The cost of interferon-free combination therapies remains high to provide widespread access to treatment, regardless of fibrosis stage.

What is known and objective Rheumatoid arthritis (RA) is an autoimmune disease characterized primarily by inflammation and pain in the joints. Tofacitinib is an oral drug recently approved for RA treatment; it inhibits Janus protein kinases (JAK) that reduces RA symptoms when conventional DMARDs do not trigger a response. This study aimed to compare the efficacy of biological DMARDs in monotherapy or combined with methotrexate in RA patients and compare the treatments. Methods We reviewed the literature for articles published up to June 2017, evaluating the efficacy and safety of the biological DMARDs indicated for RA in patients with inadequate responses to conventional DMARDs and naïve to biological DMARDs, in similar populations, considering ACR50 as the efficacy variable. The odds ratio (OR) and 95% confidence interval (CI) were calculated for each drug combination, and these parameters were transformed into differences in responses to assess the effectiveness of the alternative medicines. Equivalence therapeutic alternatives (ETA) were ensured to assess the possibility of considering these medications with equivalent efficacy. A network meta‐analysis (NMA) was performed using Bayesian approaches and the fixed‐effects model. Results and discussion Twenty‐seven randomized clinical trials (RCTs) that met the pre‐established criteria were identified. The 95% CI of biological DMARDs was higher than that of placebo without methotrexate, except for certolizumab, golimumab‐m, anakinra‐m and adalimumab monotherapy. These DMARDs performed significantly better than the placebo, except for etanercept, certolizumab, tofacitinib and golimumab. Certolizumab‐m was better than anakinra‐m and adalimumab, and tocilizumab alone or combined with methotrexate was superior to adalimumab. Etanercept‐m yielded a higher difference in responses compared with the other biological DMARDs, which presented more homogeneous responses, except for adalimumab and anakinra‐m, which yielded worse results. None of the biological DMARDs displayed ETA to etanercept‐m; however, they displayed ETA with certolizumab‐m, except for adalimumab and anakinra‐m. What is new and conclusion All biological DMARDs used in combination with methotrexate, except for etanercept, anakinra, certolizumab and tocilizumab without methotrexate, were displayed ETA on using ACR50 at week 24 in patients naïve to biological DMARDs. Etanercept displayed a greater difference in responses, although the high uncertainty of the comparative results prevented the confirmation of the increased efficacy of this drug.

Background: This study provides a cost-effectiveness analysis of therapeutic strategies for chronic hepatitis C genotype 3 infected patients in Spain. Methods: A Markov model was designed to simulate the progression in a cohort of patients aged 50 years over a lifetime horizon. Results: Sofosbuvir (SOF) plus peginterferon and ribavirin for 12 weeks was a cost-effective option when compared to standard of care (SoC) in the treatment of both ‘moderate fibrosis’ and ‘cirrhotic’ patients. Incremental cost-effectiveness ratios were €35,276/QALY and €18,374/QALY respectively. ICERs for SOF plus daclatasvir (DCV) regimens versus SoC were over the threshold limit considered, at €56,178/QALY and €77,378/QALY for ‘moderate fibrosis’ and ‘cirrhotic’ patients respectively. Conclusion: Addition of SOF to IFN-based regimens for genotype 3 was cost-effective for both ‘moderate fibrosis’ and ‘cirrhotic’ patients. IFN-free options including SOF and DCV association required price reductions lower than the list prices to be considered cost-effective.

The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyzes artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer's block. Python libraries such as LangChain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasized. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimize information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically.