Abstract

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and GLP-1/glucose-dependent insulinotropic polypeptide (GIP) dual agonists have revolutionized the treatment of type 2 diabetes and obesity. The rapid growth in public interest, off-label use, and the emergence of counterfeit drugs underscores the need for timely monitoring of information demand. Objective: The objective of this study is to quantitatively characterize the temporal dynamics, market concentration, seasonality, and semantic structure of Russian-language search queries regarding GLP-1RAs and GLP-1/GIP dual agonists and to assess their correlation with pharmaceutical demand. Materials and Methods: This was a retrospective study of Yandex.Wordstat data from March 2018 to March 2026 (covering 97 months, 27 INNs and brand names). Time series analysis (trends, structural breaks, Seasonal-Trend decomposition based on Loess (STL decomposition)), calculation of the Herfindahl–Hirschman Index (HHI), and semantic analysis of 4562 unique formulations (bigrams, trigrams, Term Frequency–Inverse Document Frequency (TF-IDF), thematic classification, morphological normalization) were performed. Validation was conducted using DSM Group pharmacy sales data. Results: A total of 46.05 million queries were analyzed. Interest in semaglutide increased 215-fold, with the structural break point identified in January 2021. The HHI decreased from 0.311 (indicating a highly concentrated market) to 0.141 (indicating a competitive market). The share of diabetes-related queries did not exceed 0.46%, while the share of weight-loss-related queries reached 13.91%, and the share of commercial-component queries reached 41.1%. Four semantic signatures were identified: brand-dominant (Ozempic), instruction-targeted (Saxenda), dose-commercial (Tirzetta), and instruction-commercial (Trulicity). No statistically significant seasonality was confirmed after adjustment for multiple comparisons. The correlation between search interest and pharmacy sales was the strongest for Tirzetta (r = 0.976; n = 8; p < 0.001) and remained significant after trend removal (first differences: r = 0.819; p = 0.024). Conclusions: Yandex.Wordstat data provide a valuable supplementary source for digital pharmacoepidemiology. A systematic discrepancy was found between registered indications and actual information demand, a finding that has significant implications for pharmacovigilance and healthcare planning.

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