SER-109: An Oral Investigational Microbiome Therapeutic for Patients with Recurrent Clostridioides difficile Infection (rCDI)
Bibliographic record
Abstract
Clostridioides difficile infection (CDI) is classified as an urgent health threat by the Centers for Disease Control and Prevention (CDC), and affects nearly 500,000 Americans annually. Approximately 20−25% of patients with a primary infection experience a recurrence, and the risk of recurrence increases with subsequent episodes to greater than 40%. The leading risk factor for CDI is broad-spectrum antibiotics, which leads to a loss of microbial diversity and impaired colonization resistance. Current FDA-approved CDI treatment strategies target toxin or toxin-producing bacteria, but do not address microbiome disruption, which is key to the pathogenesis of recurrent CDI. Fecal microbiota transplantation (FMT) reduces the risk of recurrent CDI through the restoration of microbial diversity. However, FDA safety alerts describing hospitalizations and deaths related to pathogen transmission have raised safety concerns with the use of unregulated and unstandardized donor-derived products. SER-109 is an investigational oral microbiome therapeutic composed of purified spore-forming Firmicutes. SER-109 was superior to a placebo in reducing CDI recurrence at Week 8 (12% vs. 40%, respectively; p < 0.001) in adults with a history of recurrent CDI with a favorable observed safety profile. Here, we discuss the role of the microbiome in CDI pathogenesis and the clinical development of SER-109, including its rigorous manufacturing process, which mitigates the risk of pathogen transmission. Additionally, we discuss compositional and functional changes in the gastrointestinal microbiome in patients with recurrent CDI following treatment with SER-109 that are critical to a sustained clinical response.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".