Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide...

Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene-related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of more diverse and heterogeneous patient populations, who often have higher disease burden and more comorbidities. Therefore, postmarketing safety surveillance is warranted. Regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, applying the same requirements as for spontaneous reports. Real-world data from social media platforms constitute a potential venue to capture diverse patient experiences and help detect treatment-related adverse events. However, while social media holds promise for this purpose, its use in pharmacovigilance is still in its early stages. Computational linguistics, which involves the automatic manipulation and quantitative analysis of oral or written language, offers a potential method for exploring this content.

This study aims to characterize adverse events related to monoclonal antibodies targeting CGRP signaling on Reddit, a large online social media forum, by using computational linguistics.

We examined differences in word frequencies from medication-related posts on the Reddit subforum r/Migraine over a 10-year period (2010-2020) using computational linguistics. The study had 2 phases: a validation phase and an application phase. In the validation phase, we compared posts about propranolol and topiramate, as well as posts about each medication against randomly selected posts, to identify known and expected adverse events. In the application phase, we analyzed posts discussing 2 monoclonal antibodies targeting CGRP signaling-erenumab and fremanezumab-to identify potential adverse events for these medications.

From 22,467 Reddit r/Migraine posts, we extracted 402 (2%) propranolol posts, 1423 (6.33%) topiramate posts, 468 (2.08%) erenumab posts, and 73 (0.32%) fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events, for example, "appetite," "weight," "taste," "foggy," "forgetful," and "dizziness." Comparing erenumab against a random selection of terms identified "constipation" as a recurring keyword. Comparing erenumab against fremanezumab identified "constipation," "depression," "vomiting," and "muscle" as keywords. No adverse events were identified for fremanezumab.

The validation phase of our study accurately identified common adverse events for oral migraine preventive medications. For example, typical adverse events such as "appetite" and "dizziness" were mentioned in posts about topiramate. When we applied this methodology to monoclonal antibodies targeting CGRP or its receptor-fremanezumab and erenumab, respectively-we found no definite adverse events for fremanezumab. However, notable flagged words for erenumab included "constipation," "depression," and "vomiting." In conclusion, computational linguistics applied to social media may help identify potential adverse events for novel therapeutics. While social media data show promise for pharmacovigilance, further work is needed to improve its reliability and usability.