Bacterial metabolism rescues the inhibition of intestinal drug absorption by food and drug additives
Bibliographic record
Abstract
Food and drug products contain diverse and abundant small-molecule additives (excipients) with unclear impacts on human physiology, drug safety, and response. Here, we evaluate their potential impact on intestinal drug absorption. By screening 136 unique compounds for inhibition of the key intestinal transporter OATP2B1 we identified and validated 24 potent OATP2B1 inhibitors, characterized by higher molecular weight and hydrophobicity compared to poor or noninhibitors. OATP2B1 inhibitors were also enriched for dyes, including 8 azo (R-N=N-R') dyes. Pharmacokinetic studies in mice confirmed that FD&C Red No. 40, a common azo dye excipient and a potent inhibitor of OATP2B1, decreased the plasma level of the OATP2B1 substrate fexofenadine, suggesting that FD&C Red No. 40 has the potential to block drug absorption through OATP2B1 inhibition in vivo. However, the gut microbiomes of multiple unrelated healthy individuals as well as diverse human gut bacterial isolates were capable of inactivating the identified azo dye excipients, producing metabolites that no longer inhibit OATP2B1 transport. These results support a beneficial role for the microbiome in limiting the unintended effects of food and drug additives in the intestine and provide a framework for the data-driven selection of excipients. Furthermore, the ubiquity and genetic diversity of gut bacterial azoreductases coupled to experiments in conventionally raised and gnotobiotic mice suggest that variations in gut microbial community structure may be less important to consider relative to the high concentrations of azo dyes in food products, which have the potential to saturate gut bacterial enzymatic activity.
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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".