19. The Impact of Investigational Purified Microbiome Therapeutic SER-109 on Health-Related Quality of Life (HRQoL) of Patients with Recurrent <i>Clostridioides difficile</i> Infection (rCDI) in ECOSPOR III, a Placebo-Controlled Clinical Trial
Bibliographic record
Abstract
Abstract Background Following standard of care antibiotics, investigational microbiome therapeutic, SER-109, achieved superiority vs placebo (PBO) at 8 weeks in reducing rCDI in patients with ≥3 prior episodes (12.4% vs 39.8%, respectively; p&lt; 0.001). We evaluated the impact of SER-109 vs PBO on HRQoL with general (EQ-5D-5L) and disease-specific (Cdiff32) measures [Garey 2016]. Methods EQ-5D-5L measures outcomes in 5 domains (mobility, self-care, activities, pain/discomfort, and anxiety/depression) while Cdiff32 measures outcomes in 3 domains (physical, mental, and social) including 5 associated subdomains. Patients completed EQ-5D-5L and Cdiff32 measures at baseline (BL), Wk 8, and at recurrence/early termination. Changes from BL were assessed between SER-109 vs PBO and by clinical outcome (recurrence versus nonrecurrence) in the ITT population and within each treatment arm. The between treatment group comparison analysis controlled for age, gender, prior antibiotics, and number of prior CDI episodes. Results Mean EQ-5D-5L and Cdiff32 scores were comparable between SER-109 and PBO at BL. EQ-5D-5L did not detect differences at Wk 8 from BL between SER-109 and PBO or by clinical outcome. In contrast, Cdiff32 detected significant improvements at Wk 8 from BL within both SER-109 subjects and PBO subjects (Fig1) and by recurrence status (Fig2). Subjects achieved significant improvement in all domains at Wk 8 from BL regardless of treatment group. When examining recurrence status within treatment arms, all PBO subjects with non-recurrence showed improvement in all health domains, while PBO subjects with recurrence had declines in several subdomains (Fig3B). Similarly, SER-109 subjects with non-recurrence showed improvement in all domains compared to BL. However, overall and mental domain/subdomains scores also improved in SER-109 subjects with recurrence (Fig3A). Conclusion Significant HRQoL improvements were associated with CDI nonrecurrence, which highlights the negative impact of this debilitating infection. SER-109 was associated with improved overall and mental scores, regardless of clinical outcome. Further investigation is warranted on the impact of SER-109 on mental health even among those with CDI recurrence. Disclosures Elizabeth Hohmann, MD, Seres Therapeutics (Research Grant or Support) Paul Feuerstadt, MD, FACG, Ferring/Rebiotix Pharmaceuticals (Consultant, Scientific Research Study Investigator, Speaker’s Bureau)Finch Pharmaceuticals (Scientific Research Study Investigator)Merck and Co (Speaker’s Bureau)SERES Therapeutics (Consultant, Scientific Research Study Investigator)Takeda Pharmaceuticals (Consultant) Christine Lee, MD, FRCPC, Pfizer (Board Member)Rebiotix-Ferring (Board Member)Rebiotix-Ferring (Grant/Research Support)Seres (Grant/Research Support)Summit (Grant/Research Support) Sissi Pham, PharmD, Seres (Consultant) Pat Ray Reese, PhD, Reese Associates, LLC (Consultant, Independent Contractor) Elaine E. Wang, MD, Seres Therapeutics (Employee) Elaine E. Wang, MD, Seres Therapeutics (Employee, Shareholder) Lisa von Moltke, MD, Seres Therapeutics (Employee, Shareholder) Kevin W. Garey, Pharm.D., M.S., FASHP, Summit Therapeutics (Research Grant or Support)
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".