Pharmacological Efficacy/Toxicity of Drugs: A Comprehensive Update About the Dynamic Interplay of Microbes

GRL-PUL: predicting microbe-drug association based on graph representation learning and positive unlabeled learning

Extensive research has confirmed the widespread presence of microorganisms in the human body and their crucial impact on human health, with drugs being an effective method of regulation. Hence it is essential to identify potential microbe-drug associations (MDAs). Owing to the limitations of wet experiments, such as high costs and long durations, computational methods for binary classification tasks have become valuable alternatives for traditional experimental approaches. Since validated negative MDAs are absent in existing datasets, most methods randomly sample negatives from unlabeled data, which evidently leads to false negative issues. In this manuscript, we propose a novel model based on graph representation learning and positive-unlabeled learning (GRL-PUL), to infer potential MDAs. Firstly, we screen reliable negative samples by applying weighted matrix factorization and the PU-bagging strategy on the known microbe-drug bipartite network. Then, we combine muti-model attributes and constructed a microbe-drug heterogeneous network. After that, graph attention auto-encoder module, an encoder combining graph convolutional networks and graph attention networks, is introduced to extract informative embeddings based on the microbe-drug heterogeneous network. Lastly, we adopt a modified random forest as the final classifier. Comparison experiments with five baseline models on three benchmark datasets show that our model surpasses other methods in terms of the AUC, AUPR, ACC, F1-score and MCC. Moreover, several case studies show that GRL-PUL could capably predict latent MDAs. Notably, we further verify the effectiveness of a reliable negative sample selection module by migrating it to other state-of-the-art models, and the experimental results demonstrate its ability to substantially improve their prediction performance.

More than Our Enemy: Making Space for the Microbiome in Pharmacy Education

The microbiome is the collection of commensal microorganisms along with their genomes inhabiting the human body. Despite the many known beneficial effects of these microbes on human health, the 2016 ACPE Standards for Doctor of Pharmacy curricula describe Medical Microbiology in Appendix 1 with a pathogen-centered focus. Over the last twenty years, evolving biotechnology has enabled a deeper understanding of the microbiome in the context of both wellness and disease. Retail stores are allocating increasing shelf space to commercial probiotic products, while the approach to PharmD training on the selection and use of these natural care products remains static, creating a disproportionate footprint between PharmD curricula and consumer markets. Looking to the future of patient care, we brief pharmacy educators on the current evidence and invite discussion around a proposed revision to the 2025 ACPE Standards that would add language recognizing the beneficial role of the commensal microbiota and expanding therapeutic applications of microbiome supplementation. We suggest a variety of opportunities within Doctor of Pharmacy curricula as leverage points for including relevant aspects of the microbiome in the training of future pharmacists.

Unravelling the antimicrobial action of antidepressants on gut commensal microbes

Over the past decade, there has been increasing evidence highlighting the implication of the gut microbiota in a variety of brain disorders such as depression, anxiety, and schizophrenia. Studies have shown that depression affects the stability of gut microbiota, but the impact of antidepressant treatments on microbiota structure and metabolism remains underexplored. In this study, we investigated the in vitro antimicrobial activity of antidepressants from different therapeutic classes against representative strains of human gut microbiota. Six different antidepressants: phenelzine, venlafaxine, desipramine, bupropion, aripiprazole and (S)-citalopram have been tested for their antimicrobial activity against 12 commensal bacterial strains using agar well diffusion, microbroth dilution method, and colony counting. The data revealed an important antimicrobial activity (bacteriostatic or bactericidal) of different antidepressants against the tested strains, with desipramine and aripiprazole being the most inhibitory. Strains affiliating to most dominant phyla of human microbiota such as Akkermansia muciniphila, Bifidobacterium animalis and Bacteroides fragilis were significantly altered, with minimum inhibitory concentrations (MICs) ranged from 75 to 800 μg/mL. A significant reduction in bacterial viability was observed, reaching 5 logs cycle reductions with tested MICs ranged from 400 to 600 μg/mL. Our findings demonstrate that gut microbiota could be altered in response to antidepressant drugs.

Prescribed Drugs and the Microbiome

The interaction between drug use, the microbiome, and the host is complex and multidimensional. Drugs and the microbiota may be risk factors or protective factors for disease. These interactions may explain interpersonal variations in drug efficacy and toxicity, but also interpersonal variations in microbiota composition and functioning, and potential (long-term) side effects from drugs.