Patient Characteristics Influencing Ciclosporin Pharmacokinetics and Accurate Bayesian Estimation of Ciclosporin Exposure in Heart, Lung and Kidney Transplant Patients

Abstract

Population pharmacokinetic analysis of ciclosporin microemulsion in allograft transplants resulted in the design of a new pharmacokinetic model for ciclosporin microemulsion, identification of significant covariates and the design of an accurate MAP-BE based on three blood concentrations and these covariates.

Impact of Tacrolimus Intraindividual Variability and CYP3A5 Genetic Polymorphism on Acute Rejection in Kidney Transplantation

“…2 Between-patient variability of tacrolimus, and cyclosporine, has been investigated in a number of studies. 3,4 The interindividual variability of calcineurin inhibitor concentrations has been shown to be an important risk factor for poor kidney allograft outcomes. 3,4 In contrast to the studies about interindividual variability, there have been a few studies on the intraindividual variability (IIV) of calcineurin inhibitor concentrations [5][6][7] and the mechanism of the impact of IIV on graft outcomes has not been well documented.…”

Ciclosporin Population Pharmacokinetics and Bayesian Estimation in Thoracic Transplant Recipients

“…Several ciclosporin population pharmacokinetic studies have been reported in patients with solid-organ transplantation [15][16][17][18][19][20][21] , but to our knowledge few have been published in heart [22;23] , lung or heart-lung transplant recipients [24] . Given the potential pharmacokinetic differences between populations, the pharmacokinetics of immunosuppressants may be different in thoracic and liver or kidney transplant recipients, and thus deserve to be specifically studied.…”

Forensic science meets clinical pharmacology: pharmacokinetic model based estimation of alcohol concentration of a defendant as requested by a local prosecutor's office

“…To ensure generalizability and to reflect parameter uncertainty, a non-parametric bootstrap analysis was applied to the Bayesian prediction. [4][5][6] Specifically, 1,000 different datasets were generated by random sampling with replacement from the original PK data used to build OAPKM. Repetitive model fittings were then performed 1,000 times using the OAPKM and each of 1,000 bootstrap datasets.…”