Real-world population pharmacokinetic modeling of elexacaftor, tezacaftor, and ivacaftor

Why clinicians should know about it

• Picked for Genetics (clinical) (top studies of the week, 2 August 2026).

Abstract

BACKGROUND: Cystic fibrosis transmembrane conductance regulator modulators have revolutionized therapy for people with cystic fibrosis, yet marked interindividual variability in drug exposure limits targeted individualized dosing. We aimed to characterize the pharmacokinetics of elexacaftor, tezacaftor, and ivacaftor and develop population pharmacokinetic models that enable model-informed precision dosing in adults with cystic fibrosis. METHODS: In this retrospective analysis of real-world therapeutic drug monitoring data from 176 adults (62.5% male; median age 36 years; median body-mass index 22.5 kg/m²), nonlinear mixed-effects modeling was used to fit one-compartment models with first-order absorption and elimination for each substance. The developed model was employed to evaluate real-world target attainment and Monte Carlo simulations (n = 1000) were used to predict steady-state exposures. RESULTS: Graphical and numerical diagnostics supported adequate model fit. In covariate analyses, concomitant antifungal therapy was associated with a significant reduction in clearance, whereas higher albumin exerted a modest effect on reduction in clearance. Higher bilirubin and higher levels of vitamins D and E were likewise associated with lower clearance. Simulations revealed substantial interindividual variability. Target-attainment analysis identified a subset of patients with inadequate exposure CONCLUSIONS: Our population pharmacokinetic analysis of a large real-world cohort identified clinically relevant covariates driving variability in elexacaftor, tezacaftor, and ivacaftor exposure. These models complement previous population PK studies by identifying clinically accessible covariates associated with ETI exposure in a real-world adult CF cohort and may inform future personalized dosing approaches, pending external validation and prospective exposure-response evaluation.

Abstract as published, via PubMed.