A Bibliometric Analysis of the HCV Drug-Resistant Majority and Minority Variants

In recent decades, research on Hepatitis C Virus (HCV) drug-resistant variants has expanded; however, critical gaps remain in our understanding of global contributions, emerging trends, and future research directions. Here, we present a bibliometric analysis to understand the research themes and trends in research related to HCV drug-resistant variants published between 1999 and 2025.

Publications related to HCV drug-resistant variants published between 1999 and 2025 were searched on the Web of Science and Scopus databases. Publication metadata and content-based data were extracted and analyzed using Bibliometrix and VOSviewer for keyword co-occurrence plot and cluster analysis.

The analysis of 653 articles revealed a clear paradigm shift, driven by the introduction of direct-acting antivirals (DAAs), which led to a significant surge in annual publications, peaking between 2014 and 2018. This shift in focus led to an emphasis on DAA efficacy, resistance mechanisms, and advanced genotyping. The United States was the most productive country, with the highest number of publications (n = 134) and citations (n = 6458). The University of São Paulo was the most productive institution (n = 40), while Antimicrobial Agents and Chemotherapy published the highest number of articles in this field (n = 40). Susser S. was the most productive researcher. Collaboration networks were found to be predominantly centered in high-income countries. Analysis of studies on minority variants showed that most studies originated from Europe and the United States, identifying low-frequency resistance-associated substitutions (RASs) such as A156V, D168V, Y93H, and S282T, with prevalence ranging from <1% to 35%, which were frequently associated with viral breakthrough and reduced treatment response.

The field successfully transitioned to the DAA era, but research output and collaboration networks were primarily driven by high-income countries, leaving a critical gap in data from Low- and Middle-Income Countries (LMICs). Closing this gap by integrating LMIC data is the next essential step to ensure global elimination strategies are effective for all countries from different income strata.