Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract.

Wang, Fanjin et al.·International journal of pharmaceutics·2023·
RPEP-075072023RETHINKTHC RESEARCH DATABASErethinkthc.com/research

Quick Facts

Study Type
Not classified
Evidence
Not graded
Sample
Not reported

What This Study Found

Key Numbers

How They Did This

Why This Research Matters

What This Study Doesn't Tell Us

Trust & Context

Original Title:
Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract.
Published In:
International journal of pharmaceutics, 634, 122643 (2023)
Database ID:
RPEP-07507

Evidence Hierarchy

Meta-Analysis / Systematic Review
Randomized Controlled Trial
Cohort / Case-Control
Cross-Sectional / ObservationalSnapshot without intervening
This study
Case Report / Animal Study
What do these levels mean? →

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Cite This Study

RPEP-07507·https://rethinkpeptides.com/research/RPEP-07507

APA

Wang, Fanjin; Sangfuang, Nannapat; McCoubrey, Laura E; Yadav, Vipul; Elbadawi, Moe; Orlu, Mine; Gaisford, Simon; Basit, Abdul W. (2023). Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract.. International journal of pharmaceutics, 634, 122643. https://doi.org/10.1016/j.ijpharm.2023.122643

MLA

Wang, Fanjin, et al. "Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract.." International journal of pharmaceutics, 2023. https://doi.org/10.1016/j.ijpharm.2023.122643

RethinkPeptides

RethinkPeptides Research Database. "Advancing oral delivery of biologics: Machine learning predi..." RPEP-07507. Retrieved from https://rethinkpeptides.com/research/wang-2023-advancing-oral-delivery-of

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Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.

This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.