A physiologically based pharmacokinetic model of voriconazole in human CNS—Integrating time-dependent inhibition of CYP3A4, genetic polymorphisms of CYP2C19 and possible transporter mechanisms

Abstract

Objectives

Voriconazole is a classical antifungal drug that is often used to treat CNS fungal infections due to its permeability through the BBB. However, its clinical use remains challenging because of its narrow therapeutic window and wide inter-individual variability. In this study, we proposed an optimised and validated PBPK model by integrating in vitro, in vivo and clinical data to simulate the distribution and PK process of voriconazole in the CNS, providing guidance for clinical individualised treatment.

Methods

The model structure was optimised and tissue-to-plasma partition coefficients were obtained through animal experiments. Using the allometric relationships, the distribution of voriconazole in the human CNS was predicted. The model integrated factors affecting inter-individual variation and drug interactions of voriconazole—polymorphisms in the CYP2C19 gene and auto-inhibition and then was validated using real clinical data.

Results

The overall AFE value showing model predicted differences was 1.1420 in the healthy population; and in the first prediction of plasma and CSF in actual clinical patients, 89.5% of the values were within the 2-fold error interval, indicating good predictive performance of the model. The bioavailability of voriconazole varied at different doses (39%-86%), and the optimised model conformed to this pattern (46%–83%).

Conclusions

Combined with the relevant pharmacodynamic indexes, the PBPK model provides a feasible way for precise medication in patients with CNS infection and improve the treatment effect and prognosis.