Pharmacokinetic-Pharmacodynamic Modeling of the Ponesimod Effect on Heart Rate in Patients With Multiple Sclerosis.
Belén ValenzuelaItalo PoggesiNicolas LuyckxAndrea VaclavkovaJuan Jose Perez RuixoPublished in: Clinical pharmacology and therapeutics (2023)
The purpose of this study was to characterize the ponesimod effect on the heart rate (HR) in patients with multiple sclerosis (MS). A previous pharmacokinetic (PK) and pharmacodynamic model developed in healthy participants was updated using data from phase II and III trials conducted in patients with MS. Clinically relevant covariates were assessed. Simulations were conducted to evaluate the impact of the lack of adherence to ponesimod treatment and provide guidance in cases of treatment re-initiation. The maximal effect parameter of the PK/HR model was lower in patients with MS (23.5% decrease) compared with healthy volunteers (43.2%). The effect of patient covariates on PK/HR was similar to those identified in healthy participants and not clinically relevant in patients with MS. The population PK/HR model well characterized the effect of ponesimod on the time course of HR in patients with MS. After 2 weeks of treatment with 10 mg or higher doses, the model indicated full tolerance development. After repeated dosing at 20 mg, tolerance was maintained > 60% of the steady-state tolerance for up to 4 days after the last dose. Re-initiating with gradual uptitration is recommended if drug discontinuation lasts ≥ 4 days. This managed the negative chronotropic effects of ponesimod. No bradycardia events were observed within the first 2 weeks of treatment in patients with relapsing MS with a baseline HR > 55 bpm. This justifies the recommendation included in the human prescription drug labeling to monitor HR after the first ponesimod dose in these patients.
Keyphrases
- heart rate
- multiple sclerosis
- mass spectrometry
- ms ms
- blood pressure
- heart rate variability
- clinical trial
- phase ii
- emergency department
- end stage renal disease
- type diabetes
- endothelial cells
- rheumatoid arthritis
- newly diagnosed
- metabolic syndrome
- chronic kidney disease
- machine learning
- systemic lupus erythematosus
- insulin resistance
- skeletal muscle
- big data
- ejection fraction
- case report
- replacement therapy
- adverse drug