Bringing pharmacomicrobiomics to the clinic through well‐designed studies

Abstract

Pharmacomicrobiomic studies investigate drug‐microbiome interactions, such as the effect of microbial variation on drug response and disposition. Studying and understanding the interactions between the gut microbiome and drugs is becoming increasingly relevant to clinical practice due to its potential for avoiding adverse drug reactions or predicting variability in drug response. The highly variable nature of the human microbiome presents significant challenges to assessing microbes’ influence. Studies aiming to explore drug‐microbiome interactions should be well‐designed to account for variation in the microbiome over time and collect data on confounders such as diet, disease, concomitant drugs, and other environmental factors. Here, we assemble a set of important considerations and recommendations for the methodological features required for performing a pharmacomicrobiomic study in humans with a focus on the gut microbiome. Consideration of these factors enable discovery, reproducibility, and more accurate characterization of the relationships between a given drug and the microbiome. Furthermore, appropriate interpretation and dissemination of results from well‐designed studies will push the field closer to clinical relevance and implementation.

Co‐consuming green tea with raloxifene decreases raloxifene systemic exposure in healthy adult participants

“…The abundance of each taxa in the stool collections was analyzed using the phyloseq package in R 28 . Microbial alpha diversity was assessed using the Shannon and Chao1 diversity indices, which address species richness and evenness 29 . Compositional heterogeneity of the microbial community in each sample from each arm was visualized at the genus level using principal coordinate analyses and the Bray–Curtis dissimilarity index 29 .…”

“…Microbial alpha diversity was assessed using the Shannon and Chao1 diversity indices, which address species richness and evenness 29 . Compositional heterogeneity of the microbial community in each sample from each arm was visualized at the genus level using principal coordinate analyses and the Bray–Curtis dissimilarity index 29 . Beta diversity was statistically evaluated using the adonis function through the vegan package in R 30 .…”

“…The abundance of each taxa in the stool collections was analyzed using the phyloseq package in R. 28 Microbial alpha diversity was assessed using the Shannon and Chao1 diversity indices, which address species richness and evenness. 29 Compositional heterogeneity of the microbial community in each sample from each arm was visualized at the genus level using principal coordinate analyses and the Bray–Curtis dissimilarity index. 29 Beta diversity was statistically evaluated using the adonis function through the vegan package in R. 30 This statistical approach was repeated for the putative functional data of the microbiota expressed through the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) method.…”

“… 29 Compositional heterogeneity of the microbial community in each sample from each arm was visualized at the genus level using principal coordinate analyses and the Bray–Curtis dissimilarity index. 29 Beta diversity was statistically evaluated using the adonis function through the vegan package in R. 30 This statistical approach was repeated for the putative functional data of the microbiota expressed through the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) method. 31 PICRUSt is a computational method used to predict the metatranscriptomics for each sample based on 16S data.…”

Microbiome Dynamics: A Paradigm Shift in Combatting Infectious Diseases

“…Furthermore, the microbiome has been found to impact the response to antiviral therapies by impacting drug metabolism, immune system activation, drug resistance, inflammation, and offering potential for personalized medicine approaches [172][173][174]. Individuals with a specific composition of gut bacteria may have a higher likelihood of responding positively to antiviral medications for hepatitis B [175][176][177][178][179]. Similarly, the composition of the respiratory tract microbiome has been linked to the severity and duration of respiratory viral infections like influenza [180,181].…”