<i>Clostridioides difficile</i>: innovations in target discovery and potential for therapeutic success
Bibliographic record
Abstract
Introduction Clostridioides difficile infection (CDI) remains a worldwide clinical problem. Increased incidence of primary infection, occurrence of hypertoxigenic ribotypes, and more frequent occurrence of drug resistant, recurrent, and non-hospital CDI, emphasizes the urgent unmet need of discovering new therapeutic targets.Areas covered We searched PubMed and Web of Science databases for articles identifying novel therapeutic targets or treatments for C. difficile from 2001 to 2021. We present an updated review on current preclinical efforts on designing inhibitory compounds against these drug targets and indicate how these could become the focus of future therapeutic approaches. We also evaluate the increasing exploitability of gut microbial-derived metabolites and host-derived therapeutics targeting VEGF-A, immune targets and pathways, ion transporters, and microRNAs as anti-C. difficile therapeutics, which have yet to reach clinical trials. Our review also highlights the therapeutic potential of re-purposing currently available agents . We conclude by considering translational hurdles and possible strategies to mitigate these problems.Expert opinion Considerable progress has been made in the development of new anti-CDI drug candidates. Nevertheless, a greater comprehension of CDI pathogenesis and host-microbe interactions is beginning to uncover potential novel therapeutic targets, which can be exploited to plug gaps in the CDI drug discovery pipeline.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".