Debra van Asten (1, 2), Frédéric Gaspar (1,3,4), Anne Ravix (1,3,4), Jean Terrier (5,6,7), Pauline Gosselin (5,6), Pierre Fontana (6,8) ,Youssef Daali (6,7), Jean- Luc Reny (5,6), Chantal Csajka(1,3,4*), Monia Guidi (1,4,9*)
(1) Centre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. (2) Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands. (3) School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland. (4) Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Switzerland. (5) Division of General Internal Medicine, Geneva University Hospitals, Geneva, Switzerland. (6) Geneva Platelet Group, Faculty of Medicine, University of Geneva, Geneva, Switzerland. (7) Division of Clinical Pharmacology and Toxicology, Anesthesiology, Pharmacology, Intensive Care, and Emergency Medicine Department, Geneva University Hospitals, Geneva, Switzerland. (8) Division of Angiology and Haemostasis, Geneva University Hospitals, Geneva, Switzerland. (9) Service of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. (*) Equal contribution
Objectives:
Ticagrelor (reversible P2Y12-receptor antagonist) in combination with aspirin is a well-established treatment of acute coronary syndromes (ACS). Approximately 30-40% of a ticagrelor dose is metabolized by cytochrome P450 (CYP) 3A4 and, to a lesser extent, 3A5 into its active and equipotent metabolite, AR-C124910XX [1,2]. Previous publications in small patient populations and healthy volunteers showed substantial variability in the pharmacokinetics (PK) of ticagrelor and AR-C124910XX. However, studies aiming at characterizing the drug and metabolite PK profiles and potential sources of variability are lacking in a real-world patient population [3,4]. The aim of this research was to characterize ticagrelor and AR-C124910XX dried bloodspot (DBS) PK profiles in real-world hospitalized patients enrolled in the OptimAT study [5], and to identify sources of variability using a population PK (popPK)-modelling approach.
Methods:
The popPK analysis was performed based on ticagrelor and AR-C124910XX rich DBS data collected at the Geneva University Hospitals, as part of the OptimAT study. Population PK modelling was performed in Monolix (version 2021) using the SAEM algorithm [6]. First, a popPK model was developed for ticagrelor to best describe drug concentration-time profile and associated variability comparing structural models with up to three compartments in a sequential approach. Subsequently, different absorption models were tested to accurately capture the drug absorption phase. Second, the AR-C124910XX data were included in the ticagrelor model assuming linear and irreversible biotransformation between the parent drug to the metabolite and comparing several structural models (up to two compartments). Due to parameter identifiability the central volume of distribution (Vd) of the metabolite was fixed to 1. Third, the effect of covariates, including body weight, age, sex, obesity tobacco use, P-gp and CYP3A4/5 phenotypes were investigated.
Results:
All included patients (n=103) received a dose of 90 mg ticagrelor in the morning and reached steady state prior to the start of the study. In total 811 DBS concentrations were available for the analysis. Modelling results indicated a 2-compartmental model structure with zero-order absorption and a lag-time best characterized ticagrelor PK, with additional 2 compartments for the metabolite. The final population PK-model included a negative relationship between clearance (CL) of the metabolite and age, which allowed a reduction of 8.4% on the parameter inter-individual variability (IIV). No significant associations could be identified between PK model parameters and other available covariates, including CYP3A4/5. Furthermore, covariance between the parent Vd and CL parameter was included. The parameter estimates (including IIV), of the final model were (IIV, CV%); total CL of the parent of 18.9 L/h (45%) and of the metabolite 21.6 L/h (36%); Vd of the central ticagrelor compartment of 118 L (57%); Vd of the peripheral compartment of the parent of 326 L (125%) and of the metabolite of 41.6 L (119%); duration of the zero-order absorption of 1.1 h (65%); lag time of 0.34 h (66%). In the final model an increase of 10-years in age produces a 15% relative decrease (3.2 L/h decrease) in CL of the metabolite.
Conclusions:
The ticagrelor/AR-C124910XX popPK model developed could accurately characterize DBS data collected in a hospitalized patient population. This study confirmed the substantial variability in the PK profile of ticagrelor and its metabolite in DBS samples as reported in previous plasma sample studies. In contrast to other ticagrelor and AR-C124910XX population PK articles, our study found a significant relationship between CL of the metabolite and age. The clinical relevance of the large IIV and age effect is currently unknown due to the absence of a reference range for ticagrelor and its metabolite. Hence, further research is needed to confirm the influence and study the significance of the large IIV and the relationship between CL of the metabolite and age.
References:
[1] Teng, R. et al. Absorption, distribution, metabolism, and excretion of ticagrelor in healthy subjects. Drug Metab Dispos 38, 1514–1521 (2010).
[2] Fitchett, D. H. et al. Assessment and Management of Acute Coronary Syndromes (ACS): A Canadian Perspective on Current Guideline-Recommended Treatment-Part 1: Non-ST-Segment Elevation ACS. (2011)
[3] Dobesh, P. P. & Oestreich, J. H. Ticagrelor: Pharmacokinetics, pharmacodynamics, clinical efficacy, and safety. Pharmacotherapy 34, 1077–1090 (2014).
[4] Åstrand, M. et al. Pharmacokinetic-pharmacodynamic modelling of platelet response to ticagrelor in stable coronary artery disease and prior myocardial infarction patients Correspondence. British Journal of Clinical Pharmacology Br J Clin Pharmacol 85, 413 (2019).
[5] Antithrombotics’ Therapeutic Optimization in Hospitalized Patients Using Physiologically- and Population-based Pharmacokinetic Modeling. https://classic.clinicaltrials.gov/ct2/show/NCT03477331.
[6] Monolix version 2021 R1 (Lixoft SAS, Antony, France). https://lixoft.com/products/monolix/.
Reference: PAGE 32 (2024) Abstr 11184 [www.page-meeting.org/?abstract=11184]
Poster: Drug/Disease Modelling - Other Topics