The DAV132 colon-targeted adsorbent does not interfere with plasma concentrations of antibiotics but prevents antibiotic-related dysbiosis: a randomized phase I trial in healthy volunteers
Bibliographic record
Abstract
The deleterious impact of antibiotics (ATB) on the microbiome negatively influences immune checkpoint inhibitors (ICI) response in patients with cancer. We conducted a randomized phase I study (EudraCT:2019-A00240-57) with 148 healthy volunteers (HV) to test two doses of DAV132, a colon-targeted adsorbent, alongside intravenous ceftazidime-avibactam (CZA), piperacillin-tazobactam (PTZ) or ceftriaxone (CRO) and a group without ATB. The primary objective of the study was to assess the effect of DAV132 on ATB plasma concentrations and both doses of DAV132 did not alter ATB levels. Secondary objectives included safety, darkening of the feces, and fecal ATB concentrations. DAV132 was well tolerated, with no severe toxicity and similar darkening at both DAV132 doses. DAV132 led to significant decrease in CZA or PTZ feces concentration. When co-administered with CZA or PTZ, DAV132 preserved microbiome diversity, accelerated recovery to baseline composition and protected key commensals. Fecal microbiota transplantation (FMT) in preclinical cancer models in female mice from HV treated with CZA or PTZ alone inhibited anti–PD-1 response, while transplanted samples from HV treated with ATB + DAV132 circumvented resistance to anti–PD-1. This effect was linked to activated CD8+ T cell populations in the tumor microenvironment. DAV132 represents a promising strategy for overcoming ATB-related dysbiosis and further studies are warranted to evaluate its efficacy in cancer patients. The oral antibiotic adsorbent DAV132 can reduce fecal concentrations of antibiotics while preserving their pharmacokinetic properties. Here, in a randomized trial in healthy volunteers treated with antibiotics, the authors show that DAV132 does not affect plasma concentrations of the antibiotics but preserves microbiome diversity and composition, with implications for cancer immunotherapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".