Background: Toll-like receptors (TLRs), a family of innate pattern-recognition receptors, have been exploited as a target for antitumor strategy. An increasing number of TLR agonists, serving as immunotherapeutics or vaccine adjuvants, were developed. This study aimed at exploring the status and trend of current researches on TLR agonists through bibliometric analysis. Methods: Original publications on TLR agonists were collected from the Web of Science Core Collection. Data were analyzed in terms of publication outputs, journals, countries, institutions, authors, co-authorship, co-citation, research hotspots, and evolution trends through VOSviewer and CiteSpace. Results: A total of 1914 TLR agonists-related articles, published in 612 academic journals between 2000 and 2019, were enrolled in the study. The Journal of Immunology published the most publications, followed by PLoS One and Blood . The USA that is in possession of the largest number of articles and the most extensive cooperators was the most leading country in this field. University of Minnesota ranked the first in terms of paper totality, but its average citations ranking was lower than University of Pennsylvania. Gudkov AV was the most productive author, whose team reported a TLR5 agonist that had radioprotective activity in mouse and primate models in 2008. The paper of Akira Shizuo, professor of Osaka University, was widely cited by international peers. The research trend of TLR agonists has undergone 3 periods: mechanisms of TLR signalings in immunotherapy (2000–2010), discovery of TLR agonists (2011–2014), application, therapeutic evaluation, and drug design of TLR agonists (2015–2019). Conclusion: This study provides investigators a landscape of TLR agonists research from the perspective of bibliometrics.

Background: In the emergent situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients more adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs to help health professionals and patients get rid of the risk.Objective: This pharmacovigilance study aimed to investigate the ADEs of some attractive drugs (i.e., “hot drugs” in this study) in COVID-19 prevention and treatment based on the data from the US Food and Drug Administration (FDA) adverse event reporting system (FAERS).Methods: The FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2022 were retrieved with hot drugs and frequent ADEs were recognized. A combination of support, lower bound of 95% confidence interval (CI) of the proportional reporting ratio (PRR) was applied to detect significant hot drug and ADE signals by the Python programming language on the Jupyter notebook.Results: A total of 66,879 COVID-19 associated cases were retrieved with 22 hot drugs and 1,109 frequent ADEs on the “preferred term” (PT) level. The algorithm finally produced 992 significant ADE signals on the PT level among which unexpected signals such as “hypofibrinogenemia” of tocilizumab and “disease recurrence” of nirmatrelvir\ritonavir stood out. A picture of signals on the “system organ class” (SOC) level was also provided for a comprehensive understanding of these ADEs.Conclusion: Data mining is a promising and efficient way to assist pharmacovigilance work, and the result of this study could help timely recognize ADEs in the prevention and treatment of COVID-19.

BACKGROUND In the emergency situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs so as to help health professionals and patients get rid of these risks. OBJECTIVE This pharmacovigilance study aimed to investigate the ADEs of “Hot Drugs” in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). METHODS FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2021 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, proportional reporting ratio (PRR) and Chi-square (2) test was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. RESULTS 13,178 COVID-19 cases were retrieved with 18 “Hot Drugs” and 312 frequent ADEs on “Preferred Term” (PT) level. 18  312 = 5,616 “Drug & ADE” candidates were formed for further data mining. The algorithm finally produced 219 significant ADE signals associated with 17 “Hot Drugs”and 124 ADEs.Some unexpected ADE signals were observed for chloroquine, ritonavir, tocilizumab, Oxford/AstraZeneca COVID-19 Vaccine and Moderna COVID-19 Vaccine. CONCLUSIONS Data mining is a promising and efficient way to assist pharmacovigilance work and the result of this paper could help timely recognize ADEs in the prevention and treatment of COVID-19.