Background: Biosimilars account for 30-40% of biologic medications dispensed in the United States (US), yet healthcare providers in relevant medical specialties have limited awareness of biosimilars and their characteristics. Likewise, many providers perceive biosimilars as less safe and effective than original biologics and are more comfortable prescribing original biologics to patients.
Methods: We conducted in-person focus groups at three clinical sites in California and Texas (n = 49) to explore the reasons behind US healthcare providers' limited understanding of, cautious attitudes toward, and reluctance to prescribe biosimilars. We conducted thematic analysis by having three researchers independently analyze verbatim transcripts and identify patterns in provider responses.
Results: Providers' limited knowledge of and cautious attitudes toward biosimilars are driven by uncertainty about how biosimilarity is defined and operationalized as well as negative past experiences with generic drugs that did not perform as well as branded counterparts. Additionally, healthcare providers are unfamiliar with the Food and Drug Administration's (FDA's) approval pathway for biosimilars and are skeptical that an abbreviated approval process is rigorous enough to ensure biosimilars deliver the same efficacy and have the same side effect profiles as original biologics. Physicians also expressed concerns about pharmacy substitution of biosimilars and interchangeables, explaining they would be unaware of which medication was ultimately given to their patients.
Conclusions: Educating physicians and pharmacists about biosimilars-including how biosimilarity is defined and operationalized, the structure of the biosimilar approval process, and how analytical data can ensure biosimilar safety and efficacy-will be important for reducing healthcare providers' concerns and increasing biosimilar adoption in the US.
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http://dx.doi.org/10.1007/s40259-022-00545-7 | DOI Listing |
Viruses
November 2024
Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Amanda Psyrri was not included as an author in the original publication [...
View Article and Find Full Text PDFViruses
November 2024
Institute of Biology, ELTE Eötvös Loránd University, 1117 Budapest, Hungary.
The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatics workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we assessed the performance and applicability of four open-source bioinformatics pipelines (shiver-for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign; viral-ngs; and V-pipe) using both simulated and real-world HIV-1 paired-end short-read datasets and default settings. All four pipelines produced consensus genome assemblies with high quality metrics (genome fraction recovery, mismatch and indel rates, variant calling F1 scores) when the reference sequence used for assembly had high similarity to the analyzed sample.
View Article and Find Full Text PDFVaccines (Basel)
November 2024
Laboratory of Proteolytic Enzyme Chemistry, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 117997 Moscow, Russia.
IgA1 protease is one of the virulence factors of , and other pathogens causing bacterial meningitis. The aim of this research is to create recombinant proteins based on fragments of the mature IgA1 protease A-P from serogroup B strain H44/76. These proteins are potential components of an antimeningococcal vaccine for protection against infections caused by pathogenic strains of and other bacteria producing serine-type IgA1 proteases.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Orobix Life, 24121 Bergamo, Italy.
We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust () diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust.
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