Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential.

This paper aims to explore the possibility of employing large language models (LLMs) - a type of artificial intelligence (AI) - in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation and potential risk reduction measures are analysed. The existing literature is reviewed to investigate the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the OECD.ai platform was used. The review highlights the dual-use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework. The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual-use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability and safety. Additionally, AI's potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks.

Analysis of the Gut Microbiome and Dietary Habits in Metastatic Melanoma Patients with a Complete and Sustained Response to Immunotherapy.

Immunotherapy has improved the prognosis of metastatic melanoma patients, although most patients do not achieve a complete response. While specific gut microbiome and dietary habits might influence treatment success, there is a lack of concordance between the studies, potentially due to dichotomizing patients only into responders and non-responders. The aim of this study was to elucidate whether metastatic melanoma patients with complete and sustained response to immunotherapy exhibit differences in gut microbiome composition among themselves, and whether those differences were associated with specific dietary habits. Shotgun metagenomic sequencing revealed that patients who exhibited a complete response after more than 9 months of treatment (late responders) exhibited a significantly higher beta-diversity (p = 0.02), with a higher abundance of Coprococcus comes (LDA 3.548, p = 0.010), Bifidobacterium pseudocatenulatum (LDA 3.392, p = 0.024), and lower abundance of Prevotellaceae (p = 0.04) compared to early responders. Furthermore, late responders exhibited a different diet profile, with a significantly lower intake of proteins and sweets and a higher intake of flavones (p < 0.05). The research showed that metastatic melanoma patients with a complete and sustained response to immunotherapy were a heterogeneous group. Patients with a late complete response exhibited microbiome and dietary habits which were previously associated with an improved response to immunotherapy.

Impact of the COVID-19 pandemic on hospital antimicrobial consumption in Croatia.

OBJECTIVES: The aim of the study was to assess the impact of the COVID-19 pandemic on antimicrobial consumption (AMC) in the hospital sector in Croatia by analysing data reported to the European Surveillance of Antimicrobial Consumption Network (ESAC-Net) between 2016 and 2020. METHODS: AMC was measured as the number of DDDs/1000 inhabitants/day and as the number of DDDs/100 bed days. To assess trends, linear regression was performed. To assess the impact of the pandemic on AMC, the compound annual growth rate was calculated for the years preceding the pandemic based on which AMC for 2020 was forecasted and compared with the actual consumption. RESULTS: While hospital AMC expressed as DDDs/1000 inhabitants/day between 2019 and 2020 decreased by 17%, when expressed as DDDs/100 bed days, an 8% increase was observed. Hospital consumption of antibacterials for systemic use in the 5 year period did not significantly change when expressed as DDDs/1000 inhabitants/day, while it statistically significantly increased when expressed as DDDs/100 bed days. An increasing trend in consumption of broad-spectrum antimicrobials was found. CONCLUSIONS: During the pandemic there was an increase in hospital AMC with a shift towards broad-spectrum antimicrobials requiring further in-depth qualitative analysis based on patient-level data. Contrasting results obtained using different denominators indicate that the metric DDDs/1000 inhabitants/day is not sensitive enough to evaluate hospital AMC. When assessing hospital AMC, the population under surveillance should be relevant for healthcare context. Antimicrobial stewardship remains one of the most important strategies to tackle antimicrobial resistance and antimicrobial surveillance methods must be as sensitive as possible.