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A comprehensive mixed-method approach to characterize the source of diurnal tacrolimus exposure variability in children: Systematic review, meta-analysis, and application to an existing dataset.

Abbie D LeinoJohn C MageeDavid B KershawManjunath Amit P PaiJeong M Park
Published in: Journal of clinical pharmacology (2023)
Tacrolimus is widely reported to display diurnal variation in pharmacokinetic parameters with twice-daily dosing. However, the contribution of chronopharmacokinetics versus food intake is unclear, with even less evidence in the pediatric population. The objectives of this study were to summarize the existing literature by meta-analysis and evaluate the impact of food composition on 24-hour pharmacokinetics in pediatric kidney transplant recipients. For the meta-analysis, ten studies involving 253 individuals were included. The pooled effect sizes demonstrated significant differences in AUC 0-12 (SMD: 0.27, 95% CI: 0.03, 0.52) and C max (SMD: 0.75, 95% CI: 0.35, 1.15) between morning and evening dose administration. However, there was significant between-study heterogeneity which was explained by food exposure. The effect size for C min was not significantly different overall (SMD: -0.09, 95% CI: -0.27, 0.09) or across the food exposure subgroups. A two-compartment model with a lag time, linear clearance, and first-order absorption best characterized the tacrolimus pharmacokinetics in pediatric participants. As expected, adding the time of administration and food composition covariates reduced the unexplained within-subject variability for k a, but only caloric composition significantly reduced variability for T lag . The available data suggest food intake is the major driver of diurnal variation in tacrolimus exposure, but the associated changes are not reflected by trough concentrations alone. This article is protected by copyright. All rights reserved.
Keyphrases
  • systematic review
  • meta analyses
  • case control
  • human health
  • young adults
  • blood pressure
  • physical activity
  • machine learning
  • risk assessment
  • climate change
  • big data