Effect of Probiotics on Immunosuppressive Drug Pharmacokinetics: Interaction between <i>Bacillus subtilis</i> and Tacrolimus in Mice
Bibliographic record
Abstract
The pharmacokinetics of drugs significantly affects their efficacy and adverse effects. Clarifying and predicting individual pharmacokinetic differences is crucial for the appropriate use of pharmaceuticals. Recent studies have suggested that the metabolic potential of intestinal bacteria may influence individual differences in pharmacokinetics. The consumption of probiotics, including supplements and fermented foods, has increased as a recent health trend. However, probiotics may also influence drug pharmacokinetics. In this study, we focused on the immunosuppressive drugs tacrolimus, everolimus, and cyclosporin A, which require careful dosing regimens, and investigated their interactions with microorganisms present in probiotics. Among the evaluated microorganisms, Bacillus subtilis exhibited a particularly strong drug degradation activity, with B. subtilis TO-A reducing the residual rates of tacrolimus and everolimus by 8 and 17%, respectively, in vitro. In a nonclinical pharmacokinetic trial, mice administered B. subtilis TO-A showed a significant reduction in the maximum blood concentration of tacrolimus. The concentration was 86.36 ± 63.61 ng/mL, much lower than that (212.8 ± 67.40 ng/mL) observed in the non-administered group. Based on these results, we can infer that orally ingested microorganisms can metabolize pharmaceuticals before reaching the small intestine, which is the primary site of absorption. These findings strongly suggest the need for caution when concurrently administering tacrolimus and probiotics in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".