Mechanistic insight into digoxin inactivation by<i>Eggerthella lenta</i>augments our understanding of its pharmacokinetics
Bibliographic record
Abstract
The human gut microbiota plays a key role in pharmacology, yet the mechanisms responsible remain unclear, impeding efforts toward personalized medicine. We recently identified a cytochrome-encoding operon in the common gut Actinobacterium Eggerthella lenta that is transcriptionally activated by the cardiac drug digoxin. These genes represent a predictive microbial biomarker for the inactivation of digoxin. Gnotobiotic mouse experiments revealed that increased protein intake can limit microbial drug inactivation. Here, we present a biochemical rationale for how the proteins encoded by this operon might inactivate digoxin through substrate promiscuity. We discuss digoxin signaling in eukaryotic systems, and consider the possibility that endogenous digoxin-like molecules may have selected for microbial digoxin inactivation. Finally, we highlight the diverse contributions of gut microbes to drug metabolism, present a generalized approach to studying microbe-drug interactions, and argue that mechanistic studies will pave the way for the clinical application of this work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".