Benefit-risk assessment based on number needed to treat and number needed to harm: Atogepant vs. calcitonin gene–related peptide monoclonal antibodies

Abstract

Antibodies; Gepant; Migraine disorders

Anticuerpos; Gepant; Trastornos de migraña

Anticossos; Gepant; Trastorns de migranya

Background To evaluate the benefit-risk assessment of atogepant and calcitonin gene–related peptide (CGRP) monoclonal antibodies (mAbs) vs. placebo based on the number needed to treat (NNT) and the number needed to harm (NNH) in a blended episodic migraine and chronic migraine (EM + CM) population. Methods The NNT was calculated based on achievement of a ≥ 50% reduction in mean monthly migraine days (MMDs) from baseline across 12 weeks. The NNH was calculated using the proportion of participants reporting a discontinuation due to adverse events (AEs). The primary analysis included data from studies of atogepant, erenumab, galcanezumab, eptinezumab and fremanezumab. Results In the primary analysis, the calculated NNT for atogepant 60 mg vs. placebo was 4.2 (95% credible interval (CrI) = 3.1–6.7), which was the lowest relative to the CGRP mAbs in the blended EM + CM population. Participants who received atogepant 60 mg or fremanezumab showed the most favorable NNH values (−1010 (95% Crl = 44 to ∞ to number needed to benefit 80) for atogepant) resulting from lower rates of discontinuation due to AEs compared with those receiving placebo. Conclusions Atogepant demonstrated a favorable benefit-risk profile, with NNT and NNH values comparable (not statistically significant) with those of CGRP mAbs across all analyses.

The authors disclosed receipt of the following financial support for the research, authorship, and publication of this article: This study was sponsored and funded by AbbVie. Medical writing support was provided to the authors by Sara Nathan, PhD, of Peloton Advantage, LLC, an OPEN Health company, and was funded by AbbVie. Support for the study design and implementation was provided by James Gahn and David Rowe of Medical Decision Modeling Inc., and was funded by AbbVie.