Microbes and medicines: interrelationships between pharmaceuticals and the gut microbiome
Bibliographic record
Abstract
The human gut microbiome plays a critical role in modulating pharmacological and toxicological responses to medications. With a gene pool vastly exceeding that of the human host, the gut microbiome acts as a metabolically active organ capable of transforming, inactivating, or accumulating drugs. This review explores the bidirectional interplay between prescription medicines and the gut microbiome, encompassing three key mechanisms: direct biotransformation by microbial enzymes, indirect modulation of host metabolism and signaling pathways, and drug bioaccumulation within microbial cells. Particular attention is given to six major drug classes: immunotherapeutics, chemotherapeutics, antidepressants, statins, hypoglycemics, and antihypertensives. The ways in which individual microbial profiles can influence therapeutic outcomes are also reviewed. We examined how common non-antibiotic pharmaceuticals can significantly alter microbial diversity and promote antimicrobial resistance. Strategies to enhance drug efficacy through microbiome modulation, including probiotics, prebiotics, and fecal microbiota transplantation (FMT), are critically assessed. Experimental models ranging from in vitro batch and chemostat systems to animal and clinical studies are compared in terms of their utility for studying drug‒microbiome interactions. Finally, emerging evidence suggesting the gut microbiota composition may serve as a predictive biomarker for personalized medicine and therapeutic success is highlighted. Understanding and harnessing the complex interrelationships between medicines and microorganisms could offer novel avenues to optimize treatment outcomes and mitigate adverse drug effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".