Transformation of the Anticancer Drug Doxorubicin in the Human Gut Microbiome
Bibliographic record
Abstract
Bacteria living in the human gut are implicated in the etiology of several diseases. Moreover, dozens of drugs are metabolized by elements of the gut microbiome, which may have further implications for human health. Here, we screened a collection of gut isolates for their ability to inactivate the widely used antineoplastic drug doxorubicin and identified a strain of Raoultella planticola as a potent inactivator under anaerobic conditions. We demonstrate that R. planticola deglycosylates doxorubicin to metabolites 7-deoxydoxorubicinol and 7-deoxydoxorubicinolone via a reductive deglycosylation mechanism. We further show that doxorubicin is degraded anaerobically by Klebsiella pneumoniae and Escherichia coli BW25113 and present evidence that this phenotype is dependent on molybdopterin-dependent enzyme(s). Deglycosylation of doxorubicin by R. planticola under anaerobic conditions is found to reduce toxicity to the model species Caenorhabditis elegans, providing a model to begin understanding the role of doxorubicin metabolism by microbes in the human gut. Understanding the in vivo metabolism of important therapeutics like doxorubicin by the gut microbiome has the potential to guide clinical dosing to maximize therapeutic benefit while limiting undesirable side 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 | 0.000 |
| 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".