Modeling the Efficacy of Natalizumab in Multiple Sclerosis Patients Who Switch From Every‐4‐Week Dosing to Extended‐Interval Dosing. (19th September 2020) - Chang, Ih; Muralidharan, Kumar Kandadi; Campbell, Nolan; Ho, Pei‐Ran
Abstract: Natalizumab is approved for multiple sclerosis treatment at a dose of 300 mg every 4 weeks. Extended‐interval dosing of natalizumab has been proposed as a strategy to mitigate the risk of progressive multifocal leukoencephalopathy, but the efficacy of extended‐interval dosing is not established. Previous models suggesting lower efficacy when initiating natalizumab treatment with extended‐interval dosing rather than every‐4‐week dosing are inconsistent with reports from clinical observations and real‐world studies conducted in patient populations switching to extended‐interval dosing after a period of receiving natalizumab every 4 weeks. Here, the efficacy of natalizumab extended‐interval dosing was modeled specifically in patients switching from every‐4‐week dosing to extended‐interval dosing. Published population pharmacokinetic/pharmacodynamic models were used to simulate the distribution of alpha‐4 integrin saturations for different body weight categories and dosing intervals (every 5, 6, 7, 8, 10, or 12 weeks). Generalized estimating equations relating alpha‐4 integrin saturation to probability of multiple sclerosis lesion or relapse were derived from RESTORE trial data, which included patients (n = 175) who discontinued natalizumab after being treated every 4 weeks for ≥1 year and had no relapses in the year before discontinuation. The model‐based simulations described indicate that every‐5‐week or every‐6‐week dosing is likely to maintain the efficacy of natalizumab, particularly at body weights <80 kg, in patients who switch after a period of stability on every‐4‐week dosing. The efficacy of natalizumab decreases as dosing intervals and body weight increase. Partial model validation was achieved in that observed outcomes in an independent clinical study were similar to those predicted by the models.