Development of a Physiologically Based Pharmacokinetic Model for Elexacaftor, Tezacaftor, and Ivacaftor in Pregnant Women with Cystic Fibrosis

Jiangfan Wu and Oliver Scherf-Clavel

Department of Pharmacy, Clinical Pharmacy and Pharmacotherapy, LMU Munich, Germany.

 

Introduction

Cystic fibrosis (CF) is an autosomal recessive genetic disease characterized by impaired chloride transport leading to thickened secretions and chronic respiratory and gastrointestinal complications [1, 2]. The combination of elexacaftor, tezacaftor, and ivacaftor (ETI) is extensively employed in the management of CF. With the advancements of CF management, addressing CF in pregnancy has emerged as an increasingly significant topic. During pregnancy, further physiological adaptations can profoundly impact drug dispositions. Due to the pharmacokinetic (PK) data in pregnant women being scarce from ethical and technical limitations, this study aimed to build a physiologically based pharmacokinetic (PBPK) model of ETI to predict the pharmacokinetics during pregnancy.

Methods

A PBPK model for ETI was developed using PK-Sim® (Version 12, part of the Open Systems Pharmacology Suite) to describe drug disposition in healthy individuals. Parameters were optimized with clinical data from published literature [3-6]. A goodness-of-fit plot and prediction error calculation were employed to evaluate model accuracy and precision. Pregnancy-specific physiological changes will be incorporated using MoBi® (Version 12) based on published data, including alterations in gastrointestinal absorption, hepatic metabolism, and transporter kinetics [7-9]. WebPlotDigitizer (Version 5.2, Automeris LLC, CA, USA) was used for data digitalization. The R programming language (Version 4.3.3) and RStudio (Version 2024.09.1) were employed for post-processing, visualizing concentration-time profiles, and calculating PK parameters.

Results

A whole body PBPK model for ETI was effectively developed utilizing data from single-dose clinical trials and subsequently used for predicting multiple-dose scenarios, with the predicted steady-state Cmax and AUC values deviating by less than 15% from the actual clinical averages. External validation with an independent dataset showed exceptional prediction accuracy, evidenced by a mean absolute percentage error (MAPE) and mean relative deviations (MRD). Visual predictive checks validated the model’s performance as satisfactory.

Conclusions

The verified PBPK models have demonstrated the ability to capture ETI pharmacokinetics in healthy subjects. Following these discoveries, an extension of the model to simulate pharmacokinetics during pregnancy is the planned next step. The model will function as a valuable tool for optimizing pharmacotherapy in pregnant individuals with CF and for improving safety assessments for those administered CFTR modulators.

References

(1) Elborn, J. S. Cystic fibrosis. Lancet 2016, 388 (10059), 2519-2531. DOI: 10.1016/s0140-6736(16)00576-6  From NLM.

(2) De Sutter, P. J.; Gasthuys, E.; Van Braeckel, E.; Schelstraete, P.; Van Biervliet, S.; Van Bocxlaer, J.; Vermeulen, A. Pharmacokinetics in Patients with Cystic Fibrosis: A Systematic Review of Data Published Between 1999 and 2019. Clin Pharmacokinet 2020, 59 (12), 1551-1573. DOI: 10.1007/s40262-020-00932-9  From NLM.

(3) US Food and Drug Administration, Clinical pharmacology and biopharmaceutics review(s), Ivacaftor. 2012.

(4) US Food and Drug Administration, Clinical Inspection Summary, Ivacaftor. 2012.

(5) US Food and Drug Administration, Clinical pharmacology and biopharmaceutics review(s), Tezacaftor/Ivacaftor. 2017.

(6) US Food and Drug Administration, NDA/BLA Multi-Disciplinary Review and Evaluation, elexacaftor/tezacaftor/ivacaftor. 2019.

(7) Dallmann, A.; Pfister, M.; van den Anker, J.; Eissing, T. Physiologically Based Pharmacokinetic Modeling in Pregnancy: A Systematic Review of Published Models. Clin Pharmacol Ther 2018, 104 (6), 1110-1124. DOI: 10.1002/cpt.1084  From NLM.

(8) Anderson, G. D. Pregnancy-induced changes in pharmacokinetics: a mechanistic-based approach. Clin Pharmacokinet 2005, 44 (10), 989-1008. DOI: 10.2165/00003088-200544100-00001  From NLM.

(9) Scherf-Clavel, M.; Leutritz, A. L.; Gehrmann, A.; Unterecker, S.; Walther, S.; Kittel-Schneider, S. Physiologically based pharmacokinetic modelling predicts altered maternal pharmacokinetics of amitriptyline during pregnancy. British Journal of Clinical Pharmacology 2025, 1-13. DOI: https://doi.org/10.1002/bcp.70084.