Chih-hsuan Hsin (1), Uta Schilling (1), Atef Halabi (2), Jasper Dingemanse (1), Andreas Krause (1)
(1) Idorsia Pharmaceuticals Ltd, Department of Clinical Pharmacology, Hegenheimermattweg 91, 4123 Allschwil, Switzerland, (2) Clinical Research Services, Lornsenstraße 7, 24105 Kiel, Germany
Introduction:
Selatogrel is a potent, selective, and reversible P2Y12 receptor antagonist with rapid onset that can be self-administered subcutaneously (s.c.). Self-administration by the patient if symptoms of acute myocardial infarction (AMI) are suspected is a new clinical paradigm, aimed at gaining vital time to bridge the time to initiation of emergency procedures. Currently, a Phase 3 trial in patients with a recent history of AMI is ongoing (NCT04957719). Selatogrel is not metabolized by major CYP enzymes and mainly eliminated unchanged hepatically [1]. Since the liver plays an essential role in the pharmacokinetics (PK) of selatogrel and in haemostasis [2, 3], hepatic impairment might affect the inhibition of platelet aggregation while exposed to selatogrel.
Objectives:
This study used a pharmacometric modelling and simulation approach to describe and quantify the impact of mild and moderate hepatic impairment on PK and pharmacodynamics (PD) of a single dose of 16 mg s.c. selatogrel.
Methods:
Data from a prospective, open-label, single-centre, single-dose Phase 1 study in 24 subjects receiving a single s.c. dose of 16 mg selatogrel were evaluated. Eight subjects per group with mild and moderate hepatic impairment (Child-Pugh A and B) and 8 matched healthy subjects with normal liver function were included. Dense PK and PD data were collected over 72 h post-dose. Selatogrel plasma concentrations were quantified using liquid chromatography with tandem mass spectrometry. P2Y12 reaction units (PRU) were measured via the VerifyNow® assay (Accumetrics, San Diego, CA, USA), and the percentage of inhibition of platelet aggregation (%IPA) was derived from PRU as 100*((PRUbaseline – PRUt)/PRUbaseline).
A population PK/PD model was developed using Monolix 2020R1 [4] to assess the effect of hepatic impairment on PK/PD parameters and to evaluate the relationships between selatogrel exposure and response (%IPA). R version 4.0.4 [5] was used for dataset preparation, exploratory analyses, and visualization of results. In total, 368 PK and 222 PD observations were included. A base model was identified based on the corrected Bayesian information criterion (BICc) and standard model diagnostics. Subsequently, the effect of hepatic impairment on PK and PD parameters was explored and the resulting model was used to evaluate the time until IPA returned to baseline (defined as 10% IPA) by simulating a single 16 mg dose in 100 subjects per hepatic impairment group.
Results:
A linear two-compartment PK model with first-order absorption with a transit compartment coupled with an indirect PD effect described selatogrel plasma concentrations and %IPA well. The PK/PD model indicated notable differences in clearance (CL) and volume of distribution of the central compartment (Vc) between healthy subjects and subjects with mild or moderate hepatic impairment. Subjects with mild and moderate hepatic impairment were estimated to have a 32% and 66% lower CL and 18% and 50% lower Vc, respectively, than healthy subjects.
Furthermore, the model suggested that the steepness of the concentration-effect curve (Hill coefficient) and the half-maximum inhibitory concentration (IC50) were reduced with higher degree of hepatic impairment. The estimated IC50 values were 6.62, 3.38, and 0.20 ng/mL in healthy subjects and subjects with mild or moderate hepatic impairment, respectively. Based on the simulation results, the median estimated times to return to baseline were 22.0 h, 38.5 h, and 97.0 h in healthy subjects and subjects with mild and moderate hepatic impairment, respectively.
Conclusions:
Hepatic impairment influences both PK and PD of selatogrel and results in a prolonged inhibition of platelet aggregation.
References:
[1] Ufer M et al. Xenobiotica. 2020;50:427–34. doi:10.1080/00498254.2019.1646440.
[2] Lisman T et al. J Hepatol. 2002;37:280–7. doi:10.1016/s0168-8278(02)00199-x.
[3] Verbeeck RK. Eur J Clin Pharmacol. 2008;64:1147–61. doi:10.1007/s00228-008-0553-z.
[4] Monolix version 2020R1. Antony, France: Lixoft SAS, 2020. http://lixoft.com/products/monolix/
[5] R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Reference: PAGE 30 (2022) Abstr 10050 [www.page-meeting.org/?abstract=10050]
Poster: Drug/Disease Modelling - Other Topics