A Hybrid Model Associating Population Pharmacokinetics with Machine Learning: A Case Study with Iohexol Clearance Estimation.

Background: Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms.

Objective: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation.

Comparison of Tacrolimus Starting Doses Based on CYP3A5 Phenotype or Genotype in Kidney Transplant Recipients.

Background: Selection of expected phenotypes (ie, expressers/non-expressers) is currently used in CYP3A5*3 genotype-based tacrolimus dosing. The authors assessed whether a dosing regimen based on the 3 CYP3A5 genotypes may reduce the occurrence of inadequate exposure.

Methods: Tacrolimus whole blood trough levels () were retrieved from a retrospective cohort of 100 kidney transplant recipients treated with a starting dose of 0.

Changes in the metabolic composition of storage solution with prolonged cold ischemia of the uterus.

Introduction: The development of uterine transplantation (UTx) from deceased donors requires knowledge of the tolerance of the uterus to prolonged cold ischemia (CI). This can be evaluated through the use of biological parameters to assess degradation of the organ between its procurement and transplantation. The objective of this study was to analyze changes in the metabolic composition of the storage solution in cases of prolonged CI in uteri from ewes.

Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined H NMR and GC-MS approach.

Correction for 'Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined H NMR and GC-MS approach' by Kienana Muhrez et al., Mol. BioSyst.