JOURNAL ARTICLE

A review of fecal microbiota, live-jslm for the prevention of recurrent Clostridioides difficile infection.

  • Published In: American Journal of Health-System Pharmacy, 2024, v. 81, n. 15. P. e402 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hunt, Aaron; Drwiega, Emily; Wang, Yifan; Danziger, Larry 3 of 3

Abstract

This article reviews Rebyota (fecal microbiota, live-jslm), the first Food and Drug Administration (FDA)-approved live biotherapeutic product (LBP) designed to prevent recurrent Clostridioides difficile infection (rCDI) by restoring the intestinal microbiome after antibiotic treatment. Clinical trials, including the PUNCH series, demonstrated that Rebyota is generally safe, with mostly mild to moderate gastrointestinal adverse events, and effective in reducing rCDI rates with sustained responses up to 24 months. The product is administered rectally by healthcare providers following antibiotic therapy and is manufactured from screened human donor stool to ensure consistent bacterial composition. While early trials excluded patients with gastrointestinal comorbidities, ongoing studies are addressing this gap, and current guidelines recommend LBPs like Rebyota for patients with multiple CDI recurrences. The approval of Rebyota marks a significant advancement in microbiome-based therapies, offering a standardized alternative to traditional fecal microbiota transplantation.

Additional Information

  • Source:American Journal of Health-System Pharmacy. 2024/08, Vol. 81, Issue 15, pe402
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:1079-2082
  • DOI:10.1093/ajhp/zxae066
  • Accession Number:178650195
  • Copyright Statement:Copyright of American Journal of Health-System Pharmacy is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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