Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists

TL;DR

The paper tackles the problem of incomplete post-approval safety profiles for GLP-1 receptor agonists by proposing a digital health pipeline that fuses social media, clinical literature, manufacturers’ reports, and ChatGPT. It employs an NLP-based ASE extraction workflow with ScispaCy, aligns findings to the SIDER database, and validates results against multiple data sources, complemented by an ASE-ASE network to uncover co-occurrence patterns. The study identifies 134 ASEs for GLP-1 RAs, including 21 novel ASEs observed uniquely in social media and demonstrates a 53% overlap with established ASEs from manufacturers and literature, supporting the method’s ability to reveal unreported safety signals. Four ASE clusters reveal distinct co-occurrence patterns (GI distress, emotional/psychological, somatic, neurological), highlighting potential mechanisms and implications for real-time pharmacovigilance and regulatory decision-making. Overall, the approach offers a scalable, data-driven framework for continuous drug safety monitoring that can be generalized to other therapies to enhance patient safety and public health outcomes.

Abstract

Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonists (GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by 2030. Using a Named Entity Recognition (NER) model, our method successfully detected 21 potential ASEs overlooked upon FDA approval, including irritability and numbness. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed drugs, leveraging cutting-edge AI-driven social media analytics. It can increase the safety of new drugs in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.

Figures (7)

• Figure 1: Overview of the methodology of this research.
• Figure 2: Venn diagram offering a visual representation of and distinction among ASEs.
• Figure 3: Overlap score as a function of $P_{f\%}$.
• Figure 5: ASE mention frequency ($>1$) on $\mathbb{X}$ and Reddit for each GLP-1 receptor agonist.
• Figure 6: ASEs mentions $> 10$ in each of the 14-day intervals on $\mathbb{X}$ and Reddit.