the model accurately and precisely predicted a future C₀ or area under the concentration–time curve (AUC) from prior pharmacokinetic observations. Bias and imprecision were expressed as the mean percentage prediction error (MPPE) and mean absolute percentage prediction error (MAPE), stratified on 6 months post-transplant. Additionally, we compared dose adaptation recommendations of conventional C₀-based therapeutic drug monitoring and C₀- or AUC-based model-informed precision dosing, and assessed the percentage of differences between observed and haematocrit-normalised C₀ (∆C₀) and AUC (∆AUC) exceeding ± 20%. Results The model showed adequate accuracy and precision for C₀ and AUC prediction at ≤ 6 months (MPPE_C0: 8.1 ± 2.5%, MAPE_C0: 26.8 ± 2.1%; MPPE_AUC: − 9.7 ± 5.1%, MAPE_AUC: 13.3 ± 3.9%) and > 6 months post-transplant (MPPE_C0: 4.7 ± 2.0%, MAPE_C0: 25.4 ± 1.4%; MPPE_AUC: − 0.13 ± 4.8%, MAPE_AUC: 13.3 ± 2.8%). On average, dose adaptation recommendations derived from C₀-based and AUC-based model-informed precision dosing were 2.91 ± 0.01% and 13.7 ± 0.18% lower than for conventional C₀-based therapeutic drug monitoring at ≤ 6 months, and 0.93 ± 0.01% and 3.14 ± 0.04% lower at > 6 months post-transplant. The ∆C₀ and ∆AUC exceeded ± 20% on 13.6% and 14.3% of occasions, respectively. Conclusions We demonstrated that our population pharmacokinetic model was able to accurately and precisely predict future everolimus exposure from prior pharmacokinetic measurements. In addition, we illustrated the potential added value of performing everolimus therapeutic drug monitoring with haematocrit-normalised whole-blood concentrations. Our results provide reassurance to implement this methodology in clinical practice for further evaluation.