A Theoretical Physiologically‐Based Pharmacokinetic Approach to Ascertain Covariates Explaining the Large Interpatient Variability in Tacrolimus Disposition

Abstract

Physiologically‐based pharmacokinetic ( PBPK ) modeling allows assessment of the covariates contributing to the large pharmacokinetic ( PK ) variability of tacrolimus; these include multiple physiological and biochemical differences among patients. A PBPK model of tacrolimus was developed, including a virtual population with physiological parameter distributions reflecting renal transplant patients. The ratios of predicted to observed dose‐normalized maximum plasma concentration (C max ), 0–12‐hour area under the concentration–time curve ( AUC 0–12 hour ), and trough plasma concentration (C trough ) ranged from 0.92‐fold to 1.15‐fold, indicating good predictive performance. The model quantitatively indicated the impact of cytochrome P450 ( CYP )3A4 abundance, hematocrit, and serum albumin levels, in addition to CYP 3A5 genotype status, on tacrolimus PK and associated variability. Age‐dependent change in tacrolimus trough concentration in pediatric patients was mainly attributed to the CYP 3A ontogeny profile. This study demonstrates the utility of PBPK modeling as a tool for mechanistic and quantitative assessment of the impact of patient physiological differences on observed large PK variability.

Utilizing Pediatric Physiologically Based Pharmacokinetic Models to Examine Factors That Contribute to Methadone Pharmacokinetic Variability in Neonatal Abstinence Syndrome Patients

“…The adult fraction unbound was scaled to a neonatal level using the method proposed by McNamara and Alcorn . The blood‐to‐plasma ratio in neonates was estimated based on the fixed adult erythrocyte‐to‐plasma ratio and neonatal hematocrit values (), as described in the Supplementary Material S1 of Emoto et al These parameters are summarized in Table . The pharmacokinetic profiles of R‐ and S‐methadone were simulated according to the actual dosing regimen for each of the 20 full‐term newborns with no known compromising illnesses treated for NAS, and then the simulated results were compared to the time‐course concentration data observed in the PK study in newborns with NAS .…”

Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients

“…TAC plasma binding is defined as the unbound plasma fraction fu p , which was 0.012 34 , despite some variability. This value is commonly used in other modelling studies 21,35 . An exploratory Monte Carlo simulation was performed to confirm the model's robustness.…”

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically‐based pharmacokinetic modelling approach

“…The interactions between Schisandra lignans and both midazolam and tacrolimus in Chinese people were predicted with excellent accuracy (Figure 2, Table 3). The simulation results implied that the PBPK model for tacrolimus, which was originally developed in European people, 57 could seamlessly be extrapolated to a Chinese population relying on the differences in body size, amount of CYP3A enzymes and frequency of CYP3A5 expression in the populations 68 . Conversely, the elimination components for the default PBPK model of midazolam in the Simcyp Simulator had to be adjusted due to a far lower CL/F of midazolam in the associated clinical interaction study, compared to previously reported values 58 .…”

“…PBPK models for perpetrator and victim compounds were linked through competitive inhibition, MBI and induction of hepatic and intestinal CYP3A enzymes as described previously 48 . The PBPK model of tacrolimus based on clinical pharmacokinetic data in people from European ancestry 57 was utilised (Table S1). The default PBPK model of midazolam within the Simcyp Simulator was initially used for PBPK prediction of the interaction with S. sphenanthera.…”