639. Time to Recurrence of <i>Clostridioides difficile</i> Infection (rCDI) is Rapid Following Completion of Standard of Care Antibiotics: Results from ECOSPOR-III, a Phase 3 Double-Blind, Placebo-Controlled Randomized Trial of SER-109, an Investigational Microbiome Therapeutic
Bibliographic record
Abstract
Abstract Background The natural history of CDI recurrence after antibiotics may be helpful to understand the window of opportunity for microbiome repair. ECOSPOR III evaluated the efficacy of SER-109, an investigational microbiome therapeutic, compared to placebo with rates of rCDI as the primary endpoint. SER-109 was superior to placebo in reducing the rate of rCDI following standard-of-care antibiotics at 8 weeks (12.4% vs 39.8%, respectively; P &lt; 0.001). Herein, we describe results from the secondary endpoint, time to recurrence, in this well-characterized study population. Methods A total of 182 C. difficile toxin+ adults with ≥ 3 CDI episodes and symptom resolution on CDI antibiotics were randomly assigned to SER-109 (4 capsules orally x 3 days) or placebo. Recurrence for this analysis was defined as ≥ 3 unformed stools/day for ≥ 48 hours, ± C. difficile stool toxin test, and an investigator decision to treat. Time to CDI recurrence was analyzed using observed data and Kaplan-Meier methods. Data were not imputed for subjects lost to follow-up or discontinued from study. Subjects who did not have a CDI recurrence were censored on the date of study completion, study discontinuation or death. Results Through 24 weeks, 11/89 (12.4%) SER-109 and 36/93 (38.7%) placebo subjects had rCDI (P &lt; 0.001). Of all recurrence events in the study population, 16/47 (34.0%) were observed within 1 week; 30/47 (63.8%) within 2 weeks; and 34/47 (72.3%) within 4 weeks after randomization, highlighting the rapid onset of recurrence. On the other hand, 12/47 (25.5%) recurrences occurred between 4 and 12 weeks, highlighting late onset of recurrence in a subset of patients (Table). Significantly lower rates of recurrence in patients on SER-109 compared to placebo was maintained throughout the 24-week follow-up (Figure). Time of rCDI K-M Plot Conclusion SER-109, an investigational oral microbiome therapeutic, maintained significant efficacy in reducing rCDI vs placebo through 24 weeks. About two-thirds of all recurrences occurred within 14 days of antibiotic completion highlighting the need for rapid repair of the disrupted microbiome. However, the significant number of late recurrences in the placebo arm also highlights that rCDI trials limited to 4 weeks of follow-up after treatment completion may underestimate recurrences. Disclosures Thomas J. Louie, MD, Artugen (Advisor or Review Panel member)Crestone (Consultant, Grant/Research Support)Da Volterra (Advisor or Review Panel member)Finch Therapeutics (Grant/Research Support, Advisor or Review Panel member)MGB Biopharma (Grant/Research Support, Advisor or Review Panel member)Rebiotix (Consultant, Grant/Research Support)Seres Therapeutics (Consultant, Grant/Research Support)Summit PLC (Grant/Research Support)Vedanta (Grant/Research Support, Advisor or Review Panel member) Matthew Sims, MD, PhD, Astra Zeneca (Independent Contractor)Diasorin Molecular (Independent Contractor)Epigenomics Inc (Independent Contractor)Finch (Independent Contractor)Genentech (Independent Contractor)Janssen Pharmaceuticals NV (Independent Contractor)Kinevant Sciences gmBH (Independent Contractor)Leonard-Meron Biosciences (Independent Contractor)Merck and Co (Independent Contractor)OpGen (Independent Contractor)Prenosis (Independent Contractor)Regeneron Pharmaceuticals Inc (Independent Contractor)Seres Therapeutics Inc (Independent Contractor)Shire (Independent Contractor)Summit Therapeutics (Independent Contractor) Richard Nathan, DO, none (Other Financial or Material Support, I am PI on several clinical trials. If you need that information, I would be happy to supply it.) Princy N. Kumar, MD, AMGEN (Other Financial or Material Support, Honoraria)Eli Lilly (Grant/Research Support)Gilead (Grant/Research Support, Shareholder, Other Financial or Material Support, Honoraria)GSK (Grant/Research Support, Shareholder, Other Financial or Material Support, Honoraria)Merck & Co., Inc. (Grant/Research Support, Shareholder, Other Financial or Material Support, Honoraria) Elaine E. Wang, MD, Seres Therapeutics (Employee) Elaine E. Wang, MD, Seres Therapeutics (Employee, Shareholder) Robert Stevens, PharmD, Seres Therapeutics (Employee, Shareholder) Kelly Brady, MS, Seres Therapeutics (Employee, Shareholder) Barbara McGovern, MD, Seres Therapeutics (Employee, Shareholder) Lisa von Moltke, MD, Seres Therapeutics (Employee, Shareholder)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| 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 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".