epinephrine (Emerade)

Regarding emergency medical services activation after epinephrine for anaphylaxis.

Bahna Sami L SL

Removal of selected pharmaceuticals from wastewater using spawn-based pellets of Pleurotus ostreatus: efficiency and environmental implications.

Hultberg Malin M, Surra Elena E, Golovko Oksana O

Pharmaceutical residues are increasingly detected in aquatic environments, and conventional wastewater treatment plants often provide insufficient removal. In this study, we evaluated the capacity of spawn-based pellets of the white-rot fungus Pleurotus ostreatus to remove 11 pharmaceuticals from synthetic wastewater and the secondary effluent from a municipal wastewater treatment plant and conducted a preliminary life cycle assessment (LCA) of the treatment process. In synthetic wastewater, living fungal pellets reduced the pharmaceutical concentration by > 30% within 10 min compared to the control. Significant removal of nine compounds, particularly macrolide and sulfonamide antibiotics, was observed. Biosorption contributed to removal but accounted for only a minor fraction. In the secondary effluent, total pharmaceutical concentrations decreased by 51-66% after 2-24 h, with time-dependent removal observed for several compounds. LCA results indicated that fungal treatment combined with incineration of spent biomass had lower environmental impacts than landfilling, largely due to energy recovery. The major contributors to environmental burden were energy demands, particularly due to sterilisation during grain spawn preparation and aeration. Use of spawn-based pellets of P. ostreatus is a biologically based treatment technology for pharmaceutical removal. Future work should address transformation products, toxicity, and process optimization to improve sustainability at scale.

Reported Distribution Patterns of Five Kampo Formulations in the United States: Observations from a Single Manufacturer and Comparison with Japanese Prescribing Trends.

Williams Nathan L NL, Yoshino Tetsuhiro T, Shumer Gregory G, Watanabe Kenji K et al.

Kampo medicine is a traditional Japanese herbal medical system that is integrated into contemporary healthcare in Japan but remains less widely characterized in the United States. Publicly available information regarding Kampo formulation utilization in the United States is limited. Manufacturer-reported distribution information was obtained through direct correspondence with Honso Pharmaceuticals, a company distributing Kampo formulations in the United States. The company provided a ranked list of its most commonly distributed Kampo formulations. The five highest-ranked formulations were reviewed descriptively and compared with published reports of Kampo prescribing patterns in Japan. The five highest-ranked formulations reported by Honso Pharmaceuticals were Kamishoyosan, Shosaikoto, Hachimijiogan, Keishibukuryogan, and Juzentaihoto. These formulations differ from commonly prescribed Kampo formulations reported in Japanese health insurance and outpatient prescription datasets. Potential explanations may include differences in regulatory status, practitioner education, product availability, and healthcare-system structure between the United States and Japan. Manufacturer-reported distribution patterns suggest that Kampo formulation use in the United States may differ from prescribing trends reported in Japan. Because these observations are derived from a single manufacturer and lack patient-level utilization data, they should be interpreted cautiously. Additional research is needed to better characterize Kampo medicine utilization in the United States.

Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.

Magnusson Rasmus R, Johansson Markus M, Hagvall Sepideh S, Synnergren Jane J

Drug repurposing has emerged as an attractive strategy in contemporary pharmaceutical research, presenting an opportunity to expedite drug discovery, minimize developmental costs, and mitigate risks associated with developing new pharmaceuticals. In this study, we investigated a novel approach based on deep learning of human transcriptomic mechanisms for systematic identification of additional therapeutic potential in preexisting drugs. We trained a composite feedforward neural network model using gene expression data sourced from the ARCHS4 compilation of the GEO, encompassing extensive human datasets. Subsequently, disease-associated gene expression data were generated from our stem cell-derived in vitro model of cardiac hypertrophy induced by Endothelin-1 stimulation. These data were employed to identify latent variables associated with genes showing differential expression due to Endothelin-1 stimulation. By examining the differential expression profiles within the model's latent space, we successfully correlated the disease signal with known drug targets found in pharmaceutical compounds cataloged in DrugBank. The model accurately encoded additional disease-related genes beyond the curated gene set, demonstrating its ability to generalize disease associations. Leveraging the model, we identified potential drug candidates, such as lapatinib and amiodarone showing promise in mitigating proBNP concentration associated with cardiac hypertrophy. This study demonstrates the power of deep learning of human transcriptomic mechanisms in swiftly identifying new therapeutic potentials for existing drugs, highlighting the pivotal role of artificial intelligence technologies in accelerating drug development for other complex medical conditions.