LAUSR

Objectives: This study aimed to investigate the pharmacokinetic characteristics of siponimod in healthy volunteers and patients with MS based on aggregated data from published clinical trials, and to explore the factors influencing siponimod exposure. Methods: A total of 476 siponimod plasma concentrations aggregated from 28 dosage groups (corresponding to 294 healthy volunteers and 207 patients with MS) were collected from published clinical trials. Population pharmacokinetic (PPK) analysis was performed using a nonlinear, mixed-effect modeling approach. The pharmacokinetic properties of siponimod in healthy volunteers and patients with MS were compared, and the influence of covariates on siponimod exposure was evaluated using both PPK analysis and noncompartmental analysis (NCA). Results: A one-compartment model with first-order absorption and elimination adequately described siponimod pharmacokinetics. The typical population parameter estimates of clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (ka) were 3.17 L/h, 112.70 L, and 0.38 h−1, respectively. An 11.85% lower siponimod clearance was estimated for patients with MS relative to healthy volunteers. Subgroup analyses using NCA assessments revealed that siponimod presented an accumulation index of approximately 2 after multiple administration. Compared with nonobese participants, obese participants had a relatively lower dose-corrected area under the concentration-time curve (AUC0-∞/D) (0.31 vs. 0.42 h/L) and V/F (120.95 vs. 133.75 L), and a relatively higher CL/F (3.25 vs. 3.21 L/h). Participants with CYP2C9*2/*3, *1/*3, and *3/*3 genotypes experienced an increased (1.3- and 3.4-fold, respectively) AUC0-∞/D and a decreased (0.7- and 0.3-fold, respectively) CL/F compared with those in participants with the CYP2C9*1/*1, *1*2, and *2*2 genotypes. Fluconazole combination led to a decrease in CL/F (approximately 0.5 times) and an increase in AUC0-∞/D (approximately 1.3 times). Conclusion: Siponimod pharmacokinetic properties in healthy volunteers and patients with MS were explored using complementary model-based meta-analysis (MBMA) and NCA approaches. A slightly lower siponimod clearance was observed in patients with MS than in healthy volunteers. The dosage regimen, body mass index, CYP2C9 genetic polymorphism and fluconazole combination may had influences on siponimod pharmacokinetics. Such model paves the road to more population-based analyses in different patient populations with MS to quantify the effect of any influencing factors on siponimod pharmacokinetics.